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!

ISC18’s Industrial Day Slate: Digital Twins, CFD for Automotive, HPC for SMEs

June 23, 2018

For enterprise IT strategists, this year’s Industrial Day (Tuesday, June 26) at ISC18 in Frankfurt will cover a range of topics – digital twins, AI and machine learning in automotive design, HPC for SME’s and deve Read more…

By Doug Black

What’s Hot and What’s Not at ISC 2018?

June 22, 2018

As the calendar rolls around to late June we see the ISC conference, held in Frankfurt (June 24th-28th), heave into view. With some of the pre-show announcements already starting to roll out, what do we think some of the Read more…

By Dairsie Latimer

Servers in Orbit, HPE Apollos Make 4,500 Trips Around Earth

June 22, 2018

The International Space Station shines a little brighter in the night sky thanks to what amounts to an orbiting supercomputer lofted to the outpost last year as part of a year-long experiment to determine if high-end com Read more…

By George Leopold

HPE Extreme Performance Solutions

HPC and AI Convergence is Accelerating New Levels of Intelligence

Data analytics is the most valuable tool in the digital marketplace – so much so that organizations are employing high performance computing (HPC) capabilities to rapidly collect, share, and analyze endless streams of data. Read more…

IBM Accelerated Insights

Taking the AI Training Wheels Off: From PoC to Production

Even though it seems simple now, there were a lot of skills to master in learning to ride a bike. From balancing on two wheels, and steering in a straight line, to going around corners and stopping before running over the dog, it took lots of practice to master these skills. Read more…

HPCwire Readers’ and Editors’ Choice Awards Turns 15

June 22, 2018

A hallmark of sustainability is this: If you are not serving a need effectively and efficiently you do not last. The HPCwire Readers’ and Editors’ Choice awards program has stood the test of time. Each year, our read Read more…

By Tiffany Trader

What’s Hot and What’s Not at ISC 2018?

June 22, 2018

As the calendar rolls around to late June we see the ISC conference, held in Frankfurt (June 24th-28th), heave into view. With some of the pre-show announcement Read more…

By Dairsie Latimer

Servers in Orbit, HPE Apollos Make 4,500 Trips Around Earth

June 22, 2018

The International Space Station shines a little brighter in the night sky thanks to what amounts to an orbiting supercomputer lofted to the outpost last year as Read more…

By George Leopold

HPCwire Readers’ and Editors’ Choice Awards Turns 15

June 22, 2018

A hallmark of sustainability is this: If you are not serving a need effectively and efficiently you do not last. The HPCwire Readers’ and Editors’ Choice aw Read more…

By Tiffany Trader

ISC 2018 Preview from @hpcnotes

June 21, 2018

Prepare for your social media feed to be saturated with #HPC, #ISC18, #Top500, etc. Prepare for your mainstream media to talk about supercomputers (in between t Read more…

By Andrew Jones

AMD’s EPYC Road to Redemption in Six Slides

June 21, 2018

A year ago AMD returned to the server market with its EPYC processor line. The earth didn’t tremble but folks took notice. People remember the Opteron fondly Read more…

By John Russell

European HPC Summit Week and PRACEdays 2018: Slaying Dragons and SHAPEing Futures One SME at a Time

June 20, 2018

The University of Ljubljana in Slovenia hosted the third annual EHPCSW18 and fifth annual PRACEdays18 events which opened May 29, 2018. The conference was chair Read more…

By Elizabeth Leake (STEM-Trek for HPCwire)

Cray Introduces All Flash Lustre Storage Solution Targeting HPC

June 19, 2018

Citing the rise of IOPS-intensive workflows and more affordable flash technology, Cray today introduced the L300F, a scalable all-flash storage solution whose p Read more…

By John Russell

Sandia to Take Delivery of World’s Largest Arm System

June 18, 2018

While the enterprise remains circumspect on prospects for Arm servers in the datacenter, the leadership HPC community is taking a bolder, brighter view of the x86 server CPU alternative. Amongst current and planned Arm HPC installations – i.e., the innovative Mont-Blanc project, led by Bull/Atos, the 'Isambard’ Cray XC50 going into the University of Bristol, and commitments from both Japan and France among others -- HPE is announcing that it will be supply the United States National Nuclear Security Administration (NNSA) with a 2.3 petaflops peak Arm-based system, named Astra. Read more…

By Tiffany Trader

MLPerf – Will New Machine Learning Benchmark Help Propel AI Forward?

May 2, 2018

Let the AI benchmarking wars begin. Today, a diverse group from academia and industry – Google, Baidu, Intel, AMD, Harvard, and Stanford among them – releas Read more…

By John Russell

How the Cloud Is Falling Short for HPC

March 15, 2018

The last couple of years have seen cloud computing gradually build some legitimacy within the HPC world, but still the HPC industry lies far behind enterprise I Read more…

By Chris Downing

US Plans $1.8 Billion Spend on DOE Exascale Supercomputing

April 11, 2018

On Monday, the United States Department of Energy announced its intention to procure up to three exascale supercomputers at a cost of up to $1.8 billion with th Read more…

By Tiffany Trader

Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale

March 19, 2018

Petaflop per second deep learning training performance on the NERSC (National Energy Research Scientific Computing Center) Cori supercomputer has given climate Read more…

By Rob Farber

ORNL Summit Supercomputer Is Officially Here

June 8, 2018

Oak Ridge National Laboratory (ORNL) together with IBM and Nvidia celebrated the official unveiling of the Department of Energy (DOE) Summit supercomputer toda Read more…

By Tiffany Trader

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Hennessy & Patterson: A New Golden Age for Computer Architecture

April 17, 2018

On Monday June 4, 2018, 2017 A.M. Turing Award Winners John L. Hennessy and David A. Patterson will deliver the Turing Lecture at the 45th International Sympo Read more…

By Staff

Google Chases Quantum Supremacy with 72-Qubit Processor

March 7, 2018

Google pulled ahead of the pack this week in the race toward "quantum supremacy," with the introduction of a new 72-qubit quantum processor called Bristlecone. Read more…

By Tiffany Trader

Leading Solution Providers

SC17 Booth Video Tours Playlist

Altair @ SC17

Altair

AMD @ SC17

AMD

ASRock Rack @ SC17

ASRock Rack

CEJN @ SC17

CEJN

DDN Storage @ SC17

DDN Storage

Huawei @ SC17

Huawei

IBM @ SC17

IBM

IBM Power Systems @ SC17

IBM Power Systems

Intel @ SC17

Intel

Lenovo @ SC17

Lenovo

Mellanox Technologies @ SC17

Mellanox Technologies

Microsoft @ SC17

Microsoft

Penguin Computing @ SC17

Penguin Computing

Pure Storage @ SC17

Pure Storage

Supericro @ SC17

Supericro

Tyan @ SC17

Tyan

Univa @ SC17

Univa

Google I/O 2018: AI Everywhere; TPU 3.0 Delivers 100+ Petaflops but Requires Liquid Cooling

May 9, 2018

All things AI dominated discussion at yesterday’s opening of Google’s I/O 2018 developers meeting covering much of Google's near-term product roadmap. The e Read more…

By John Russell

Pattern Computer – Startup Claims Breakthrough in ‘Pattern Discovery’ Technology

May 23, 2018

If it weren’t for the heavy-hitter technology team behind start-up Pattern Computer, which emerged from stealth today in a live-streamed event from San Franci Read more…

By John Russell

Nvidia Ups Hardware Game with 16-GPU DGX-2 Server and 18-Port NVSwitch

March 27, 2018

Nvidia unveiled a raft of new products from its annual technology conference in San Jose today, and despite not offering up a new chip architecture, there were still a few surprises in store for HPC hardware aficionados. Read more…

By Tiffany Trader

Sandia to Take Delivery of World’s Largest Arm System

June 18, 2018

While the enterprise remains circumspect on prospects for Arm servers in the datacenter, the leadership HPC community is taking a bolder, brighter view of the x86 server CPU alternative. Amongst current and planned Arm HPC installations – i.e., the innovative Mont-Blanc project, led by Bull/Atos, the 'Isambard’ Cray XC50 going into the University of Bristol, and commitments from both Japan and France among others -- HPE is announcing that it will be supply the United States National Nuclear Security Administration (NNSA) with a 2.3 petaflops peak Arm-based system, named Astra. Read more…

By Tiffany Trader

AMD’s EPYC Road to Redemption in Six Slides

June 21, 2018

A year ago AMD returned to the server market with its EPYC processor line. The earth didn’t tremble but folks took notice. People remember the Opteron fondly Read more…

By John Russell

Part One: Deep Dive into 2018 Trends in Life Sciences HPC

March 1, 2018

Life sciences is an interesting lens through which to see HPC. It is perhaps not an obvious choice, given life sciences’ relative newness as a heavy user of H Read more…

By John Russell

Intel Pledges First Commercial Nervana Product ‘Spring Crest’ in 2019

May 24, 2018

At its AI developer conference in San Francisco yesterday, Intel embraced a holistic approach to AI and showed off a broad AI portfolio that includes Xeon processors, Movidius technologies, FPGAs and Intel’s Nervana Neural Network Processors (NNPs), based on the technology it acquired in 2016. Read more…

By Tiffany Trader

Google Charts Two-Dimensional Quantum Course

April 26, 2018

Quantum error correction, essential for achieving universal fault-tolerant quantum computation, is one of the main challenges of the quantum computing field and it’s top of mind for Google’s John Martinis. At a presentation last week at the HPC User Forum in Tucson, Martinis, one of the world's foremost experts in quantum computing, emphasized... Read more…

By Tiffany Trader

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