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 industy updates delivered to you every week!

LANL Researchers Simulate Billion-Atom Biomolecule

April 23, 2019

Simulating large biomolecules has long been challenging. Now, researchers from Los Alamos National Laboratory, RIKEN Center for Computational Science in Japan, the New Mexico Consortium, and New York University have succ Read more…

By John Russell

Students Gird for Cluster Mayhem at ASC19

April 23, 2019

Final cluster configurations have been set, and competitors in the ASC19 Student Supercomputer Challenge have started running the various AI models and HPC benchmarks that will determine who is declared champion. But if Read more…

By Alex Woodie

Student Cluster Season Opener: ASC19

April 22, 2019

Calling all computer sports fans! Now hear this:  The 2019 Student Cluster Competition season is officially underway with the beginning of the ASC19 event on Tuesday, April 22nd. For you millions of student cluster c Read more…

By Dan Stark

HPE Extreme Performance Solutions

HPE and Intel® Omni-Path Architecture: How to Power a Cloud

Learn how HPE and Intel® Omni-Path Architecture provide critical infrastructure for leading Nordic HPC provider’s HPCFLOW cloud service.

powercloud_blog.jpgFor decades, HPE has been at the forefront of high-performance computing, and we’ve powered some of the fastest and most robust supercomputers in the world. Read more…

IBM Accelerated Insights

Bridging HPC and Cloud Native Development with Kubernetes

The HPC community has historically developed its own specialized software stack including schedulers, filesystems, developer tools, container technologies tuned for performance and large-scale on-premises deployments. Read more…

A Beginner’s Guide to the ASC19 Finals

April 22, 2019

Three thousand watts. That's how much power the competitors in the 2019 ASC Student Supercomputer Challenge here in Dalian, China, have to work with. Everybody would like more juice to run compute-intensive HPC simulatio Read more…

By Alex Woodie

A Beginner’s Guide to the ASC19 Finals

April 22, 2019

Three thousand watts. That's how much power the competitors in the 2019 ASC Student Supercomputer Challenge here in Dalian, China, have to work with. Everybody Read more…

By Alex Woodie

At ASF 2019: The Virtuous Circle of Big Data, AI and HPC

April 18, 2019

We've entered a new phase in IT -- in the world, really -- where the combination of big data, artificial intelligence, and high performance computing is pushing Read more…

By Alex Woodie with Doug Black and Tiffany Trader

Interview with 2019 Person to Watch Michela Taufer

April 18, 2019

Today, as part of our ongoing HPCwire People to Watch focus series, we are highlighting our interview with 2019 Person to Watch Michela Taufer. Michela -- the Read more…

By HPCwire Editorial Team

Intel Gold U-Series SKUs Reveal Single Socket Intentions

April 18, 2019

Intel plans to jump into the single socket market with a portion of its just announced Cascade Lake microprocessor line according to one media report. This isn Read more…

By John Russell

BSC Researchers Shrink Floating Point Formats to Accelerate Deep Neural Network Training

April 15, 2019

Sometimes calculating solutions as precisely as a computer can wastes more CPU resources than is necessary. A case in point is with deep learning. In early stag Read more…

By Ken Strandberg

Intel Extends FPGA Ecosystem with 10nm Agilex

April 11, 2019

The insatiable appetite for higher throughput and lower latency – particularly where edge analytics and AI, network functions, or for a range of datacenter ac Read more…

By Doug Black

Nvidia Doubles Down on Medical AI

April 9, 2019

Nvidia is collaborating with medical groups to push GPU-powered AI tools into clinical settings, including radiology and drug discovery. The GPU leader said Monday it will collaborate with the American College of Radiology (ACR) to provide clinicians with its Clara AI tool kit. The partnership would allow radiologists to leverage AI techniques for diagnostic imaging using their own clinical data. Read more…

By George Leopold

Digging into MLPerf Benchmark Suite to Inform AI Infrastructure Decisions

April 9, 2019

With machine learning and deep learning storming into the datacenter, the new challenge is optimizing infrastructure choices to support diverse ML and DL workfl Read more…

By John Russell

The Case Against ‘The Case Against Quantum Computing’

January 9, 2019

It’s not easy to be a physicist. Richard Feynman (basically the Jimi Hendrix of physicists) once said: “The first principle is that you must not fool yourse Read more…

By Ben Criger

Why Nvidia Bought Mellanox: ‘Future Datacenters Will Be…Like High Performance Computers’

March 14, 2019

“Future datacenters of all kinds will be built like high performance computers,” said Nvidia CEO Jensen Huang during a phone briefing on Monday after Nvidia revealed scooping up the high performance networking company Mellanox for $6.9 billion. Read more…

By Tiffany Trader

ClusterVision in Bankruptcy, Fate Uncertain

February 13, 2019

ClusterVision, European HPC specialists that have built and installed over 20 Top500-ranked systems in their nearly 17-year history, appear to be in the midst o Read more…

By Tiffany Trader

Intel Reportedly in $6B Bid for Mellanox

January 30, 2019

The latest rumors and reports around an acquisition of Mellanox focus on Intel, which has reportedly offered a $6 billion bid for the high performance interconn Read more…

By Doug Black

It’s Official: Aurora on Track to Be First US Exascale Computer in 2021

March 18, 2019

The U.S. Department of Energy along with Intel and Cray confirmed today that an Intel/Cray supercomputer, "Aurora," capable of sustained performance of one exaf Read more…

By Tiffany Trader

Looking for Light Reading? NSF-backed ‘Comic Books’ Tackle Quantum Computing

January 28, 2019

Still baffled by quantum computing? How about turning to comic books (graphic novels for the well-read among you) for some clarity and a little humor on QC. The Read more…

By John Russell

IBM Quantum Update: Q System One Launch, New Collaborators, and QC Center Plans

January 10, 2019

IBM made three significant quantum computing announcements at CES this week. One was introduction of IBM Q System One; it’s really the integration of IBM’s Read more…

By John Russell

Deep500: ETH Researchers Introduce New Deep Learning Benchmark for HPC

February 5, 2019

ETH researchers have developed a new deep learning benchmarking environment – Deep500 – they say is “the first distributed and reproducible benchmarking s Read more…

By John Russell

Leading Solution Providers

SC 18 Virtual Booth Video Tour

Advania @ SC18 AMD @ SC18
ASRock Rack @ SC18
DDN Storage @ SC18
HPE @ SC18
IBM @ SC18
Lenovo @ SC18 Mellanox Technologies @ SC18
One Stop Systems @ SC18
Oracle @ SC18 Panasas @ SC18
Supermicro @ SC18 SUSE @ SC18 TYAN @ SC18
Verne Global @ SC18

IBM Bets $2B Seeking 1000X AI Hardware Performance Boost

February 7, 2019

For now, AI systems are mostly machine learning-based and “narrow” – powerful as they are by today's standards, they're limited to performing a few, narro Read more…

By Doug Black

The Deep500 – Researchers Tackle an HPC Benchmark for Deep Learning

January 7, 2019

How do you know if an HPC system, particularly a larger-scale system, is well-suited for deep learning workloads? Today, that’s not an easy question to answer Read more…

By John Russell

Arm Unveils Neoverse N1 Platform with up to 128-Cores

February 20, 2019

Following on its Neoverse roadmap announcement last October, Arm today revealed its next-gen Neoverse microarchitecture with compute and throughput-optimized si Read more…

By Tiffany Trader

Intel Launches Cascade Lake Xeons with Up to 56 Cores

April 2, 2019

At Intel's Data-Centric Innovation Day in San Francisco (April 2), the company unveiled its second-generation Xeon Scalable (Cascade Lake) family and debuted it Read more…

By Tiffany Trader

France to Deploy AI-Focused Supercomputer: Jean Zay

January 22, 2019

HPE announced today that it won the contract to build a supercomputer that will drive France’s AI and HPC efforts. The computer will be part of GENCI, the Fre Read more…

By Tiffany Trader

Oil and Gas Supercloud Clears Out Remaining Knights Landing Inventory: All 38,000 Wafers

March 13, 2019

The McCloud HPC service being built by Australia’s DownUnder GeoSolutions (DUG) outside Houston is set to become the largest oil and gas cloud in the world th Read more…

By Tiffany Trader

Intel Extends FPGA Ecosystem with 10nm Agilex

April 11, 2019

The insatiable appetite for higher throughput and lower latency – particularly where edge analytics and AI, network functions, or for a range of datacenter ac Read more…

By Doug Black

UC Berkeley Paper Heralds Rise of Serverless Computing in the Cloud – Do You Agree?

February 13, 2019

Almost exactly ten years to the day from publishing of their widely-read, seminal paper on cloud computing, UC Berkeley researchers have issued another ambitious examination of cloud computing - Cloud Programming Simplified: A Berkeley View on Serverless Computing. The new work heralds the rise of ‘serverless computing’ as the next dominant phase of cloud computing. Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This