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!

Advancing Modular Supercomputing with DEEP and DEEP-ER Architectures

February 24, 2017

Knowing that the jump to exascale will require novel architectural approaches capable of delivering dramatic efficiency and performance gains, researchers around the world are hard at work on next-generation HPC systems. Read more…

By Sean Thielen

Weekly Twitter Roundup (Feb. 23, 2017)

February 23, 2017

Here at HPCwire, we aim to keep the HPC community apprised of the most relevant and interesting news items that get tweeted throughout the week. Read more…

By Thomas Ayres

HPE Server Shows Low Latency on STAC-N1 Test

February 22, 2017

The performance of trade and match servers can be a critical differentiator for financial trading houses. Read more…

By John Russell

HPC Financial Update (Feb. 2017)

February 22, 2017

In this recurring feature, we’ll provide you with financial highlights from companies in the HPC industry. Check back in regularly for an updated list with the most pertinent fiscal information. Read more…

By Thomas Ayres

HPE Extreme Performance Solutions

O&G Companies Create Value with High Performance Remote Visualization

Today’s oil and gas (O&G) companies are striving to process datasets that have become not only tremendously large, but extremely complex. And the larger that data becomes, the harder it is to move and analyze it – particularly with a workforce that could be distributed between drilling sites, offshore rigs, and remote offices. Read more…

Rethinking HPC Platforms for ‘Second Gen’ Applications

February 22, 2017

Just what constitutes HPC and how best to support it is a keen topic currently. Read more…

By John Russell

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

IDC: Will the Real Exascale Race Please Stand Up?

February 21, 2017

So the exascale race is on. And lots of organizations are in the pack. Government announcements from the US, China, India, Japan, and the EU indicate that they are working hard to make it happen – some sooner, some later. Read more…

By Bob Sorensen, IDC

ExxonMobil, NCSA, Cray Scale Reservoir Simulation to 700,000+ Processors

February 17, 2017

In a scaling breakthrough for oil and gas discovery, ExxonMobil geoscientists report they have harnessed the power of 717,000 processors – the equivalent of 22,000 32-processor computers – to run complex oil and gas reservoir simulation models. Read more…

By Doug Black

Advancing Modular Supercomputing with DEEP and DEEP-ER Architectures

February 24, 2017

Knowing that the jump to exascale will require novel architectural approaches capable of delivering dramatic efficiency and performance gains, researchers around the world are hard at work on next-generation HPC systems. Read more…

By Sean Thielen

HPC Technique Propels Deep Learning at Scale

February 21, 2017

Researchers from Baidu’s Silicon Valley AI Lab (SVAIL) have adapted a well-known HPC communication technique to boost the speed and scale of their neural network training and now they are sharing their implementation with the larger deep learning community. Read more…

By Tiffany Trader

IDC: Will the Real Exascale Race Please Stand Up?

February 21, 2017

So the exascale race is on. And lots of organizations are in the pack. Government announcements from the US, China, India, Japan, and the EU indicate that they are working hard to make it happen – some sooner, some later. Read more…

By Bob Sorensen, IDC

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

Drug Developers Use Google Cloud HPC in the Fight Against ALS

February 16, 2017

Within the haystack of a lethal disease such as ALS (amyotrophic lateral sclerosis / Lou Gehrig’s Disease) there exists, somewhere, the needle that will pierce this therapy-resistant affliction. Read more…

By Doug Black

Azure Edges AWS in Linpack Benchmark Study

February 15, 2017

The “when will clouds be ready for HPC” question has ebbed and flowed for years. Read more…

By John Russell

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

For IBM/OpenPOWER: Success in 2017 = (Volume) Sales

January 11, 2017

To a large degree IBM and the OpenPOWER Foundation have done what they said they would – assembling a substantial and growing ecosystem and bringing Power-based products to market, all in about three years. Read more…

By John Russell

US, China Vie for Supercomputing Supremacy

November 14, 2016

The 48th edition of the TOP500 list is fresh off the presses and while there is no new number one system, as previously teased by China, there are a number of notable entrants from the US and around the world and significant trends to report on. Read more…

By Tiffany Trader

Lighting up Aurora: Behind the Scenes at the Creation of the DOE’s Upcoming 200 Petaflops Supercomputer

December 1, 2016

In April 2015, U.S. Department of Energy Undersecretary Franklin Orr announced that Intel would be the prime contractor for Aurora: Read more…

By Jan Rowell

D-Wave SC16 Update: What’s Bo Ewald Saying These Days

November 18, 2016

Tucked in a back section of the SC16 exhibit hall, quantum computing pioneer D-Wave has been talking up its new 2000-qubit processor announced in September. Forget for a moment the criticism sometimes aimed at D-Wave. This small Canadian company has sold several machines including, for example, ones to Lockheed and NASA, and has worked with Google on mapping machine learning problems to quantum computing. In July Los Alamos National Laboratory took possession of a 1000-quibit D-Wave 2X system that LANL ordered a year ago around the time of SC15. Read more…

By John Russell

Enlisting Deep Learning in the War on Cancer

December 7, 2016

Sometime in Q2 2017 the first ‘results’ of the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) will become publicly available according to Rick Stevens. He leads one of three JDACS4C pilot projects pressing deep learning (DL) into service in the War on Cancer. Read more…

By John Russell

IBM Wants to be “Red Hat” of Deep Learning

January 26, 2017

IBM today announced the addition of TensorFlow and Chainer deep learning frameworks to its PowerAI suite of deep learning tools, which already includes popular offerings such as Caffe, Theano, and Torch. Read more…

By John Russell

HPC Startup Advances Auto-Parallelization’s Promise

January 23, 2017

The shift from single core to multicore hardware has made finding parallelism in codes more important than ever, but that hasn’t made the task of parallel programming any easier. Read more…

By Tiffany Trader

Tokyo Tech’s TSUBAME3.0 Will Be First HPE-SGI Super

February 16, 2017

In a press event Friday afternoon local time in Japan, Tokyo Institute of Technology (Tokyo Tech) announced its plans for the TSUBAME3.0 supercomputer, which will be Japan’s “fastest AI supercomputer,” Read more…

By Tiffany Trader

Leading Solution Providers

CPU Benchmarking: Haswell Versus POWER8

June 2, 2015

With OpenPOWER activity ramping up and IBM’s prominent role in the upcoming DOE machines Summit and Sierra, it’s a good time to look at how the IBM POWER CPU stacks up against the x86 Xeon Haswell CPU from Intel. Read more…

By Tiffany Trader

Nvidia Sees Bright Future for AI Supercomputing

November 23, 2016

Graphics chipmaker Nvidia made a strong showing at SC16 in Salt Lake City last week. Read more…

By Tiffany Trader

BioTeam’s Berman Charts 2017 HPC Trends in Life Sciences

January 4, 2017

Twenty years ago high performance computing was nearly absent from life sciences. Today it’s used throughout life sciences and biomedical research. Genomics and the data deluge from modern lab instruments are the main drivers, but so is the longer-term desire to perform predictive simulation in support of Precision Medicine (PM). There’s even a specialized life sciences supercomputer, ‘Anton’ from D.E. Shaw Research, and the Pittsburgh Supercomputing Center is standing up its second Anton 2 and actively soliciting project proposals. There’s a lot going on. Read more…

By John Russell

TSUBAME3.0 Points to Future HPE Pascal-NVLink-OPA Server

February 17, 2017

Since our initial coverage of the TSUBAME3.0 supercomputer yesterday, more details have come to light on this innovative project. Of particular interest is a new board design for NVLink-equipped Pascal P100 GPUs that will create another entrant to the space currently occupied by Nvidia's DGX-1 system, IBM's "Minsky" platform and the Supermicro SuperServer (1028GQ-TXR). Read more…

By Tiffany Trader

IDG to Be Bought by Chinese Investors; IDC to Spin Out HPC Group

January 19, 2017

US-based publishing and investment firm International Data Group, Inc. (IDG) will be acquired by a pair of Chinese investors, China Oceanwide Holdings Group Co., Ltd. Read more…

By Tiffany Trader

Dell Knights Landing Machine Sets New STAC Records

November 2, 2016

The Securities Technology Analysis Center, commonly known as STAC, has released a new report characterizing the performance of the Knight Landing-based Dell PowerEdge C6320p server on the STAC-A2 benchmarking suite, widely used by the financial services industry to test and evaluate computing platforms. The Dell machine has set new records for both the baseline Greeks benchmark and the large Greeks benchmark. Read more…

By Tiffany Trader

Is Liquid Cooling Ready to Go Mainstream?

February 13, 2017

Lost in the frenzy of SC16 was a substantial rise in the number of vendors showing server oriented liquid cooling technologies. Three decades ago liquid cooling was pretty much the exclusive realm of the Cray-2 and IBM mainframe class products. That’s changing. We are now seeing an emergence of x86 class server products with exotic plumbing technology ranging from Direct-to-Chip to servers and storage completely immersed in a dielectric fluid. Read more…

By Steve Campbell

What Knights Landing Is Not

June 18, 2016

As we get ready to launch the newest member of the Intel Xeon Phi family, code named Knights Landing, it is natural that there be some questions and potentially some confusion. Read more…

By James Reinders, Intel

  • arrow
  • Click Here for More Headlines
  • arrow
Share This