Software Carpentry Revisited

By Nicole Hemsoth

July 18, 2011

Software engineering is still something that gets too little attention from the technical computing community, much to the detriment of the scientists and engineers writing the applications. Greg Wilson has been on a mission to remedy that, mainly through his efforts at Software Carpentry, where he is the project lead. HPCwire asked Wilson about the progress he’s seen over the last several years and what remains to be done.

HPCwire: We last spoke five years ago about Software Carpentry — your work to improve the software development skills of scientists and engineers. Have you been able to see any progress along this front?

Greg Wilson: Yes, on a small scale, but no, not in general. A lot of students and professionals have used the Software Carpentry materials — we get several hundred hits a day, mostly via Google searches — and based on their feedback, they do find them useful. Elsewhere, we have seen a growing number of conscientious scientists worrying about the problems of sharing and reproducibility, and other courses like Software Carpentry springing up, primarily in bioinformatics and astronomy.

Overall, though, I have to say that most scientists and engineers don’t use computers any more proficiently today than they did twenty years ago, never mind five. For example, I would bet that the percentage of grad students in science and engineering departments using version control to keep track of what they did when, and to share their work with colleagues, hasn’t shifted in that time.

HPCwire: What hasn’t improved?

Wilson: Fundamentally, what hasn’t improved is people’s ability to do math. Suppose that picking up some basic computational skills—version control, testing, Make, the shell, using a debugger, and so on—takes five full-time weeks. Whether that’s one five-week marathon, or the time is spread out over several months, it still costs roughly 10 percent of the scientist’s annual salary, if you’re thinking like an administrator, or 10 percent of their annual published output, if you’re thinking like a grad student’s supervisor.

If we assume our scientist only keeps doing research for another 10 years (which I hope is pessimistic), and a depreciation rate of 20 percent (which I also hope is pessimistic), then this only has to improve the scientist’s productivity by 2.4 percent in order to pay for itself. That works out to just under an hour per week during those ten years; anything above that is money or time in the bank. Looking at the results of the survey we did in 2008, even scientists who _aren’t_ primarily computationalists are spending a lot more time than that wrestling with software.

Now suppose the feedback we get from people who’ve taken the course is right, and that these skills save them a day a week or more. Let’s assume the average scientist or engineer costs $75,000 a year. 20 percent of their time over ten years, at the same 20 percent discount rate, works out to roughly $63,000; at a more realistic discount rate of 10 percent, it’s roughly $93,000. That’s roughly a ten-fold return on $7,500 — five weeks of their time right now at the same annual salary.

So why don’t people do it? Or to put a sharper point on it, why don’t their bosses and supervisors require them to? I think there are four reasons:

(1) Time and money spent show up in the budget; time and money saved through higher productivity don’t. Of course, this is a problem for more than just computational skills training.

(2) Sure, if I knew some Perl, I could solve this problem in five minutes instead of an hour, but learning that much Perl will take two days, and the deadline for this paper is tomorrow. And then I have to prepare a mid-term for the course I’m teaching, or fill in my benefits paperwork.  Something that pays off in the long run is not useful if all our deadlines are short-term.

(3) It’s a case of the blind leading the blind. If most of the people around you don’t know how to automate tasks using Make and the shell, for example, you’re unlikely to start doing it yourself. And yes, there are lots of good tutorials on the web, but it’s hard to find the right ones if you don’t know what keywords the cognoscenti use to describe these things, and even harder to understand them.

(4) Institutionally, the people who fight for scientific computing resources are usually those doing HPC, and because of (3), they almost always fight for more hardware, rather than the skills to use that hardware effectively. Most HPC vendors aren’t any more enlightened, which is shortsighted. If more people knew how to do simple things well, more of them would try advanced things, which would lead pretty quickly to increased sales. Right now, though, it’s easier to get a million dollars for a new cluster than a hundred thousand to train people how to use computers effectively.

HPCwire: Are there software development skills or practices that turned out to be more difficult to impart to non-computer science types than you first thought?

Wilson: Most of the difficulty has actually been our misconceptions of what scientists and engineers want, rather than difficulties on their side. Scientists and engineers _do_ tend to be fairly smart people. As a computer scientist, I always want to teach fundamental principles of computing that can be widely applied. As per point (2) above, what students can actually invest time in is solutions to the specific problems they face today. They’re happy to have the general principles explained after the fact, if ever, and even happier to infer those general principles themselves from lots of useful worked examples.

It’s sometimes possible to find a happy medium, and I think our lectures on regular expressions and SQL do so. But in other areas, where the payoff takes longer, it’s really hard to find a path where every step is immediately rewarding. For example, object-oriented programming doesn’t solve any problem that people writing hundred-line programs realize they have.

This is all complicated by the fact that for a lot of people in engineering, neuroscience, and other fields, computing means computing in a specific platform like R, SPSS, SAS, or MATLAB — and even then, “MATLAB” might actually mean a large domain-specific package on top of MATLAB itself. Most of our course materials are in Python, and while it’s an easy language to learn, someone who whose colleagues work exclusively in R will quite rightly think that learning a new language is a high price to pay for some insights whose value isn’t immediately apparent.

Reaching those people would require an retooling for every single language, which we simply don’t have the resources to do.  However, these people can and do benefit from generic material on version control, the shell, and databases, so that’s where more of our effort is currently going.

HPCwire: HPC practitioners seem to be of two minds about optimizing software workflow. Some believe the emphasis needs to be on minimizing development time, while others believe maximizing runtime performance is paramount. Often these two approaches are at odds with one another. Where do you stand on this dynamic?

Wilson: It’s a false dichotomy, and a dangerous one to boot. Given the complexity of modern architectures, the only way to make something fast is to get it working, build some tests so that you can tell when subsequent changes break things, and then start tweaking it based on performance profiling. Maximizing runtime performance therefore doesn’t compete with minimizing development time; it _requires_ it, particularly if you’re then going to have to move it to a slightly different chip set, or maybe, a few years down the road, port it to a very different architecture.

HPCwire: You recently performed a study on how scientists develop and use software? What were the major findings?

Wilson: Yes, in the fall of 2008 we did an online survey of how scientists and engineers use computers, where they learned what they know, and so on.  1,972 people responded, and we published the results in 2009. The major finding, in my opinion, was to confirm that almost everyone in science and engineering is primarily self-taught when it comes to computing, and that they’re spending a lot of time banging their heads against software problems.

HPCwire: Based on the study results, what do you think needs to be done now to help scientists adopt better software practices?

Wilson: The easy answer is, “Put more computing lab courses in undergraduate programs,” but that’s not realistic. As a physicist once said to me, “What should we take out to make room — thermodynamics or quantum mechanics?” Another solution would be to require people to pass something like a driving test before letting them use big iron, but that will never fly politically — as much as people working in HPC centers might want it to.

Realistically, I think there are only two possibilities. The first is for HPC vendors to start emphasizing these skills as a prerequisite for getting your money’s worth out of that shiny new cluster you just bought. The second is for journal editors to start requiring some evidence of competence when people submit work with a large computational component. I don’t think full reproducibility is a realistic goal, but [something like] “All of our code is under version control, it can be built with a single command, or with two commands, if there’s a separate configuration step, and we have a test suite that exercises at least _some_ of its functionality,” would be an excellent start.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

Nvidia Appoints Andy Grant as EMEA Director of Supercomputing, Higher Education, and AI

March 22, 2024

Nvidia recently appointed Andy Grant as Director, Supercomputing, Higher Education, and AI for Europe, the Middle East, and Africa (EMEA). With over 25 years of high-performance computing (HPC) experience, Grant brings a Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Houston We Have a Solution: Addressing the HPC and Tech Talent Gap

March 15, 2024

Generations of Houstonian teachers, counselors, and parents have either worked in the aerospace industry or know people who do - the prospect of entering the fi Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire