SteveOH

 

Robert L. Glass

 

This month’s column is simply a collection of what I consider to be facts—truths, if you will—about software engineering. I’m presenting this software engineering laundry list because far too many people who call themselves software engineers, or computer scientists, or programmers, or whatever nom du jour you prefer, either aren’t familiar with these facts or have forgotten them.

I don’t expect you to agree with all these facts; some of them might even upset you. Great! Then we can begin a dialog about which facts really are facts and which are merely figments of my vivid loyal opposition imagination! Enough preliminaries. Here are the most frequently forgotten fundamental facts about software engineering. Some are of vital importance—we forget them at considerable risk.

 

Complexity

C1. For every 10-percent increase in problem complexity, there is a 100-percent increase in the software solution�s complexity. That’s not a condition to try to change (even though reducing complexity is always desirable); that’s just the way it is. (For one explanation of why this is so, see RD2 in the section “Requirements and design.”)

 

People

P1. The most important factor in attacking complexity is not the tools and techniques that programmers use but rather the quality of the programmers themselves.

P2. Good programmers are up to 30 times better than mediocre programmers, according to “individual differences” research. Given that their pay is never commensurate, they are the biggest bargains in the software field.

 

Tools and techniques

T1. Most software tool and technique improvements account for about a 5- to 30-percent increase in productivity and quality. But at one time or another, most of these improvements have been claimed by someone to have “order of magnitude” (factor of 10) benefits. Hype is the plague on the house of software.

T2. Learning a new tool or technique actually lowers programmer productivity and product quality initially. You achieve the eventual benefit only after overcoming this learning curve.

T3. Therefore, adopting new tools and techniques is worthwhile, but only if you (a) realistically view their value and (b) use patience in measuring their benefits.

 

Quality

Q1. Quality is a collection of attributes. Various people define those attributes differently, but a commonly accepted collection is portability, reliability, efficiency, human engineering, testability, understandability, and modifiability.

Q2. Quality is not the same as satisfying users, meeting requirements, or meeting cost and schedule targets. However, all these things have an interesting relationship: User satisfaction = quality product + meets requirements + delivered when needed + appropriate cost.

Q3. Because quality is not simply reliability, it is about much more than software defects.

Q4. Trying to improve one quality attribute often degrades another. For example, attempts to improve efficiency often degrade modifiability.

 

Reliability

RE1. Error detection and removal accounts for roughly 40 percent of development costs. Thus it is the most important phase of the development life cycle.

RE2. There are certain kinds of software errors that most programmers make frequently. These include off-by-one indexing, definition or reference inconsistency, and omitting deep design details. That is why, for example, N-version programming, which attempts to create multiple diverse solutions through multiple programmers, can never completely achieve its promise.

RE3. Software that a typical programmer believes to be thoroughly tested has often had only about 55 to 60 percent of its logic paths executed. Automated support, such as coverage analyzers, can raise that to roughly 85 to 90 percent. Testing at the 100-percent level is nearly impossible.

RE4. Even if 100-percent test coverage (see RE3) were possible, that criteria would be insufficient for testing. Roughly 35 percent of software defects emerge from missing logic paths, and another 40 percent are from the execution of a unique combination of logic paths. They will not be caught by 100-percent coverage (100-percent coverage can, therefore, potentially detect only about 25 percent of the errors!).

RE5. There is no single best approach to software error removal. A combination of several approaches, such as inspections and several kinds of testing and fault tolerance, is necessary.

RE6. (corollary to RE5) Software will always contain residual defects, after even the most rigorous error removal. The goal is to minimize the number and especially the severity of those defects.

 

Efficiency

EF1. Efficiency is more often a matter of good design than of good coding. So, if a project requires efficiency, efficiency must be considered early in the life cycle.

EF2. High-order language (HOL) code, with appropriate compiler optimizations, can be made about 90 percent as efficient as the comparable assembler code. But that statement is highly task dependent; some tasks are much harder than others to code efficiently in HOL.

EF3. There are trade-offs between size and time optimization. Often, improving one degrades the other.

 

Maintenance

M1. Quality and maintenance have an interesting relationship (see Q3 and Q4).

M2. Maintenance typically consumes about 40 to 80 percent (60 percent average) of software costs. Therefore, it is probably the most important life cycle phase.

M3. Enhancement is responsible for roughly 60 percent of software maintenance costs. Error correction is roughly 17 percent. So, software maintenance is largely about adding new capability to old software, not about fixing it.

M4. The previous two facts constitute what you could call the “60/60″ rule of software.

M5. Most software development tasks and software maintenance tasks are the same—except for the additional maintenance task of “understanding the existing product.” This task is the dominant maintenance activity, consuming roughly 30 percent of maintenance time. So, you could claim that maintenance is more difficult than development.

 

Requirements and design

RD1. One of the two most common causes of runaway projects is unstable requirements. (For the other, see ES1.)

RD2. When a project moves from requirements to design, the solution process’s complexity causes an explosion of “derived requirements.” The list of requirements for the design phase is often 50 times longer than the list of original requirements.

RD3. This requirements explosion is partly why it is difficult to implement requirements traceability (tracing the original requirements through the artifacts of the succeeding lifecycle phases), even though everyone agrees this is desirable.

RD4. A software problem seldom has one best design solution. (Bill Curtis has said that in a room full of expert software designers, if any two agree, that’s a majority!) That’s why, for example, trying to provide reusable design solutions has so long resisted significant progress.

 

Reviews and inspections

RI1. Rigorous reviews commonly remove up to 90 percent of errors from a software product before the first test case is run. (Many research findings support this; of course, it’s extremely difficult to know when you’ve found 100 percent of a software product’s errors!)

RI2. Rigorous reviews are more effective, and more cost effective, than any other error-removal strategy, including testing. But they cannot and should not replace testing (see RE5).

RI3. Rigorous reviews are extremely challenging to do well, and most organizations do not do them, at least not for 100 percent of their software artifacts.

RI4. Post-delivery reviews are generally acknowledged to be important, both for determining customer satisfaction and for process improvement, but most organizations do not perform them. By the time such reviews should be held (three to 12 months after delivery), potential review participants have generally scattered to other projects.

 

Reuse

REU1. Reuse-in-the-small (libraries of subroutines) began nearly 50 years ago and is a well-solved problem.

REU2. Reuse-in-the-large (components) remains largely unsolved, even though everyone agrees it is important and desirable.

REU3. Disagreement exists about why reuse-in-the-large is unsolved, although most agree that it is a management, not technology, problem (will, not skill). (Others say that finding sufficiently common subproblems across programming tasks is difficult. This would make reuse-in-the-large a problem inherent in the nature of software and the problems it solves, and thus relatively unsolvable).

REU4. Reuse-in-the-large works best in families of related systems, and thus is domain dependent. This narrows its potential applicability.

REU5. Pattern reuse is one solution to the problems inherent in code reuse.

 

Estimation

ES1. One of the two most common causes of runaway projects is optimistic estimation. (For the other, see RD1.)

ES2. Most software estimates are performed at the beginning of the life cycle. This makes sense until we realize that this occurs before the requirements phase and thus before the problem is understood. Estimation therefore usually occurs at the wrong time.

ES3. Most software estimates are made, according to several researchers, by either upper management or marketing, not by the people who will build the software or by their managers. Therefore, the wrong people are doing estimation.

ES4. Software estimates are rarely adjusted as the project proceeds. So, those estimates done at the wrong time by the wrong people are usually not corrected.

ES5. Because estimates are so faulty, there is little reason to be concerned when software projects do not meet cost or schedule targets. But everyone is concerned anyway!

ES6. In one study of a project that failed to meet its estimates, the management saw the project as a failure, but the technical participants saw it as the most successful project they had ever worked on! This illustrates the disconnect regarding the role of estimation, and project success, between management and technologists. Given the previous facts, that is hardly surprising.

ES7. Pressure to achieve estimation targets is common and tends to cause programmers to skip good software process. This constitutes an absurd result done for an absurd reason.

 

Research

RES1. Many software researchers advocate rather than investigate. As a result, (a) some advocated concepts are worth less than their advocates believe and (b) there is a shortage of evaluative research to help determine the actual value of new tools and techniques.

There, that’s my two cents’ worth of software engineering fundamental facts. What are yours? I expect, if we can get a dialog going here, that there are a lot of similar facts that I have forgotten—or am not aware of. I’m especially eager to hear what additional facts you can contribute.

And, of course, I realize that some will disagree (perhaps even violently!) with some of the facts I’ve presented. I want to hear about that as well.

Robert L. Glass is the editor of Elsevier’s Journal of Systems and Software and the publisher and editor of The Software Practitioner newsletter. Contact him at rglass@indiana (dot) education; he’d be pleased to hear from you.

Reprinted from IEEE Software, vol. 18, no. 3, 2001, pp. 112, 110–111.

Courtesy of Cain’s Brain.

1. Any PERMISSION DENIED has an OVERRIDE function.

2. Complex calculations and loading of huge amounts of data will be accomplished in under three seconds. In the movies, modems transmit data at two gigabytes per second.

3. When the power plant/missile site/whatever overheats, all the control panels will explode, as will the entire building.

4. If you display a file on the screen and someone deletes the file, it also disappears from the screen. There are no ways to copy a backup file — and there are no undelete utilities.

Corollary: Deleting a file instantly removes all copies of said file from disks, memory, frame buffers and caches across all computers in the universe.

5. If a disk has got encrypted files, you are automatically asked for a password when you try to access it.

6. No matter what kind of computer disk it is, it’ll be readable by any system you put it into. All application software is usable by all computer platforms.

7. The more high-tech the equipment, the more buttons it has. However, everyone must have been highly trained, because the buttons aren’t labeled.

8. Most computers, no matter how small, have reality-defying three-dimensional, real-time, photo-realistic animated graphics capability.

9. Laptops, for some strange reason, always seem to have amazing real-time video phone capabilities and the performance of a CRAY.

10. Whenever a character looks at a terminal, the image is so bright that it projects itself onto his/her face.

11. Computers never crash during key, high-intensity activities. Humans operating computers never make mistakes under stress.

12. (From Independence Day) No matter what kind of virus it is, any computer can be infected with it — even an alien spaceship’s computer — simply by running a virus upload program on a laptop.

13. (From Jurassic Park) A custom system with millions of lines of code controlling a multimillion dollar theme park can be operated by a 13 year old who has seen a Unix system before. Seeing an operating system means you know how to run any application on that system, even custom apps.

Note: What OS was it really running?

(1) “These are super computers”.  A CrayOS?

(2) “Quicktime movie, Apple logo, trash can.”  MacOS?

(3) “Reboot. System ready. C:\”  DOS?

(4) “Hey, this is Unix.  I know this”  Unix?

The computers in Jurassic Park were Cray supercomputers running the MacOS as a graphical shell of DOS all layered on top of a Unix base.

14. You cannot stop a destructive program or virus by unplugging the computer. Presumably the virus has it’s own built-in power supply.

15. You cannot stop a destructive program downloading onto your system by unplugging the phone line. You must figure out the mandatory “back door” all evil virus programmers put in.

16. Computers only crash if a virus or a hacker is involved.

17. All text must be at least 72 point.

18. Word processors do not have an insert point.

19. The only way to reboot is to shut off the main power to the building.

20. Passwords can be guessed in three and exactly three tries.  If you cannot guess the password in three tries, you must give up immediately.

21. Any task or program can be executed by simply pressing Enter, no matter which program or window is in the foreground.

22. All scanners, video cameras and digital cameras have a resolution of approximately 500 megapixels.  Any image can be infinitely magnified with no pixelization.

23. Security will not improve over time.  Nonaffialiated personnel can take over a space ship without needing an account or access control.  Corollary: Anyone can override access control lists in the future.

24. All hackers wear black T-shirts or Hawaiian shirts.

25. Incoming messages are displayed letter by letter.  Email over the Internet works like telegraphs.

26. Microsoft Windows doesn’t exist.  Macintosh has a 75% market share.

27. GUI operations, such as image selection and manipulation, can be handled easily and quickly via the keyboard.

28. If a robot’s eyes turn red, it becomes evil.

29. Cell phones and laptops have infinite battery life, until you need to call for help.

30. Latency does not exist.  Voice and data can be sent to Mars in real time.

The guide below was not written by me, but by Ronald T. Azuma, PhD.  All credits go to the author and/or other stated sources.  I just wanted to share this wonderful resource with anyone that may be interested.

****

Brief description

A computer science graduate school survival guide, intended for prospective or novice graduate students. This guide describes what I wish I had known at the start of graduate school but had to learn the hard way instead. It focuses on mental toughness and the skills a graduate student needs. The guide also discusses finding a job after completing the Ph.D. and points to many other related web pages.

---

“So long, and thanks for the Ph.D.!”

a.k.a.

“Everything I wanted to know about C.S. graduate school
at the beginning but didn’t learn until later.”

The 4th guide in the Hitchhiker’s guide trilogy
(and if that doesn’t make sense, you obviously have not read Douglas Adams)

by Ronald T. Azuma

v. 1.08

Original version 1997, last revised January 2003

---

Introduction

• The Feynman Problem Solving Algorithm:
  1) Write down the problem.
  2) Think very hard.
  3) Write down the solution.

“To know the road ahead, ask those coming back.”
- Chinese proverbIn February 1995, on a beautiful sunny day with clear Carolina blue skies, I turned in the final, signed copy of my dissertation. The graduate school staff member did some last-minute checks on the document and pronounced it acceptable. After six and a half years of toil and sweat, I was finally done! While walking back to the C.S. department building, I was sorely disappointed that the heavens didn’t part, with trumpet-playing angels descending to announce this monumental occasion. Upon hearing this observation, Dr. Fred Brooks (one of my committee members) commented, “And the sad fact is, you’re no smarter today than you were yesterday.” “That’s true,” I replied, “but the important thing is that I am smarter than I was six and a half years ago.”

That day was over two years ago, and since then I have had plenty of time to reflect upon my graduate student career. One thought that has repeatedly struck me is how much easier graduate school might have been if somehow, magically, some of the things I knew when I turned in my dissertation I could have known when I first entered graduate school. Instead, I had to learn those the hard way. Of course, for many topics this is impossible: the point of graduate school is to learn those by going through the experience. However, I believe other lessons can be taught ahead of time. Unfortunately, such guidance is rarely offered. While I had to learn everything the hard way, new graduate students might benefit from my experiences and what I learned. That is the purpose of this guide.

Very little of this guide discusses technical matters. Technical skills, intelligence and creativity are certainly strong factors for success in graduate school. For example, I doubt there is a C.S. graduate student who didn’t at one point wish he or she had a stronger mathematical background. However, it’s beyond the scope of this guide to tell you how to be technically brilliant, as the following joke implies:

You don’t have to be a genius to do well in graduate school. You must be reasonably intelligent, but after a certain point, I think other traits become more important in determining success.This guide covers the character traits and social skills that often separate the “star” graduate students from the ordinary ones. Who are the students who are self-motivated, take initiative, find ways around obstacles, communicate well both orally and in writing, and get along well enough with their committee and other department members to marshal resources to their cause? Which students seem to know “how the system works” and manage to get things done? These traits are hardly unique to succeeding in graduate school; they are the same ones vital to success in academic or industrial careers, which is probably why many of the best graduate students that I knew were ones who had spent some time working before they came back to school.

This document is aimed at junior C.S. graduate students, but these observations are probably broad enough to apply to graduate education in other technical fields. My conclusions are certainly colored by my particular experiences (doing my dissertation work in interactive computer graphics in the Computer Science department of the University of North Carolina at Chapel Hill) but I think they are fairly general in application and should be of interest to readers at other schools and other C.S. specialties. Obviously, these are only my opinions and may not represent the views of other sane individuals or organizations. Some points may be controversial, but if they weren’t this would not be interesting reading. Parts of this document come from two informal talks I gave at UNC about “the Ph.D. job hunt” and “observations from spending one year in industrial research.” Both talks had larger audiences than any informal technical talk I gave at UNC, which told me that students are definitely interested in these subjects!

Why get a Ph.D.?

“Being a graduate student is like becoming all of the Seven Dwarves. In the beginning you’re Dopey and Bashful. In the middle, you are usually sick (Sneezy), tired (Sleepy), and irritable (Grumpy). But at the end, they call you Doc, and then you’re Happy.”
- yours trulyThe most basic question every Ph.D. student must know the answer to is: “Why the hell am I doing this?”

It’s a good question. The hours are long. The pay is low, with minimal benefits. After graduation, Ph.D. salaries are higher than B.S. and M.S. salaries, but the difference doesn’t make up for the income lost by staying in school longer. The M.S. has a better “bucks for the time invested” ratio than the Ph.D. does. And in terms of social status, a graduate student doesn’t rank very high on the ladder.

If you do not have an acceptable answer to this question, then don’t get a Ph.D. I repeat: if you do not have a rock-solid reason for getting the Ph.D., then it is better that you leave with a Master’s.

Why? Completing a Ph.D. is a long, hard road with many potholes and washed out bridges along the way. You may run over some land mines and have to stop and turn around and explore other routes. If the goal is important enough to you, then these obstacles will not prevent you from completing your journey. But if you don’t know why you are on this road, then you will get discouraged and will probably leave without finishing, having wasted years of your life.

I faced this situation after the first time I took the Doctoral Written Exam (which at the time was the entrance examination into the Ph.D. program). I missed passing it by just 4 percentage points. I then had to decide whether or not to try again next semester (committing myself again to spending weeks getting ready for the test) or just leave with an M.S. degree.

I didn’t come to graduate school with the Ph.D. as the primary goal. So this test result forced me to answer the basic question “Why the hell am I doing this?” After much soul searching, I found my answer and decided to take the test again, passed it, and went on to get my Ph.D.

I got the Ph.D. because I wanted to get a research position after leaving graduate school. I wanted to work with the state of the art and extend it. I did not want to “bring yesterday’s technology one step closer to tomorrow.” I wanted a job that would I find interesting, challenging and stimulating. While an M.S. would give me a chance at landing a research position, the Ph.D. would give me a much better chance. And I did not want to live with regrets. If I took the Doctoral Written Exam again and failed again, then I could say that it wasn’t meant to be and move on with my life. I would have no regrets because I had given it my best shot and was not able to make it. However, if I left with an M.S. without taking the test a second time, and five years later I was in a job that was boring and uninteresting, then I would have to lie awake every night for the rest of my life wondering “What if?” What if I had taken the test again and passed? Would I then be in the job that I really wanted? That was not a situation I wanted to be in. I did not want to live the rest of my life regretting what might have been.

In hindsight, I think one of the main reasons I successfully completed the Ph.D. was the fact that I didn’t pass the exam on the first try. It’s ironic, but life sometimes works in strange ways. That initial failure caused me to answer the basic question, providing the mental fortitude to keep going despite the hurdles and problems I would later face.

My answer is you should get a Ph.D. if it is required for your goals after graduate school, such as becoming a professor or a researcher in academia, government or industry. Your answer may differ from mine. As long as you have an answer that you believe in passionately, then that’s enough. If you don’t have an answer, then save yourself a lot of grief and don’t get the Ph.D.

Academia is a business

“Remember the Golden Rule: Those who have the gold make the rules.”Academia is a business, and “graduate student” is a job title. This is especially true at private universities. Academia is very peculiar type of business. It is certainly not the Real World and does not work in the same way that the ordinary corporate world does. However, it is a business nonetheless and as a graduate student, you must treat it that way. Graduate school made a lot more sense and became much easier for me after I realized this. If you think of graduate school as an “Ivory Tower” free of politics, money problems, and real-world concerns, you are going to be severely disappointed. If you don’t believe me, read The Idea Factory by Pepper White (listed in the references) for one account of graduate life at MIT.

A few graduate students are independently wealthy or have fellowship and scholarship money that cover all their expenses for their total stay in graduate school. Such students are rare, however. Most of us needed financial support, in the form of Teaching Assistantships or Research Assistantships (RA’s). In general, RA’s are more desirable to students since those can directly fund the research you need to finish.

Where does the money come from to fund RA’s? Your professors have to raise funds from external organizations. These include government agencies such as the National Science Foundation (NSF), Defense Advanced Research Projects Agency (DARPA), the Office of Naval Research (ONR), and others. Private companies also fund some university research, although this tends to be less common, in smaller amounts, and in the form of equipment donations. These organizations don’t just give money away as charity. They expect their money to accomplish something. Increasingly these days, this takes the form of a contract for a working demonstration that must be shown at the end. That means once the money is delivered, your professors must come through with the working demonstration. It is rare that they do this by themselves. Instead, they find some very capable, young, self-motivated people who are willing to work long hours for small amounts of pay. In other words, they fund RA’s.

The RA job is crucial to the academic business. If the RA’s cannot successfully conduct the research, then the demonstration will not work in the end and the funding agencies may not be happy. They may choose not to fund your professor in the future, which will bring his or her research program to a halt. And there are many professors and other researchers chasing too few research dollars these days; it is a competitive market. Thus, each professor wants the best students available. These students are the most capable ones who can get the research done required to fulfill the funding contracts.

That means you must treat an RA like a job. You must prove to your professors that you are capable of getting the work done, being a team player, communicating your results, and most of the other characteristics needed to do well in regular jobs. That’s why many of the upcoming sections in this guide sound like ones written for the regular workplace.

What do you get out of this? At the start, you may have to do tasks specifically related to the funding contracts. But eventually your professor must be flexible enough to fund your own specific research program that leads to the completion of your dissertation. Your stipend and tuition waiver should be enough to live on frugally without going into debt. You will learn the state of the art in your chosen speciality and conduct cutting-edge research on a subject that you find interesting and enjoyable. If you don’t find this compensation sufficient, then you shouldn’t be in graduate school in the first place.

The bottom line: realize that academia is a peculiar kind of business and the role you play in this enterprise. If you do your job well (and have good negotiation and interpersonal skills, as discussed in future sections), both your needs and your professors’ needs will be met. But don’t enter an RA position thinking that the computers, research equipment, staff members and other resources that you are provided with are your birthright. Don’t take them for granted! Most of those exist only because your professors have been able to raise the money to provide those to you. In turn, you must fulfill your end of the deal by doing great research with those resources. If you don’t do your job well, don’t be surprised if your professors choose not to fund you in the future. They do not have to provide you with an RA job or let you use the computing equipment they acquired. And the student who has no funding, no tuition reimbursement and no access to required computing resources is the student who leaves the university that semester.

How do you make sure you are one of those best, highly desired RA’s? Read on!

Graduate school is a different ballgame

“Don’t let school get in the way of your education.”
- Mark Twain“The IQ test was invented to predict academic performance, nothing else. If we wanted something that would predict life success, we’d have to invent another test completely.”
- Robert Zajonc

If you go through a Ph.D. program, you will find graduate school a very different world from undergraduate school. If you just get an M.S., then graduate school may not be much different from undergrad (depending on where you get your degree), except that the courses are deeper and more advanced. But for a Ph.D. student, graduate school is a whole new ballgame. The students who do well are the ones who learn this earlier rather than later and make the necessary adjustments.

Graduate school is not primarily about taking courses. You will take classes in the beginning but in your later years you probably won’t have any classes. People judge a recently graduated Ph.D. by his or her research, not by his or her class grades. And, without any offense to my professors, most of what you learn in a Ph.D. program comes outside of classes: from doing research on your own, attending conferences, and talking to your fellow students. Success in graduate school does not come from completing a set number of course units but rather from successfully completing a research program.

Graduate school is more like an apprenticeship where each student has his or her own project, and the masters may or may not be particularly helpful. It’s like teaching swimming by tossing students into the deep end of the pool and seeing who makes it to the other end alive and who drowns. It’s like training clock designers by locking students inside a clock factory with some working clocks and lots of clock parts and machines for building clocks. However, the instructions are at best incomplete and even the masters themselves don’t know exactly how to build next year’s models.

Excelling in a Ph.D. program requires different skills than doing well in undergrad. Undergraduate education tests you through class projects (that do not last more than a semester), essays, midterms and finals. For the most part, you work alone. Your professor may not know your name. Every other student in your class takes the same tests or does similar projects. But in a Ph.D. program, you must select and complete a unique long-term research program. For most of us, this means you have to learn how to do research and all that entails: working closely with your professors, staff and fellow students, communicating results, finding your way around obstacles, dealing with politics, etc.

I’m not saying that tests and grades are completely unimportant in graduate school. One of the two biggest hurdles in completing a Ph.D. is passing the qualifying exam. (The other is finding an acceptable dissertation topic.) But because graduate school is not nearly as exam-based as undergraduate education and requires different skills, the GRE and undergraduate grades are not as good an indicator of who will excel and who will drop out as admission committees seem to think. Those tests do not measure creativity, tenacity, interpersonal skills, oral presentation skills, and many other important traits.

The next several sections discuss these traits.

Initiative

“The difference between people who exercise initiative and those who don’t is literally the difference between night and day. I’m not talking about a 25 to 50 percent difference in effectiveness; I’m talking about a 5000-plus percent difference, particularly if they are smart, aware, and sensitive to others.”
- Stephen R. Covey, The 7 Habits of Highly Effective PeopleThe dissertation represents a focused, personal research effort where you take the lead on your own, unique project. If you expect that your adviser is going to hold your hands and tell you what to do every step of the way, you are missing the point of the dissertation. Ph.D. students must show initiative to successfully complete the dissertation. This does not mean that guidance from professors is unimportant, just that this guidance should be at a reasonably high level, not at a micromanaging level. If you never do any tasks except those that your professor specifically tells you to do, then you need to work on initiative.

At UNC, there is a famous anecdote about a former UNC graduate student named Joe Capowski. Many years ago, UNC got a force-feedback mechanical arm to use with molecular visualization and docking experiments. The problem was how to move it to UNC. This mechanical arm is a large, heavy beast, and it was in Argonne National Labs in Chicago, IL. Unfortunately, there was a trucker’s strike going on at the time. Joe Capowski, on his own initiative (and without telling anyone), flew out to Argonne, rented a car, drove the mechanical arm all the way back to North Carolina, and then handed the computer science department the bill! Many years later, Joe Capowski ran for the Chapel Hill city council and won a seat. Prof. Fred Brooks gave him an endorsement. I still remember the words Dr. Brooks said: “I may not agree with his politics, but I know he’ll get things done.”

While the Joe Capowski anecdote is perhaps a bit extreme, it does show that it is often better to ask forgiveness than permission, provided you are not becoming a “loose cannon.” Certain universities (e.g. MIT) are good at fostering a “can do” attitude among their graduate students, and therefore they become more assertive and productive. One of the hallmarks of a senior graduate student is that he or she knows the types of tasks that require permission and those that don’t. That knowledge will come with experience. Generally, it’s the senior graduate students who have the most freedom to take initiative on projects. This privilege has to be earned. The more that you have proven that you can work independently and initiate and complete appropriate tasks, the more your professors will leave you alone to do what you want to do.

Tenacity

“Let me tell you the secret that has led me to my goal. My strength lies solely in my tenacity.”
- Louis PasteurYou don’t need to be a genius to earn a Ph.D. (although it doesn’t hurt). But nobody finishes a dissertation without being tenacious. A dissertation usually takes a few years to complete. This can be a culture shock to former undergraduates who have never worked on a project that lasted longer than one quarter or semester (at the end of which, whatever the state of the project, one declares victory and then goes home). No one can tell you in advance exactly how long the dissertation will take, so it’s hard to see where the “end of the road” lies. You will encounter unexpected problems and obstacles that can add months or years to the project. It’s very easy to become depressed and unmotivated about going on. If you are not tenacious about working on the dissertation, you won’t finish.

Tenacity means sticking with things even when you get depressed or when things aren’t going well. For example, I did not enjoy my first year of graduate school. I didn’t tell anyone this until after leaving UNC. I was not on a project and was focused on taking classes, some of which I didn’t do all that well in. I didn’t feel a part of the Department, and really wondered whether or not I fit in. Still, I stuck with it and when summer rolled around and I got a job in the Department, I became much more involved in research and enjoyed graduate school much more. Part of earning a Ph.D. is building a “thick skin” so you are not so fragile that you will give up at the first sign on any difficulties.

One lesson I learned as a graduate student is the best way to finish the dissertation is to do something every day that gets you closer to being done. If all you have left is writing, then write part of the dissertation every day. If you still have research to do, then do part of it every day. Don’t just do it when you are “in the mood” or feeling productive. This level of discipline will keep you going through the good times and the bad and will ensure that you finish.

Flexibility

“Back in graduate school, I’d learned how to survive without funding, power, or even office space. Grad students are lowest in the academic hierarchy, and so they have to squeeze resources from between the cracks. When you’re last on the list for telescope time, you make your observations by hanging around the mountaintop, waiting for a slice of time between other observers. When you need an electronic gizmo in the lab, you borrow it in the evening, use it all night, and return it before anyone notices. I didn’t learn much about planetary physics, but weaseling came naturally.”
- Clifford Stoll, The Cuckoo’s Egg“The Chinese call luck opportunity and they say it knocks every day on your door. Some people hear it; some do not. It’s not enough to hear opportunity knock. You must let him in, greet him, make friends and work together.”
- Bernard Gittelson

Flexibility means taking advantage of opportunities and synergies, working around problems, and being willing to change plans as required. As a graduate student, you are on the bottom of the academic totem pole. Even undergraduates can rank higher, especially at private universities (because they actually pay tuition!) You cannot order anybody to do anything. In general, you will be in the position of reacting to big events rather than controlling them. Therefore, you must be flexible in your approach and research program.

For example, you may not have as much access to a piece of laboratory equipment as you would like, or maybe access is suddenly cut off due to events beyond your control. What do you do? Can you find a replacement? Or reduce the time needed on that equipment? Or come in at odd hours when no normal person uses that equipment? Or redefine the direction of your project so that equipment is no longer required?

Events can be good as well as bad. The difference between the highly effective graduate student and the average one is that the former recognizes those opportunities and takes advantage of them. I had nothing to do with bringing Gary Bishop to UNC. But after he arrived I realized my research would progress much faster if he became my adviser so I made the switch and that was a big help to my graduate student career. Opportunities for synergy and serendipity do occur, but one has to be flexible enough to recognize them and take advantage of them.

Interpersonal skills

• I first learned of the capricious, human side of organizations some 15 years ago while studying the careers of engineers and scientists. The research design required that I spend eight hours a day in one-on-one interviews. For two hours I’d ask “career” questions of an engineer, chemist, physicist, or applied mathematician — all of whom worked for a Fortune 500 firm. During these 120 minutes, the subjects talked about the perils of the organizations. Two hours was scarcely enough time to share their stories. All energetically discussed their personal careers. Most had been frustrated with the “soft and gushy” side of organizations. Some had figured out the system and learned to master it. Others had not.As part of the research design, we asked to talk to low, medium, and high performers. This in itself was an interesting exercise. To determine performance rankings, we would place in front of a senior manager the names of the 10-50 people within his or her organization. Each name would be typed neatly in the middle of a three-by-five card. After asking the manager to rank the employees from top to bottom, the managers would then go through a card sort. Typically the executive would sort the names into three or four piles and then resort each pile again. Whatever the strategy, the exercise usually took only minutes. Just like that, the individual in charge of the professionals in question was able to rank, from top to bottom, as many as 50 people. It rarely took more than three minutes and a couple of head scratches and grunts. Three minutes. Although politics may appear ambiguous to those on the receiving end, those at the top were able to judge performance with crystal clarity.

  This performance ranking (conducted by individuals not involved in the interviews) was then used as a dependent measure. Those of us conducting the interviews attempted to surface information (independent measures) that would predict the ranking. What about a scientist’s career would lead to a top ranking? What trashed a perfectly good career? Surely scientific prowess would have an impact. And it did.

  But technological prowess wasn’t as predictive as another factor. We discovered that we could tell what performance group the interviewees belonged to within a minute or two by their attitudes toward people and politics. Individuals who were ranked low by their managers spoke of organizational politics as if it were poison. They were exceptionally annoyed by the people side of the business. They frequently stated they would rather be left alone to conduct their research untrammeled by human emotions. They characterized the social side of organizations as “soft and gushy.” They sounded like Spock turned bitter.

  Top performers, in contrast, found a way to work within the political system. They hadn’t exactly embraced politics. They didn’t appear like that toothy kid you knew back in college who lived to fight political battles. They didn’t come off as glad-handling sales folks. These were professional scientists who were often top ranked in their field. They looked and talked liked scientists. The difference between them and those ranked at the bottom of the totem pole was clear. They had found a way to make peace with organizations, people, and politics. They climbed to the top of their field by mastering both hard things and soft and gushy people.

  Engineers and scientists aren’t the only ones who find the human side of the organizations to be annoying. As we expanded our research to include professors, accountants, and other professionals, the findings were remarkably similar. All found political machinations to be distasteful. It’s just that some had found a way to master the social aspects — the top performers.

“For humans, honesty is a matter of degree. Engineers are always honest in matters of technology and human relationships. That’s why it’s a good idea to keep engineers away from customers, romantic interests, and other people who can’t handle the truth.”
- Scott Adams, The Dilbert Principle“I can calculate the motions of the heavenly bodies, but not the madness of people.”
- Isaac Newton

Computer Science majors are not, in general, known for their interpersonal skills. Some of us got into this field because it is easier to understand machines than people. As frustrating as computers can be, they at least behave in a logical manner, while human beings often do not. However, your success in graduate school and beyond depends a great deal upon your ability to build and maintain interpersonal relationships with your adviser, your committee, your research and support staff and your fellow students. This does not mean you must become the “life of the party.” I am not and never will be a gregarious, extroverted person. But I did make a serious effort to learn and practice interpersonal skills, and those were crucial to my graduate student career and my current industrial research position.

Why should this matter, you may ask? If one is technically brilliant, shouldn’t that be all that counts? The answer is no, because the situation is different from your undergraduate days. In both graduate school and in business, you must depend upon and work with other people to achieve your goals To put this in perspective, I have excerpted the following from an article called “Organizations: The Soft and Gushy Side” by Kerry J. Patterson, published in Fall 1991 issue of The Bent:

Students usually look down on politics, but politics in its most basic, positive form is simply the art of getting things done. Politics is mostly about who is allowed to do what and who gets the resources (money, people, equipment, etc.) To succeed in your research, you will need resources, both capital and personnel. Interpersonal skills are mandatory for acquiring those resources. If you are incapable of working with certain people or make them mad at you, you will not get those resources and will not complete your research.For example, which group of people did I try my best to avoid offending? Was it my committee? No, because healthy disagreements and negotiations with your adviser and committee are crucial to graduating within a reasonable amount of time. Nor was it my fellow students, because I did not need help from most of them, and most of them did not need me. The critical group was the research and support staff. These include the research faculty and all the various support positions (the system administrators, network administrators, audio-visual experts, electronic services, optical and mechanical engineers, and especially the secretaries). I needed their help to get my research done, but they did not directly need me. Consequently, I made it a priority to establish and maintain good working relationships with them.

Cultivating interpersonal relationships is mostly about treating people with respect and determining their different working styles. Give credit where credit is due. Acknowledge and thank them for their help. Return favors. Respect their expertise, advice and time. Apologize if you are at fault. Realize that different people work in different ways and are motivated by different things — the more you understand this diversity, the better you will be able to interact and motivate them to help you. For certain people, offering to buy them dinner or giving them free basketball tickets can work wonders.

A true example: at one point in my research, I needed to make significant modifications to some low-level code in the graphics computer called “Pixel Planes 5.” Doing this required expertise that I did not have, but another graduate student named Marc Olano did. How should I tap into Marc’s expertise and get my necessary changes done?

The wrong way is to go up to Marc, explain the problem, and get him to make the changes. Marc doesn’t need the changes done; I do. Therefore, I should do most of the work. Expecting him to do the work shows disrespect of his time.

What I actually did was to explain the problem to Marc and he sketched out a possible solution. Then I ran off and worked on my own for a few days, trying to implement the solution. I got part of it working, but ended up getting stuck on another part. Only at that point did I go back to Marc and ask him for help. By doing this, I showed that I respected his time and wanted to minimize his burden, thus making him more willing to help me. Months later, when he and Jon Cohen needed my help in setting up a system to demonstrate some of their software, I was more than happy to return the favor.

Interpersonal interaction is a huge subject and goes far beyond my description here. All I can really do in this section is (hopefully) convince you that these skills are vital to your graduate student career and encourage you to learn more if you need to improve these skills. I still have a lot to learn myself. I recommend reading The 7 Habits of Highly Effective People and Type Talk (both listed in the References section) as starting points.  The magazine article “How to be a star engineer” (listed in the References) also touches on this subject.

Organizational skills

“Failing to plan is planning to fail.”Since academia is a type of business, you will have responsibilities that you must uphold. You will be asked to greet and talk with visitors, give demos, show up to meetings, get projects done on time, etc. If you are not well organized, you will have a difficult time meeting those obligations. A technically brilliant student will be greatly hampered if he or she exhibits an “absent minded” personality and develops a reputation for being disorganized.

There are many different time management and organization skills, and you can find many books on those at your local bookstore. This guide is not going to describe them. Find one that works for you and use it. I can highly recommend Stephen Covey’s book, listed in the references. But whatever system you pick, just make sure it works for you. I have never found anyone else who uses my filing scheme, but it is effective for me (by minimizing the combined time of putting away and locating a piece of information). All that really matters is whether or not it works.

One metaphor I found useful is the following: Organize your tasks as if you were juggling them. Juggling several balls requires planning and skill. You must grab and toss each ball before it hits the ground. You can only toss one ball at a time, just as you can only work on one task at a time. The order in which you toss the balls is crucial, much as the order of working on tasks often determines whether or not you meet all your deadlines. Finally, once you start a task (grab a ball) you want to get enough done so you can ignore it for a while (throw it high enough in the air so it won’t come down for a while). Otherwise you waste too much time in context switches between tasks. Do you see jugglers try to keep each ball at the same height above the ground, frantically touching every ball every second?

Randy Pausch (a professor at CMU) has a set of notes on time management. Three words in his guide summarize the most vital step: Kill your television. He asks you to keep your priorities straight. What is the most important thing to a Ph.D. student? It should be finishing the dissertation, not watching every episode of Friends. That doesn’t mean dropping everything else in life, but it does mean knowing what takes priority and allocating time accordingly.

Communications skills

“What is written without effort is, in general, read without pleasure.”
- Samuel Johnson“Present to inform, not to impress; if you inform, you will impress. “
- Fred Brooks

I am always amazed that articles written about businesses consistently put good communication skills at or near the top of list of skills that employers want to see in people but rarely find. But you know what? It’s true!

Communication skills, both written and oral, are vital for making a good impression as a Ph.D. student and as a researcher. At a minimum, you have to defend your dissertation with an oral presentation. But you should also expect to write technical papers and reports, give presentations at conferences, and give demonstrations to groups of visitors. If you can write and speak well, you will earn recognition and distinguish yourself from the other graduate students. This is especially true when giving presentations in front of important visitors or at major conferences.

Conversely, if you cannot communicate well, then your career options after graduation will be limited.  Professors spend most of their time communicating: teaching, fundraising, guiding graduate students, and documenting their results (through papers, videos, viewgraphs, etc.)  In industry, we need people who can communicate well so they can work in teams, learn what businesses and customers need, present their results, raise funds, and transition to leadership roles in projects and personnel management.  If you are technically brilliant but are incapable of communicating, then your results will be limited to what you can accomplish alone and your career growth will be limited, both in industry and academia.

Unfortunately, not all graduate students receive training in giving presentations or writing technical documents (which are different from English essays). These are skills that can be learned! Don’t worry if giving presentations and writing papers are not something that comes naturally to you. I was not very comfortable giving oral presentations when I started graduate school, so I made a concerted effort to learn how to do so, by taking classes, reading about the subject, and practicing. It’s not easy, but it’s well worth the investment. If you need practice, try giving informal talks at research luncheons, joining Toastmasters, and studying good speakers to see what they do.

Covering everything about this subject would fill a guide by itself (check out the SIGGRAPH page on preparing and giving presentations), and would probably better done through a videotape than a written document. But here are a few basic points:

• Organization counts. Within the first few paragraphs or first few minutes, tell me why I should read your paper or listen to your talk. Make it clear where we are going and what we have already covered.
• Make the text in your slides large enough so that people sitting in the back can read them. For large presentation halls, this usually means no more than 6-7 lines per slide and 28 point type minimum. You’d be surprised how many experts on visualization (especially tenured professors!) give presentations with unreadable slides.
• Variety retains interest. Vary your pace, tone, and volume. Emphasize the important points. Look around the room. Throw in some video, pictures, or live examples.
• Don’t stand in front of the screen and block everyone’s view. You’d be surprised how often people do this without realizing it.
• Point out the limitations of your work. That helps your credibility. Similarly, give credit where credit is due.
• Make friends with the A/V crew! Running A/V is a thankless, negative reinforcement job. If everything runs smoothly, well, that’s what was supposed to happen so nobody says anything. But if anything goes wrong, the entire audience looks back at the control room. Help the A/V people help you. Always check in early and test the equipment. Tell them what you are going to do in your presentation (e.g. I’m running 3 video segments). Make sure you know how everything works long before you come up to the podium. And thank the A/V crew for their help after you are done!

Confidence is the key to giving a good presentation. And the way to gain confidence is to give good presentations. When you’re just starting out, this is a Catch-22. However, once you become good enough, this turns into a positive feedback cycle that can make giving talks a pleasure.

Writing papers and getting them published is vital for Ph.D. students who want to get jobs in research after graduation. Your ability to write well significantly improves the chances that your paper will be accepted. When I was a young graduate student and read a paper that I didn’t understand, I thought “Gee, I must be dumb.” Today I will read the same paper and think “Boy, this is a lousy paper. The authors did not do a good job explaining and presenting their work.” If I am reviewing that paper, such a reaction is enough for me to reject the paper.

Where do you submit your papers? Your professors will help you with this choice, but in general I would suggest shooting for the best conferences or journals where you think it has a reasonable chance of being accepted. It’s not much more work to write, submit and present a paper in a highly respected venue than in less respected venues. And if you don’t shoot for the top you’ll never know if it would have made it. The field of computer graphics is a bit unusual in that the most desirable place to publish is a conference (SIGGRAPH), rather than a journal. Be aware that journals can take years to publish submitted papers; the turn-around time is much faster in a conference.

Finally, don’t forget to communicate with your professors and your teammates. Keep your committee appraised of your progress. One thing I do (which few others do) is write short (1 screenfull) status reports, which I religiously e-mailed to my professors and team members on a weekly basis. These serve as an efficient way of keeping everyone up to date on what I’m doing. They are also a good way for me to record my progress. If I need to remember what I got done during a six month period, I have plenty of old status reports that I can read. You’d be amazed how appreciative professors and managers are of this simple practice. I also throw in a different humorous quote at the end of each week’s report to reward people for reading it.

When you are working in the lab and you reach a milestone or achieve a result, let people know about it! Bring in your professors and fellow students and show it off! That’s a win-win situation. It lets others know that you are making progress and achieving results, and you get valuable feedback and advice.

Choosing an adviser and a committee

“Some students in the lab are only nominally supervised by a thesis advisor. This can work out well for people who are independent self-starters. It has the advantage that you have only your own neuroses to deal with, not your advisor’s as well.”
- from “How to do research at the MIT AI Lab”The choice of an appropriate adviser is crucial to successfully completing the Ph.D. Your adviser must be someone who can cover your area of specialization and someone you can get along with. When I started graduate school, I thought the adviser – student relationship was supposed to be very close, both professionally and socially. In reality, the relationship is whatever the professor and the student choose to make of it. It can be close, with invited dinners at the professor’s home, or it can be distant, e.g. meeting once per semester just to remind the professor that the student is still alive.

One basic question in choosing an adviser is whether to pick a junior (non-tenured) or a senior (tenured) professor. Non-tenured professors tend to travel less and are generally more available. It is difficult to get help from an adviser who is never in town. Non-tenured faculty have fewer advisees that you have to compete with to get time with the professor. They are more likely to be personally involved with your research — writing code, spending time in the lab at midnight, etc. Non-tenured faculty must be energetic and hard working if they want to be awarded tenure, and this work habit can rub off on their students. However, tenured faculty have several advantages as well. They are usually the ones with most of the money and resources to support you. They do not have to compete with their students for publications and recognition. The advisee does not run the risk of having his or her adviser not getting tenure and leaving the university. Tenured faculty are more experienced with “how the game works” and thus may be better sources of guidance, personal contacts, jobs after graduation, etc.

I ended up with a non-tenured professor (actually, he was not even on the tenure track at the time) as my adviser, but also put several tenured professors on my committee, including some of the most senior ones in my specialty. In that way, I got the best of both worlds: the day-to-day attention from the primary adviser, combined with the resources and experience of the committee.

Professors develop reputations amongst graduate students. Some are known to graduate their Ph.D. students rapidly. Others are impossible to get hold of, so their students take forever to finish or leave without graduating. Some dictate what their advisees have to do, while others are accommodating of student interests. Ask around. What you learn may be revealing. And if circumstances change to make another professor a more appropriate match to your needs, don’t be afraid to switch if that is an overall win.

When picking a committee, you want to make sure they can cover all the areas of your thesis. You also want to make sure that it is likely that all the committee members will be available for meetings! Including too many professors who travel often will make it difficult to get all five or six together in one room for a three hour oral exam or proposal meeting. When scheduling such meetings, start by finding times when the difficult-to-reach professors are in town, and then add in the other committee members.

Balance and Perspective

“Life goes by so fast, that if you don’t stop and look around, you might miss it.”
- from the film Ferris Bueller’s Day Off“Generally speaking, people provide better maintenance for their cars than for their own bodies.”
- Scott Adams, The Dilbert Future

When I was in graduate school, my top priority was crystal clear to me: getting out with a Ph.D. Other people described me as “focused like a laser beam” on that goal. In retrospect, I may have been too focused. There is more to life than graduate work. Keeping your health and your sanity intact are both vital to achieving the primary goal of getting out.

Repetitive Strain Injury (RSI) is a major occupational hazard in our industry. Carpal Tunnel Syndrome is just one type of RSI. If you do not know how to set up your workspace for good ergonomics, learn now! The Pascarelli reference at the end of this guide is a good book on this subject. Over a dozen of my friends and coworkers have been inflicted with this problem. In severe cases, RSI can be a career-ending injury. If you can’t type, it’s rather difficult to write papers, computer programs, presentations, etc. Don’t let this happen to you! Prevention is the way to go. Recently I have been working with weights to strengthen my shoulders and wrists as an additional preventative step.

Earning a Ph.D. is like running a marathon. You have to learn to pace yourself and take care of your body if you want to reach the finish line. Unfortunately, students often act like sprinters running a marathon. They are highly productive for a while, but then fall by the wayside because they aren’t eating correctly, exercising, taking time out to recharge their batteries, etc. You maximize your long-term productivity by not ignoring those other aspects. While I was in graduate school, I took time out to travel up and down the East Coast, from Boston down to Orlando. That was an important part of keeping my stress down and recharging my batteries. I also did some running and circuit training for exercise. For shorter breaks, I shot nerf basketballs at a tiny hoop mounted in the graphics lab and kept a guitar in my office. Figure out what works for you.

It’s easy to lose perspective while in graduate school. You are surrounded by so many other smart, hard working people that it is easy to feel inferior and lose self-esteem and confidence. But without an underlying confidence that you do have what it takes to complete a dissertation, it’s too easy to drop out when the going gets tough instead of sticking it through. I found it useful to keep in touch with the “real world,” to remind myself that the graduate student population is not representative of humanity in general and to keep my perspective. You got into graduate school because you have already shown to your professors that you have potential and skills that are not typical among most college students, let alone most people — don’t forget that.

The Ph.D. job hunt

On résumés: “The closest to perfection a person ever comes is when he fills out a job application form.”
- Stanley RandallReal World, The (n.): Where a computer science student goes after graduation; used pejoratively (“Poor slob, he got his degree and had to go out into the REAL WORLD.”). Among programmers, discussing someone in residence there is not unlike talking about a deceased person.”
- the fortune program

Ideally, the job hunt begins years before you graduate. Networking is very important: while you are in the middle to late phases of your graduate studies, try to get yourself noticed by professors and industry people at other sites. One way to do this is to offer to give a talk about your work at another site. This is not that difficult to do, since most research places love to host seminars and bring in fresh ideas. Attending conferences and working elsewhere during the summer are other ways to get exposure. Make friends with graduate students and personnel at other schools. Make and carry your own business cards. Schmooze with important visitors during major site visits. For about two years, I ran the informal “Graphics Lunch” symposia at UNC. That means I was the point of contact for many speakers who visited UNC and that helped me make contacts. There is also a “star” system that exists. Certain outstanding graduate students can get labeled as “stars” by their professors and that can be an enormous help in getting an interview at CMU or other prestigious locations. It’s nice if you can get on that track but one shouldn’t rely upon it!

Networking is important because many jobs are found and filled that way. I got my position at HRL partially because I visited there, at my own expense, two years before I even started my job hunt. That meant that when I circulated my résumé, I was more than just a piece of paper to them. You are not going to be looking for job ads in the newspaper. Instead, you’ll look for announcements in major journals, at conferences, on the Net, and through your contacts. For industrial positions, it is crucial to get past the Human Resources department and find the individual with the ability to hire and deal with that person directly.

When do you start asking for interviews? You can start when you are able to give a talk about your dissertation work. Don’t be too early or too late, because you only get one chance per site. Academic positions generally have a particular “season” (much like getting admitted to school) that starts in the Fall and ends around April; industrial positions generally don’t follow that. The job hunt and interviewing process can take months; factor that into your time allocation.

The job supply and demand situation can vary dramatically in a few years, and anything I say here about how strong the job market is today (Jan. 2003) will likely be out of date by the time you read it.  For example, during the time I was job hunting (end of 1994 to early 1995), good positions were not easy to find. If I had a dollar for every site that told me “We don’t have a permanent position, but would you take a postdoc?” I could buy a lot of lunches. However, around 1997 the graphics job market became very strong, with many individuals getting multiple offers with high salaries. 1998 was an excellent year for people looking for tenure-track graphics faculty positions. I know many friends who found good tenure-track positions that year.  So when I revised the guide in 2000, I said the job market was strong with high demand. Of course, the tech industry went downhill at that point and hasn’t recovered yet. Now it is much more difficult to find research positions in industry or academia.  With luck, the market will be much better at the time you read this.

Before starting the job hunt, determine your goals and parameters in advance and the “angle” you will take to sell yourself. For example, my strength was in systems, so I chose to emphasize that in my cover letters. Customize your approach to each site, if time permits. What you do for your thesis determines who will and who won’t take a look at you. Try to get at least one reference from outside your university.

This guide is not going to cover the basics of interviewing; you can get that from many books (e.g. the Martin Yate and Bob Weinstein books listed in the references). However, I will mention some tips. Don’t interview on the day of arrival, and try to avoid Mondays and Fridays. Be prepared for hard or illegal questions, but you probably won’t get them. Do your homework on each site before interviewing! It continually amazes me that people show up for interviews without knowing anything about the institution they want to join. If the target is a research lab for a major company, you can easily find Wall St. Journal articles, annual reports and stockbroker reports in your library. If your goal is an academic position, check out the Tomorrow’s Professor site for guidance.  If you interview at a university, get their course catalog and use their numbering scheme to describe the courses you can teach. Interview to find out more about them, not just to sell yourself. Your 45-60 minute research presentation is crucial; make sure you practice it thoroughly. Interviews create interviews. That is, if you’ve already gone on many interviews at other places, then that makes you appear more desirable since others want you, and that makes it easier for you to get more interviews. Broadcast this fact by keeping your interview schedule on your web page. There is an anecdote about one student who received offers to interview at many different places, but only after Stanford interviewed him! Keep logs on who you talk to, what you talked about, and when. That makes it easier to keep things straight when juggling several contenders. The major conferences in your field are a good place to schedule preliminary interviews to get your foot in the door, because it is cheap for the company or university. The people you need to meet are already there, so that saves them the expense of having to fly you out and house you at their site.

Offers are a waiting game. Be prepared for lots of frustration. You need a written offer or nothing is official; you should also accept or reject in writing. Negotiate, but be aware of the strength or weakness of your position. Starting salary may not be as important as the type of work, benefits, and growth potential. Drug tests and other factors are becoming more common; you will have to decide how you want to respond to those.

Ah yes, salaries. Everybody wants to know about those. For academic (tenure track) salaries, you can get typical numbers from the annual Taulbee surveys, printed in the Computing Research News newsletter and the Communications of the ACM. Realize that these are 9-month salaries. Whether or not you can procure funding that covers 2 or 3 months of summer salary makes a big difference to your bottom line. Also, professors can make money by consulting at rates of $1000-2500 per day, although this is more common among established professors. Figures for industrial salaries are harder to come by. The Maisel and Gaddy references are the only ones I have found that specifically surveys young Ph.D.’s in industry (also see the chart a few paragraphs down). Salaries depend heavily on geography. Silicon Valley is in a league of its own, with salaries far above any other region. But before you decide to move to Palo Alto, remember that the cost of living there is also in the stratosphere. In Sept. 1997, a $60k salary in Indianapolis bought the same standard of living as a $101k salary in San Jose! The cost of living difference is larger today.  Decent houses in the Silicon Valley cost more than half a million.  More general computer science salary surveys are run by the IEEE and EE Times, available at the JobStar salary survey site.

Acquire salary information on your own by making use of your network. Don’t ask for someone’s salary directly, unless it’s someone you know very well and even then be very careful. Instead, bounce figures off people and see how they respond. Do they think the figure you mention is high, low, or about right? By seeing how people respond you can get an idea of what the market range is.

Factor in benefits and the expected workload into your compensation evaluation. That $100k offer may seem less attractive if you have to work 80 hour weeks in that position.  Traditionally, stock options made up a large fraction of the compensation packages for startups and Silicon Valley positions, but with the tech bust that may no longer be the case.

The type of work and compensation varies dramatically with the types of positions. Academic positions are tenure-track, research staff (non tenured) and postdocs. Tenure-track positions at major universities are fairly hard to come by; you need to be both good and lucky. Read the Feibelman and Ralston references for more details. The tenure-track also requires a lot of hours and dedication. As Randy Pausch put it, tenure is a competitive process where you get compared with the other assistant professors and the already-tenured professors. If they worked 70 hour weeks for six years to get tenure, don’t expect to get away with working 40 hour weeks. Postdocs are low paying but good for padding your C.V. if you think you need it to get a tenure track position. Just be sure to read the Feibelman reference, which tells you exactly what you need to do to survive a postdoc. In general, academic positions don’t pay as well as industrial positions, but universities offer more freedom, prestige, a richer intellectual environment and the possibility of long-term stability (with tenure). There’s a big difference between startups, regular industrial jobs, and industrial research positions. Startups can be the most lucrative financially, although that’s a big gamble. Read the Kawasaki and Bell references if you want to work at a startup. Expect to put in long hours while losing contact with the research community. Industrial research lies in an uncomfortable middle ground between production jobs and academic research, and blends the advantages and disadvantages of industry vs. academia.

The next two charts are the latest figures I have for academic and industrial salaries.



The above chart is from the March 2002 issue of Computing Research News.  It shows the results of the latest Taulbee survey of academic salaries, where nine-month assistant professor salaries average in the $70-80k range.



The above chart is from the November 2002 issue of Computing Research News, showing compensation for industrial positions (based on 11 organizations and 689 responses). Unsurprisingly, expected compensation from bonuses and stock options dropped significantly from 2000 to 2001.  Industrial compensation is higher than academic, although the Taulbee figures are for nine months of salary (not 12).  With summer salary and other supplements, the difference is reduced.  Still, Computing Research News estimates that total compensation for assistant and associate professors lags that of comprable industrial counterparts by 25%.

No matter where you go after you graduate, maintain your contacts with your alma mater. You may change jobs and move from place to place, but you will always have your degree from your university. If you keep good relations with your university and your fellow former students, that will serve as an excellent base for your personal network.

Conclusion

• Picture a martial artist kneeling before the master sensei in a ceremony to receive a hard-earned black belt. After years of relentless training, the student has finally reached a pinnacle of achievement in the discipline.”Before granting the belt, you must pass one more test,” says the sensei.

  “I am ready,” responds the student, expecting perhaps one final round of sparring.

  “You must answer the essential question: What is the true meaning of the black belt?”

  “The end of my journey,” says the student. “A well-deserved reward for all my hard work.”

  The sensei waits for more. Clearly, he is not satisfied. Finally, the sensei speaks. “You are not yet ready for the black belt. Return in one year.”

  A year later, the student kneels again in front of the sensei.

  “What is the true meaning of the black belt?” asks the sensei.

  “A symbol of distinction and the highest achievement in our art,” says the student.

  The sensei says nothing for many minutes, waiting. Clearly, he is not satisfied. Finally, he speaks. “You are still not ready for the black belt. Return in one year.”

  A year later, the student kneels once again in front of the sensei. And again the sensei asks: “What is the true meaning of the black belt?”

  “The black belt represents the beginning — the start of a never-ending journey of discipline, work, and the pursuit of an ever-higher standard,” says the student.

  “Yes. You are now ready to receive the black belt and begin your work.”

“Dissertations are not finished; they are abandoned.”
- Fred BrooksThe following story, called “The Parable of the Black Belt,” is excerpted from Built to Last: Successful Habits of Visionary Companies, by James C. Collins and Jerry I. Porras.

To me, there are two lessons in this story.First, the Ph.D. is the beginning, not the culmination, of your career. Don’t worry about making it your magnum opus. Get out sooner, rather than later.

Second, if you bother to talk to and learn from the people who have already gone through this process, you might graduate two years earlier.

Good luck.

Other Related Guides

• Tomorrow’s Professor, a collection of interesting articles for current graduate students and those seeking academic positions after graduation.
• The superb Graduate Student Resources on the Web! at U. Michigan
• Jeff Hollingsworth’s guides on job hunting
• Information resources for the graduate student. Includes MIT AI Lab guide and other guides from Indiana University and other places.
• Improving the Graduate School Environment for Women in Computer Science
• Graduate School Survival Links
• Advice on Research and Writing
• Rice University Chemistry grad student guide
• Marie des Jardin’s “How to be a Good Graduate Student”
• Stanford Graduate Student Survival Guide
• (Humor) A Day in the Life of a Grad Student
• (Humor) The Ph.D. vs. the Lotto
• The National Association of Graduate-Professional Students
• RPI’s Grad Student Survival Guide in Math Sciences
• Douglas Comer’s essays on computer science and the Ph.D.
• (Humor) Lord of the Rings as an allegory for getting the Ph.D.

Recommended Reading

Bell, C. Gordon and John McNamara. High-Tech Ventures: The Guide for Entrepreneurial Success. Addison-Wesley, 1991. ISBN 0-201-56321-5.
Dated, but recommended reading if you want to work for a startup.Bronson, Po. The Nudist on the Late Shift. Random House, 1999. ISBN 0375502777.
A fun read, giving the flavor of what working in the Silicon Valley is like.  Many of the chapters previously appeared as articles in Wired.  A snapshot of the culture before the tech bubble burst.

Covey, Stephen R. The 7 Habits of Highly Effective People. Fireside Simon and Schuster, 1989. ISBN 0-671-70863-5.
Excellent overall, with sections on time management, guiding principles and interpersonal skills.

EE Times Salary Survey Issue.
EE Times produces an annual survey and commentary about industrial salaries.

Feibelman, Peter J. A Ph.D. is Not Enough! A Guide to Survival in Science. Addison-Wesley, 1993. ISBN 0-201-62663-2.
Good discussion of research career paths. A must read if you choose to take a postdoc.

Kawasaki, Guy. The Macintosh Way: The Art of Guerrilla Management. Harper Perennial, 1990. ISBN 0-06-097338-2.
Despite problems that occurred at Apple, this book shows the energy and chutzpah required to survive in a startup.

Kelley, Robert E.  How to be a star engineer.  IEEE Spectrum (October 1999), 51-58.
Good description of the skills that are needed to excel at work, which go beyond sheer technical skills.

Kroeger, Otto and Janet M. Thuesen. Type Talk: The 16 Personality Types that Determine How We Live, Love and Work. Tilden Press, 1988. ISBN 0-385-29828-5.
Introduction to the Myers-Briggs type indicators, useful for interpersonal relations.

Maisel, Herbert and Catherine Gaddy. Employment and Salaries of Recent Doctorates in Computer Science. Communications of the ACM 40, 9 (September 1997), 90-93.

Maisel, Herbert and Catherine Gaddy. Employment and Salaries of Recent Doctorates. Communications of the ACM 41, 11 (November 1998), 99-101.
One of the few surveys I have seen for recent C.S. Ph.D.s that includes both industry and academic numbers. The low sample size is a problem, however.

Pascarelli, Emil and Deborah Quilter. Repetitive Strain Injury: A Complete User’s Guide. John Wiley and Sons, 1994. ISBN 0-471-59532-2.
A good introduction to RSI injuries and avoiding them.

Pastore, Robert R.  Stock Options: An Authoritative Guide to Incentive and Nonqualified Stock Options, 2nd edition.  (printed Dec. 1999).  ISBN 0966889924.  PCM Capital Publishing.
I haven't read this but I have been told this is an excellent reference for those of you fortunate enough to have a bundle of stock options.  Give me a few options as a tip for finding this book, ok?   The book covers tax and legal issues and gives advice on when to keep or exercise your options.

Ralston, Anthony. The Demographics of Candidates for Faculty Positions in Computer Science. Communications of the ACM 39, 3 (March 1996), 78-84.
A must read if you are looking for tenure track positions. The author is a former CS professor who led a faculty search, so if you don't believe what I say, then listen to him.

Weinstein, Bob. Résumés Don’t Get Jobs: The Realities and Myths of Job Hunting. McGraw-Hill, 1993. ISBN 0-07-069144-4.
Gritty, realistic job hunting guide for today's market.

White, Pepper. The Idea Factory: Learning to Think at MIT. Plume (Penguin Books), 1992. ISBN 0-452-26841-9.
While this is not about C.S., it does dispel the notion of graduate school as an ivory tower environment.

Yate, Martin. Knock ‘Em Dead: The Ultimate Job Seeker’s Handbook. Bob Adams, Inc.
Good generic guide to job hunting and interviews, including a long section on interview questions.

---

Last updated: Fri January 3, 2003

Questions? Send email to

Return to Ron Azuma’s page of guides on CS graduate school

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Copyright 1997-2003, Ronald T. Azuma, except for portions excerpted from elsewhere

Leading people is a fundamental skill

By Peggy Albright

The computing field asks a lot of its practitioners. Its professionals must be diligent, naturally curious, good problem solvers, able to grapple with big concepts as well as minutiae and willing to think outside of conventional boundaries to develop new code, architectures and applications that will find value in business and society. Yet in today’s competitive and global business environment, computing professionals must add yet another fundamental skill to the mix: the ability to lead and manage people.

In fact, developing leadership and managerial skills is no longer a career option, it is becoming a requirement, says Wade Shaw, an IEEE Fellow, editor-in-chief of IEEE’s Engineering Management Review and dean and professor of engineering at Mercer University.

According to Shaw, computing professionals today can expect to supervise someone else in some capacity within three to four years of completing an undergraduate degree. Most successful computer scientists and engineers will need to augment their technology skills with management responsibilities at some point in their career.

Teams and silos

The broadened expectations in the discipline are an outgrowth of an industry whose products are often built by project teams and whose members are often based in different parts of the world and contributing their skills from within various silos of expertise, Shaw said. With comparatively fewer advancement opportunities available for those who prefer to remain individual technology contributors in their companies, any strategy to advance one’s own value to an organization and earn better recognition and compensation must now involve management responsibility.

“The idea that someone changes their career midstream to enter management is an outdated notion,” Shaw said.

“It’s not ‘if’ I become a manager, it’s ‘when’ I become one,” he said. “That’s the starting point. It’s a key attitude difference.”

Soft skills essential

Whether a computing professional is just starting out, taking steps to advance from individual contributor status to project leader or managing multiple teams, or taking the leap from middle management to the executive ranks, the successful cultivation and use of “soft skills” is an essential part of the career advancement process.

Key among these soft skills is the ability to develop relationships with individuals and groups and to communicate effectively. Soft skills also include general business savvy, a big-picture understanding of the organization, a correct perception of one’s own role in the company and how other functions in the organization, such as finance and marketing, contribute to overall success. It includes learning how to think strategically.

“Our technical skills get us in the front door of an organization, but as we work there longer, we need soft skills,” said Dwight Jaggard, IEEE Fellow and director of the Executive Master’s in Technology Management program at the University of Pennsylvania, which is co-sponsored by Penn Engineering and The Wharton School.

Relationships matter

It might seem obvious, but strong relationship skills are among the most fundamental traits that technology professionals should have if they want to succeed in a leadership capacity. In fact, effective relationship skills are a necessity, not just an added value.

James Kouzes, executive educator, author of the best-selling book, The Leadership Challenge and the dean’s executive professor of leadership at the Leavey School of Business at Santa Clara University, says that poor relationship skills is the one weakness that is “most likely” to derail a technology professional’s ambition to succeed in management. While these skills have been emphasized in other careers, such as customer service and business development, they have not been routinely taught in the computing field. These skills can be learned, but they must be practiced regularly, and on a daily basis, Kouzes said.

“It is a competency that can be developed, but it does need time and attention,” he said.

His advice: It’s all about focusing. Listen to others’ points of view, learn how to resolve conflicts that leave both parties feeling that they’ve gained something, and if you need to give direction to non-technical people, learn to communicate coherently in non-technical language. And trust others to carry out their roles and responsibilities: lack of trust undermines one’s relationships and leads to managerial failure, he said.

Kouzes noted a few new observations from his most recent surveys of working professionals and executives around the world that should be of particular value to technology professionals who are in or seeking management roles. The first most important trait people want in their leaders is honesty, he said. The second most important trait is having a forward-looking vision that is shared by one’s peers and followers in an organization and an ability to communicate that vision to inspire others.
Additionally, he recommended that managers develop a competency in seeking feedback. This is one trait his respondents have indicted to be the most challenging, yet it is also vital to success because it important to be always learning. “The best leaders are the best learners,” he said. And feedback is an essential part of learning.

“If you want to go into management, you must ask for feedback. Be open to receiving it. Act on it. Take it earnestly and do something with it,” he said.

Big-picture perspective

Computing professionals taking steps to move higher in corporate ranks must recognize that the demands that come with developing soft skills may require that they disengage to a certain extent from the technology roots they established when they began their careers. It’s a commitment that needs to be taken seriously.

Ronald Danielson, vice provost for information services and chief information officer and associate professor of computer engineering at Santa Clara University, has found that as an IT professional advances in an organization, including to C-level roles, he or she is expected to have the same general business skills and traits as other top executives. A lack of these skills–whether real or perceived by upper management—can keep senior IT professionals from advancing to the top levels of the corporation, he said. The challenge for IT professionals is to overcome this perceived bias.

Computing professionals who are striving to advance into management, Danielson said, must recognize that once the commitment is made, there may be no going back to their former roles.

“The difficult thing for many technical people is that you can’t really do it all,” he said. “If you’re going to spend more of your time and energy on the soft skills that senior executives need to have, you’re necessarily going to give up some of your technical competencies. And that’s very unsettling because our discipline advances so rapidly that if you give that up for a while, it is very difficult to go back in as the same level of contributor that you were before.” CW
(8 July, 2009)

You can have a ball taking online quizzes on Facebook and other sites, but here are some things you should know before you do.

JR Raphael, PC World

Tuesday, May 12, 2009 04:00 PM PDT 

 

I am a genius. I’m charismatic, kind, and understanding. I’m also a Disney princess named Aurora and the reincarnation of Marilyn Monroe.

But I’m not crazy (at least, not completely). I’ve just been taking a lot of online quizzes lately — you know, the ones all over the Web promising to reveal your IQ, personality traits, or celebrity resemblances. Aside from discovering my inner Sleeping Beauty, I’ve also learned something important: These quizzes are about far more than providing users with enlightening or entertaining information.

The Real Deal

While Web quizzes may be fun to take, they’re also a powerful tool for companies to collect your data and even your money — and often in ways you might not notice. We’ll get to the spooky stuff in a moment, but let’s start with the simplest method of quiz-based marketing: advertising. The very nature of a typical online quiz requires you to divulge all sorts of details about yourself. Those tidbits of info are like nuggets of gold for advertisers craving a way to connect with you.

“The big trend is about engagement,” says Debra Aho Williamson, a senior analyst with eMarketer. “These quizzes are getting people to pay attention to ads.”

After more than 100 online ‘offers’ appeared, the author gave up on trying to obtain the results of the test he took.

Paying attention, it seems, is almost a requirement: Aside from being carefully targeted at your interests, the ads are often in-your-face and impossible to avoid. Take, for example, TheFreeIQTest.com, a quiz I found via a text ad on Google. By the time I clicked through the 105th “offer” (aka advertisement) it threw in front of my results — no exaggeration — I gave up without seeing the results of the quiz.

“There’s a clear annoyance factor, leading people to one thing, then at the last minute bait-and-switching them,” Williamson says. “The challenge with this type of advertising is walking that line between people wanting it and people wanting it to go away.”

The ads can follow you long after you click away, too. Just look at RealAge, a detailed quiz that assigns you a “biological age” based on your family history and health habits. The site, a recent investigation revealed, takes your most sensitive answers — those about sexual difficulties, say, or signs of depression — and sells them to drug companies looking to market medications.

Bigger Issues

Unwanted advertising, unfortunately, is only the tip of the iceberg. Some online quizzes will surprise you with required payments or purchases before you can access your results. While the requirement may be in the fine print somewhere, it’s often not in a place you’d easily notice before beginning the process.

It’s when the PayPal logo pops up that you realize Test-IQ.com wants $7 to give you your quiz results.

That’s exactly the scenario I found at Test-IQ.com, a quiz advertised on Facebook. The site’s home page makes no mention of a fee–you’d have to click to the privacy policy and read to the bottom to discover the $7 charge. Other sites, such as IQ-Test-Results.com, slip in recurring monthly fees for registered users.

You really have to dig to figure out what this quiz site wants to do with your credit card.
Then there are quizzes like CheckMyPersonality.com. Its Web site says, “Happy! (Shy) Sad? Outgoing, Fun? Which are you? Find Out for Free with CheckMyPersonality.com.” This site goes as far as to periodically access your credit card once you’ve signed up. I discovered a line in the company’s privacy terms that gives it an ongoing right to “verify that your credit card account is valid and has credit available” by charging fees and later crediting them off.

Worse, that line isn’t even in the terms linked on the home page–it’s in a secondary set buried deeper in the site. It comes up under a link labeled “Privacy Policy” on the fourth screen you reach as you fill out the quiz. The page is hosted on a different domain, and is separated from the site’s privacy policy page, but it is still branded as CheckMyPersonality.com.

CheckMyPersonality.com also authorizes its owners to dig up all kinds of information on you. The company states that it may use “third-party service providers” to track down everything from your household income to your buying habits–and then resell that data to marketing agencies.

“These [types of sites] are data-mining havens where users willingly opt-in from the very beginning,” says Ryan Jacobson, an attorney and cochair of the Entertainment Media and Privacy Law Group at the law firm SmithAmundsen in Chicago. “I’m afraid that the average user fails to recognize or take the time to understand what privacy rights he or she is actually giving up by responding.”

CheckMyPersonality.com, incidentally, didn’t respond to our requests for comment.

The Trust Factor

Ultimately, deciding whether you should take an online quiz comes down to a question of trust: Are you comfortable putting your information–personal or financial–into the owner’s hands? Remember, even if you don’t directly input data, it can be passed along. Such is the case with Facebook, where just opening an application automatically grants its developer access to your entire profile. And don’t assume that the developer isn’t going to use the information within.

“The very intimate and detailed nature of the information featured on Facebook profiles makes such a database very valuable to marketers,” says Guillaume Lovet, a senior manager with security company Fortinet.

Finally, bear in mind that the quizzes’ results may not even mean much. In the case of online IQ tests, for instance, many of the exams are about as valid as my excuse for missing mah-jongg night at the clubhouse.

“These things are simply not sophisticated,” says Dr. Martin Eaton, a licensed clinical psychologist and adjunct professor at the University of Southern California. “Calling them intelligence tests would be a misnomer.”

The test that declared me a genius, I can only assume, was a rare exception.

Connect with JR Raphael on Twitter (@jr_raphael) or via his Web site, jrstart.com.

I recently came across an application provided by Intel for the purpose of benchmarking NAS devices (Define: Network Attached Storage).  I’ve been looking for a solution to benchmark the new RAID-5 array I created on my Windows Server 2008 server (I love the RAID’s redundancy, but the write speeds are quite low).  I wanted to get a better idea of its performance, as Windows will report 250 – 150 MB READS and 20 – 50 MB WRITES, which is considerably poor considering the 4 drives are individually benchmarked at ~100 MB READS and 60 MB WRITES (They are Western Digital Caviar Blues / RE3 / RE16).

The software can be found here or from Intel here.

System Specifications:

Operating System: Microsoft Windows 2008 Enterprise Edition, SP1
CPU Type: Pentium 4 630, 3000 MHz w/ Hyperthreading
Motherboard Name: Dell Dimension 8400
Motherboard Chipset: Intel Alderwood i925XE
Motherboard Frontside Bus Speed: 800 MHz
System Memory (RAM): 4 GB, Dual Channel
System Memory Speed / Timings: DDR2-533 (266 MHz) / 4-4-4-12
Hard Drive Controller Interface: SATA I (1.5 Gbits / s) = 150 MB/s (maximum)
Hard Drives: Western Digital

• Drive #1 – WDC WD5002ABYS-01B1B (465 GB)
• Drive #2 – WDC WD5002ABYS-01B1B (465 GB)
• Drive #3 – WDC WD5000AAKS-00A7B (465 GB)
• Drive #4 – WDC WD5000AAKS-00A7B (465 GB)
• Drive #5 – WDC WD800BB-00JHC0 (74 GB) – SYSTEM

Raid Configuration: Software 4 disk RAID-5 (4 x 500GB, 1.36 TB Effective Storage Space)

System Memory Benchmark: Copy – 4790 MB/s, Read – 6266 MB/s, Write – 3341 MB/s, Latency – 92.7 ns
System Processor Benchmark: CPU ZLib – 10592 KB/s, CPU AES  – 3055

Benchmarks completed using Everest Ultimate Edition v4.20.1257

All drives are SATA II (3.0 GB/s) but are running in backwards-capability mode.

The primary roles of the server is to serve files and store backups (File Server) and web and application development and website hosting (Web Server).  I do not stream media, play music from the server or write extremely large amounts of data that are time sensitive.  Therefore, my decision for RAID-5 was based on the space savings (n – 1) storage availability and the redundancy by means of the parity.  I am willing to give up performance (write and read) for storage space and redundancy, but I want to know how much I’m actually giving up.

Tests

• HD Video Playback, 2x HD Video Playback, 3x HD Video Playback, 4x HD Video Playback

These benchmarks examine the behavior of the NAS unit while (simultaneously) playing one or more HD video files at 720p using Windows Media Player. Intel gives a percentage rate for the sequential reading of data in these tests, which lies at 99.5% for the HD Video Playback Test. With 2x HD Video Playback, it lies at 18.1%. The result is 6.6% with 3x HD Video Playback and 9.6 % with 4x HD Video Playback.

• HD Video Record

This test writes an HD Video file in 720p format to the NAS unit. This test is made of up of mostly sequentially transferred data.

• HD Video Playback & Recording

HD Video Play & Record examines the behavior of the NAS unit when simultaneously reading and writing an HD Video file in the 720p format. The sequentially-transferred data in this test is approximately 18% of the test.

• HD 2x Playback 2x Record

This benchmark is similar to the one above, but the proportion of sequential file operations is 3%.

• HD Playback With Office

This metric measures the data transfer rates when an HD Video file is read from the NAS unit while working with the Office applications. This test is made up of 608 files. The proportion of sequential file operations is 53.2%.

• HD Playback With Backup

Like the previous test, but this time an HD Video file is played while simultaneously carrying out a backup on the NAS unit.

• Content Creation

This benchmark is made up of 95% write operations to the NAS unit. This simulates the creation of files on the NAS unit such as is the case when, for example, using video editing programs.

• File Copy To NAS / File Copy From NAS

These tests determine the data transfer rate when copying files to or from the NAS unit. In both of these test processes, a 4 GB file is copied. Unlike with Backup / Restore, 64 KB is read and written.

• Directory Copy From NAS / Directory Copy To NAS

Similar to the previous test, files are copied to and from the NAS unit. A total of 126 files with a total size of approximately 188 MB are written and read across the network.

• Photo Album

This test determines how the NAS unit handles the supply of a multitude of small files—for example, viewing digital photographs stored on the NAS unit. It simulates the viewing of a total of 169 photographs with an overall size of approximately 1.2 GB.

Considerations and Assumptions

These tests were conducted on the Server, not over the network, which will probably result in very high throughput.  I did this to get a feel for the actual performance, not that felt over the wire, and because I’m currently running 100 Mbps (Cat 5e) on my LAN, not gigabit.  Thus, I did not want the network to be the bottleneck in these tests, as it would be saturated and skew the results.

I ran the tests 6 times, in each, manipulating the environment to establish a real world benchmark.  Since this benchmark is done via an application, it will compete with other processes for resources.  Additionally, it will compete with the actual Operating System who is responsible for transfering the data to the disk and calculating the parity for the raid (which is quite memory and CPU intensive).  For test 6 I ran the system in Safe Mode, utilizing only necessary processes and services to run the operating system (Safe Mode without Networking).  Thus, the numbers are quite high.  In the others, I ran Outlook, SQL, browsed the internet and downloading files via torrents (to the actual RAID, so this will mess with the numbers as well) – not all at the same time, of course, except for test 4.

Results

Test	1	2	3	4	5	6	Avg MB/s
HDVideo_1Play	267.789	267.789	80.364	54.54	59.134	274.603	167.37
HDVideo_2Play	223.736	223.736	68.638	44.619	75.123	223.204	143.18
HDVideo_4Play	177.201	177.201	70.222	54.731	73.009	180.005	122.06
HDVideo_1Record	626.098	626.098	651.41	618.888	678.788	732.636	655.65
HDVideo_1Play_1Record	82.744	82.744	85.224	71.802	79.058	155.986	92.93
ContentCreation	6.199	6.199	5.983	5.628	6.141	6.022	6.03
OfficeProductivity	25.383	25.383	25.474	20.958	26.249	27.955	25.23
FileCopyToNAS	633.475	633.475	748.91	690.827	768.598	806.578	713.64
FileCopyFromNAS	147.096	147.096	79.675	57.42	80.561	151.573	110.57
DirectoryCopyToNAS	65.156	65.156	25.13	18.384	23.418	81.739	46.50
DirectoryCopyFromNAS	44.048	44.048	20.412	15.53	20.929	47.274	32.04
PhotoAlbum	27.748	27.748	26.411	19.508	28.171	29.204	26.47

Discussion

I attribute the absurd file copy speeds to caching and buffering at both the OS, RAID and the Drive level, as well as the writes for the video recording.  All in all, I’m pleased with the benchmarks and am satisfied with the set up.  One thing to note is that the tests were done from the SYSTEM drive to the RAID, and thus, the SYSTEM drive introduced an additional bottleneck as the transfers from it to the RAID were going to be quite slow (the drive is benchmarked at ~56MB READ and ~30 WRITE).

 

 

By Alexis Madrigal October 06, 2008 | 11:00:00 PM

 

 

 

FREMONT, California — Solar cells have been converting sunlight into electricity for years, but scientists have been much less successful at turning that technology into money.

Now, in a staid Bay Area office park, a converted hard-drive factory with a shiny new façade has begun churning out unconventional solar tubes that could change the economics of solar power.

The highly-automated factory belongs to Solyndra, a three-year-old company that has received $600 million in venture capital and $1.2 billion in orders for its new modules, which look like curtain rods. Those big investors are betting the company’s unique product will soon blanket commercial buildings across the world.

Instead of the standard panels mounted on racks that have dominated solar for the last 20 years, Solyndra’s cylindrical solar modules collect sunlight more efficiently across a broader range of angles and catch light reflected off the roof itself. The solar cells also contain no silicon, which has been a costly component of most solar systems.

Targeted at a highly specific market — office and big-box rooftops — and with signed contracts in hand, the company, along with a small cadre of other well-funded solar startups, are racing to turn their scientific and engineering marvels into profitable businesses.

The scramble, the money, and the size of the prize — a big slice of the trillions of dollars made in energy — remind the company’s founder, Chris Gronet, of his earlier experience in the industry that became the basis for the information revolution.

“We think the solar industry or market look very similar to the way semiconductor manufacturing was 20 years ago,” Gronet, Solyndra’s CEO, told Wired.com. “We say, ‘Wow this is familiar. We’ve been through this before.’”

All types of solar power have experienced growth in the wake of increasing awareness of the risks of climate change and the rising costs of fossil fuels. A report released last week by Lux Research, a solar-focused analysis firm, predicts that the total solar market will grow from $33.4 billion in 2008 to $100.4 billion in 2013. While traditional silicon-based solar cells continue to underpin most solar systems, there is a broad expectation among industry analysts and insiders that these new thin-film solar cells, such as Solyndra is making, will experience rapid growth. While thin-film cells aren’t as efficient at using the sun’s energy as their silicon competitors, they cost less to produce.

Instead of using wafers of material, a la computer chips or traditional solar PV, thin-film solar cells use tiny amounts of material deposited in ultra thin layers along the surface of glass or metal. In Solyndra’s case, vice president of business development Kelly Truman said that their process uses just a bit more than a micron of copper indium gallium diselenide, or CIGS. Using less of the expensive photovoltaic material drives the cost of their production down.

For years, CIGS technology had appeared the most promising for cheap solar power. The National Solar Technology Roadmap, created by the National Renewable Energy Laboratory, states that steady efficiency improvement “could ultimately allow CIGS to achieve the lowest module costs and levelized cost of energy among all PV technologies.”

The total solar market can be broken into three main pieces: solar for utilities, residential installations and commercial buildings. Solyndra is focusing exclusively on the commercial side. What Gronet envisions is solar panels installed on your average Home Depot or Ikea, generating a substantial percentage of the company’s power needs right on site.

On the roof of the Solyndra office buildings, they’ve installed the first Solyndra array. What’s striking about the system is how simple it appears: The solar tubes look like reverse fluorescent light bulbs that generate electricity rather than using it. The mounting system is also light and small, as you can see in the image. They don’t have to be bolted to roofs because the spacing between the cylinders makes them less susceptible to wind damage than traditional flat solar panels.

But despite the industry’s high hopes, CIGS solar cells have proven very difficult to manufacture at industrial scales. Greentech Media analyst Michael Kanellos said that the risks for CIGS thin-film players have “increased dramatically” over the last few months with the worsening financial system and increased competition.

“Some CIGS will survive, but a lot of these companies might only leave a wet spot on the pavement,” Kanellos wrote in an e-mail to Wired.com.

Kanellos noted that Solyndra’s cylindrical design was advantageous, but also the most difficult to manufacture.

“Everyone else is having trouble making efficient flat CIGS panels. Curving adds another layer of complexity,” Kanellos wrote. “It is part of the reason that their contracts call for the delivery of their solar panels from now to 2012.”

Only two other CIGS-based thin-film manufacturers have managed to start cranking out actual saleable product. Nanosolar and Global Solar started selling cells last year. Solyndra, after hundreds of millions of dollars of investment, generated its first revenue in the third quarter of this year.

If Gronet and his team can work out the manufacturing challenges and navigate the difficult financial waters, their unique design and tightly focused business model could lead them to profitability, even after government subsidies in Europe phase out.

“In any unsubsidized world, which is a few years down the road, you need a cost structure that allows you to compete,” Gronet said. “Our panel, because it’s CIGS and thin film, will beat the costs of any silicon system.”

WiSci 2.0: Alexis Madrigal’s Twitter , Google Reader feed, and webpage; Wired Science on Facebook.

Sep 18 03:37 AM US/Eastern

The Internet got smarter this week with the release of a semantic map that teaches computers the meanings behind words — and gives the machines a vocabulary far larger than that of a typical US college graduate.

Cognition Technologies began licensing the map Tuesday to software creators interested in having programs “understand” words based on tenses and sentence context — in much the same way as the human brain does.

“We have taught the computer virtually all the meanings of words and phrases in the English language,” Cognition chief executive Scott Jarus told AFP.

“This is clearly a building block for Web 3.0, or what is known as the Semantic Web. It has taken 30 years; it is a labor of love,” Jarus said.

The semantic map is reportedly the world’s largest, and gives computers a vocabulary more than 10 times as extensive as that of a typical US college graduate.

The coming third generation of life online is predicted to feature intuitive artificial intelligence applications that work swiftly across broadband Internet connections.

When applied to Internet searches, semantic technology delivers results oriented to what people seem to be seeking instead of simply matching words used to online content.

For example, a semantic online search for “melancholy songs with birds” would know to link sadness in lyrics with various species of birds.

Cognition’s semantic map is already used in a LexisNexis Concordance “e-discovery” software to sift through documents amassed during evidence phases of trials.

“We help them find the needle in a haystack,” Jarus said.

“It used to be boxes and boxes of paper and now 80 percent of it is digital. Lawyers can search for a smoking gun within that discovery material.”

Cognition’s Caselaw program uses the technology to mine more than a half-century of US federal court decisions for legal precedents, according to the company.

The semantic map is also employed in a widely-used medical database.

Cognition says it has also “semantically enabled” globally popular online encyclopedia Wikipedia.

A Web 3.0 target is to develop artificial intelligence “agents” that mine mountains of information on the Internet for material that suit the interests of the people they serve.

“It would be a software application constantly looking for things you might be interested in while accurately understanding the concepts of what you are looking for,” Jarus said.

He described it as “artificial intelligence agents working for you on a push basis instead of a pull basis.”

Cognition has a handful of rivals, with each firm taking its own approach to semantic technology.

In July US software giant Microsoft bought San Francisco-based Powerset, a three-year-old start-up which specializes in interpreting the intent of people’s Internet searches instead of matching specific words they use.

Microsoft said it plans to use Powerset technology to enhance its free Live Search service, which has been mired in third place behind Google and Yahoo in the lucrative Internet search-related advertising arena.

Powerset’s semantic search merges linguistics with engineering in a software platform to figure out what people are seeking based on questions or phrases.

Standard search engines respond to individual words in the search query.

Microsoft senior vice president of search, portal and advertising Satya Nadella said at the time that a third of today’s online searches don’t get people the answers they seek on the first try.

“Search engines don’t understand today that ’shrub’ and ‘tree’ are similar concepts,” Nadella wrote in a blog posting.

“We don’t understand that ‘cancer’ sometimes refers to a disease and sometimes refers to a horoscope and when a query or a webpage refers to which.”

Financial terms of the deal were not disclosed but unconfirmed reports were that Microsoft may have paid as much as 100 million dollars for Powerset.

http://www.breitbart.com/article.php?id=080918073717.xvg33wf1&show_article=1

I was contacted by a Public Relations individual from uCertify who requested me to review their product.  This was quite some time ago, and since I had never used their product before, decided to test their test preparation software out in addition to others.  This short review will not mention other applications, suites or books for the preparation for this exam, only the uCertify application.

The first thing you’ll notice when you download the program, is that they offer a few things.  First, there’s 100% money back guarantee that you’ll pass the test on the first try.  That’s enticing and speaks volumes on their product.  Unfortunately, the money back guarantee is on the ~$70 you spent on the program, and not the $150+ on the exam, but alas, something is better than nothing.  At the very least, you can use that money and try again on the exam (or buy a different prep book).

Second, they offer 2 flavors of their software: a full and free (demo) version.   The demo version has a few questions, enough to give you an idea of what’s going on, but not enough for you to do anything with (ie. pass the test, study from, etc).

Lastly, I like the fact that they give you directions on how to download and install the application.  That’s very thoughtful for users who may not be highly technical but want to be, and are therefore attempting to get certified in a particular technology (this is becoming more and more common, even though the certification is supposed to be taken after experience has been attained in the work place, to validate your skills).

So let’s run the application.

As a software developer myself, I have to say that I like the initial interface a lot.  Everything is very well place and easy to navigate.  The demo version only has a Diagnostic Test (15 questions), Create a Custom Test and Create a Fixed Time Test available to you in the main navigation section.  The “Enhance your understanding” section on the right hand side has all of the items available, which are basically extras to give you an ‘edge’.  It’s mostly general information, some tips, and other resources you can find on the internet.  You don’t need this section, but it’s nice to have everything in one place anyway.

uCertify has their advertisements all over the place “Buy the full version”, which makes me laugh.  That’s what they’re supposed to do, and they do a pretty good job in keeping it in your face as you go through the demo.

Diagnostic Test:

I ran it with 10 total questions, 10 minute duration and a passing score of 730.  I also selected the “Always show last answer option as ‘None of the above’” which I believe to be crucial in getting you to think critically.  Why?  Because everyone will default themselves to “the answer must not be here” if they can’t figure it out or come up with an answer based on incorrect assumptions.  Since it’s there all the time, and you won’t know when it’s a valid option, you’ll definitely think twice before choosing it.  In my courses, I always have this option available to students.

Well, the first thing I noticed while taking the Diagnostic Quiz is that my settings didn’t stick.  I noticed at the bottom that I was on question 10 of 15 (I said a max of 10) and that the “Always show ‘none of the above’” wasn’t being shown on all questions.  Maybe this is reduced functionality for the demo, but it would have been nice to see that stated.

I found the questions a bit vague.  They’re not meant to confuse you, but require a good understanding of the underlying topic for you to know what they’re referring to.  The application does assist you with some background information and explanation, but I don’t believe it to be enough for a full understanding of the topics, but again, they are quite helpful.

The results page is fairly straight forward.  A little hard to read, maybe alternating row colors would have been nicer on the eyes.  Anyway, it tells you what you did right and wrong.  Clicking on the question will take you to the page of that question, with the answer you gave and why you were right or wrong, along with those ‘Facts’ that help you solidify your understanding.

I really like the Readiness report, which gives you a summary on how you’ve done on all the exams (there are 5 practice tests, a final test, and a diagnostic test – 423 questions in total).  They also provide an adaptive test, but that functionality is not available, so I cannot comment on it.

All in all, I believe this to be a good product.  It’s obvious the uCertify is trying to get you to pass the test, as they provide you with several ways of doing so.  If I were going to use this product, I would read a book prior to build a decent foundation, and then polish my skills and knowledge off with this application and its exams.  If you did that, you would have taken 7 tests in the uCertify application, and an additional 1 or 2 in the Microsoft book, for a total of 8 or 9.  You would iterate through the content and examinations enough to know what you have to study and understand better, and know where your strengths are.

If you used the approach above, it would cost you about $100 (Microsoft Self-Paced Certification Books – really the only books available for MCTS-ASP.NET) and $60 for the uCertify application.  That’s $160 in prep (and maybe 1 – 3 months) and then the cost of the exam, which is about $200 or so (don’t quote me on that).  When you buy the Microsoft books, you get a voucher for a discount on the actual exam, so that helps. I believe it to be worth it.

The feedback from the exams is that you need to really memorize the book’s content and have lots of coding experience (more project experience than just slapping code together).  In that respect, an application such as this one will really help you with that, as they ask you real-world type questions.  The link below is to a site of a gentlemen who gives his experience from taking the exam: here

Rating of the Application:

Visual Appeal: 8
Question Difficulty: 8
Extra Content to Help (ie. Tips): 10
Number of Questions / Exams: 9
Reporting: 9
Exam Preparedness: 8.5
Overall: 8.75

I would say this system will help anyone pass the exam, as long as they have the knowledge.  I would / will use this system in the future.

Take a look for yourself: http://www.ucertify.com/exams/Microsoft/70-536-VB.html

You can receive a 10% discount on any certification course you wish to attend using the code STEVEH.  Thank you to Roger and uCertify for their generosity.

Computerworld (07/23/08) Gaudin, Sharon

As Intel reaches its 40th anniversary, a top company executive looks forward 40 years to the point when human intelligence and machine intelligence starts to merge. Intel CTO Justin Rattner says that perhaps as early as 2012 we will start to see the lines between human and machine intelligence blur, with nanoscale chips or machines moving through our bodies to fix damage organs or unclog arteries. Rattner also says virtual worlds will become increasingly realistic, and robots will develop enough intelligence and human characteristics that they will become companions. Most aspects of our lives will be rather different as we approach the year 2050, with computing becoming less about running applications and more about living lives in which computers are inextricably woven into daily activities. “The intelligent systems will move from being information systems to intelligent systems that will carry out a whole variety of tasks that we just won’t think of as computing tasks,” says Rattner. “The technology will find its way into so many things we do, and we won’t even think about it. The explicit way we’ve done computing in the past will be there, but it will be a very small subset of what we’ll be doing.” Chip advancements will continue throughout the semiconductor industry, though computer chips will extend beyond computers and phones as people seek to become more connected in virtual worlds and computers learn to react to our motions and thoughts.