Python Snakes Its Way Into HPC

By Nicole Hemsoth

November 17, 2010

Interpreted programming languages usually don’t find too many friends in high performance computing. Yet Python, one of the most popular general-purpose interpreted languages, has garnered a small community of enthusiastic followers. True believers got the opportunity to hear about the language in the HPC realm in a tutorial session on Monday and a BoF session on Wednesday. Argonne National Lab’s William Scullin, who participated in both events, talked with HPCwire about the status of Python in this space and what developers might look forward to.

HPCwire: Python is not a language normally associated with high performance or scientific computing. What does it have to offer this user community not being fulfilled by traditional languages, like C, Fortran or other high productivity, interpreted languages like MATLAB?

William Scullin: In a way, Python’s growing adoption in the high performance and scientific computing space is a homecoming. Guido Van Rossum originally began Python as a way of providing an administrative scripting language for the Amoeba distributed operating system. Then as now, it combines simple, easy to learn and maintain syntax with access to the same powerful libraries and function calls you would find in any C or Fortran implementation. While there has always been an emphasis on reducing the time it takes to perform a computation, Python has truly shined in improving scientific computing by taking the work out of programming and reducing the time to solution.

Often, projects fail when they try to be all things to all people. MATLAB, Mathematica, SPSS, and Maple are all very useful tools, in part because they are focused on meeting the needs of a well funded community with very specific goals. Python, arising from a very diverse community that ranges from astrophysicists to game programmers to web designers to entry level computer science students, has been very successful due to the diversity of users. The standard library has become amazingly extensive without becoming inconsistent.

Likewise, the amount of software that has come out of the community is amazing, most of which is open source, and the vast majority of which follows the same coding guidelines as the core modules. This makes it possible to easily develop an interface to an embedded microcontroller to turn off the desk lamp when your simulation finally ends and automatically push results to a web server in less than an hour — or alternately turn on a coffee pot and resubmit your job when the simulation fails — all in one language.

HPCwire: Obviously, performance is a driving issue in HPC. How is the issue of execution performance being addressed?

Scullin: Performance is a matter of perspective. A favorite maxim in the Python community is that the greatest performance improvement comes from going to the working from the non-working state. A second maxim, from Knuth, is that premature optimization is the root of all evil. While the execution speed of a Python application may not be as fast as one written in C, C++, or Fortran, its ease of use and low learning curve sharply improves overall time to solution. It’s a question of developer time versus compute time.

Side stepping the issue, it’s ridiculously easy to extend Python with modules written in C, C++, and Fortran. It’s common in our community to utilize compiled high performance numerical kernels, then use Python to handle areas like I/O, workflow management, computational analysis, and steering. When areas become performance bottlenecks, those areas tend to be rewritten in C.

Conversely, I’ve seen C and Fortran projects where code complexity has prevented maintenance and functionality, leading to thousands of lines of compiled code being replaced with less than a hundred lines of Python. In many ways, Python is coming to fulfill the roles that frameworks like Cactus and Samurai sought to fill at the start of the decade — letting scientists worry about their problems while letting the language and interpreter do the heavy lifting.

HPCwire: Do you think a compiled implementation of Python would be a step in the right direction?

Scullin: There will always be a place for the interpreted reference implementation, especially in development, but if a Python compiler comes along that provides better performance without compromising the language, I can’t see it finding much resistance.

That said, there are currently projects such as Unladen Swallow, PyPy, Stackless Python, Jython, and Iron Python that provide alternatives to the CPython interpreter. Unladen Swallow, backed in part by Google, and PyPy both seek to close the performance gap with compiled languages. Unladen Swallow is particularly exciting as it’s backended into the Low Level Virtual Machine, which is the basis for multiple compilers including Clang, currently the default compiler under Apple’s OS X. This makes a Python compiler more a matter of when than if.

HPCwire: Can you describe some of the more important Python initiatives — language extensions, libraries, tools, etc. — that are aimed at the HPC domain?

Scullin: I cannot speak highly enough of NumPy, which is almost the Swiss army knife of Python for scientific and high performance computing. It’s been under active development for years now with each release providing better performance, automatic integration of popular high performance libraries like BLAS and LAPACK, more features, and greater portability. NumPy is further extended by SciPy, which provides additional tools and lab kits addressing almost every science domain.

Likewise, I think very highly of mpi4py, PyMPI, PyCUDA and its sister PyOpenCL, petsc4py, and PyTrilinos. All of these keep improving the options we have to accelerate our code using the very same tools and interfaces that are available through traditional compiled languages with none of the complexity.

HPCwire: Are there vendors out there with commercially-supported solutions?

Scullin: Indeed, and more importantly, most of them are active contributors to and supporters of the Python community. I can no longer count the number of consulting firms that provide Python solutions. It’s also been very encouraging seeing vendors add Python support to their products. Two companies well known in the HPC space, Rogue Wave and ParaTools, have both been very responsive.

Rogue Wave has provided access to their mathematical libraries, IMSL via PyIMSL. Furthermore, they have brought a number of people into the Python community via PyIMSL Studio which they market officially as a prototyping tool. I’ve encountered PyIMSL studio users so happy with their prototype Python applications with PyIMSL Studio, that they ran with the Python code as production code. I should also mention that while the TotalView debugger is not officially a Python tool, it’s seen a lot of use by Python HPC users and it will be interesting to see where it goes since Rogue Wave’s acquisition of Acumen.

ParaTools, a major contributor to the TAU Performance System and a leading consultant in the area of parallel and high performance codes has done a very good job of adding Python support to TAU.

Without hesitation, I have used their tools with C, Fortran, and Python and found their support to be helpful and responsive regardless of language.

While not directly in the HPC market, Enthought, deserves special mention. They host an array of Python projects with engineering and science applications. They provide a commercial packaging of the Python interpreter with commonly used libraries and utilities along with technical support as the Enthought Python Distribution. Most of all, they are active developers of NumPy and SciPy. Without their support and involvement, I am not sure that NumPy would have come together as nicely as it has.

While relatively new, I’ll also be interested to see what the future holds for MBA Sciences’s SPM.Python toolkit for bringing parallelism into serial Python programs. I’ll be keeping a close watch on PiCloud, a firm which provides an amazingly easy to user cloud computing platform that makes running Python codes on a compute cloud ridiculously easy. PiCloud users have their computations offloaded without any serious code changes, having to be involved in any aspect of setting up a cloud infrastructure, or doing any server management. They’ve seriously made it as simple as coding and running.

Finally, though it hasn’t been making a lot of noise lately, NVIDIA has been putting effort behind Copperhead, which while not a complete Python, allows for the rapid development of CUDA kernels in Python-like code.

HPCwire: Do you think most uses of Python in HPC will eventually involve either integration with C or Fortran or source code translation to those languages?

Scullin: I believe that HPC users will continue to choose the best possible tool to address a need in a given situation. Python is flexible enough that there will be continued integration with C, Fortran, and other languages. At the same time, interpreter performance is being rapidly addressed, which makes the issues that come with language translation into C and Fortran cause that sort of project to be less attractive to active Python developers. What will be interesting to watch is how codes written in a mix of C, C++, Fortran, Python and other languages perform and evolve as the LLVM platform continues to mature.

HPCwire: Can you point to any successful case studies or projects where Python has been employed in this arena?

Scullin: At Argonne, we are involved in the development of GPAW, a density-functional theory Python code based on the projector-augmented wave method. Originating out of an international collaboration, it is mostly a mix of C and Python with the vast majority of the code being Python. It has been run at scale successfully and routinely on our Blue Gene platform. While the porting of any application to platforms like the Cray XT series or the Blue Gene is an interesting exercise in computer science, it’s far more remarkable that the performance has been on par from what I’ve seen in C or C++ codes. Moreover, it is being used to produce reliable data used to generate publications.

The other community that a lot of people think of when looking for successful Python applications in the HPC space is bioinformatics. While I’ve not been involved with many bioinformatics codes, the last four or five years have seen a rising number of chemists and biologists appearing on Python-related mailing lists and at conferences discussing how they have been using Python to power their science. While Perl still holds sway in the field, Python is quickly becoming almost as popular.

HPCwire: For those HPC developers interested in learning more about what’s available in the Python ecosystem, can you point to some resources they could tap into?

Scullin: Depending on their particular interests, one of the best places to start is by visiting www.scipy.org. From there, you can find links to numerous mailing lists, information about conferences, code recipes, documentation, and much more. In the Chicago and Bay Areas there are very active Python users groups with sizable memberships with an interest in HPC and scientific computing. Finally, given Python’s ease of use, one of the best things you can do is to spend an afternoon with the interpreter, simply playing with code and seeing what the language can do for you without any effort. The joy of doing powerful things with simple code is one of the most admirable traits of the language.

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

Intel and Trump Announce $7B for Fab 42 Targeting 7nm

February 8, 2017

In what may be an attempt by President Trump to reset his turbulent relationship with the high tech industry, he and Intel CEO Brian Krzanich today announced plans to invest more than $7 billion to complete Fab 42. Read more…

By John Russell

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