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!

Nvidia Rolls Out Certified Server Program Targeting AI Applications

January 26, 2021

Nvidia today launched a certified systems program in which participating vendors can offer Nvidia-certified servers with up to eight A100 GPUs. Separate support contracts directly from Nvidia for the certified systems ar Read more…

By John Russell

XSEDE Supercomputers Square Off Against Ebola

January 26, 2021

COVID-19 may have dominated headlines and occupied much of the world’s scientific computing capacity over the last year, but many researchers continued their work to keep other deadly viruses at bay. One of those, Ebol Read more…

By Oliver Peckham

What’s New in HPC Research: Galaxies, Fugaku, Electron Microscopes & More

January 25, 2021

In this regular feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From parallel programming to exascale to quantum computing, the details are here. Read more…

By Oliver Peckham

Red Hat’s Disruption of CentOS Unleashes Storm of Dissent

January 22, 2021

Five weeks after angering much of the CentOS Linux developer community by unveiling controversial changes to the no-cost CentOS operating system, Red Hat has unveiled alternatives for affected users that give them severa Read more…

By Todd R. Weiss

China Unveils First 7nm Chip: Big Island

January 22, 2021

Shanghai Tianshu Zhaoxin Semiconductor Co. is claiming China’s first 7-nanometer chip, described as a leading-edge, general-purpose cloud computing chip based on a proprietary GPU architecture. Dubbed “Big Island Read more…

By George Leopold

AWS Solution Channel

Fire Dynamics Simulation CFD workflow on AWS

Modeling fires is key for many industries, from the design of new buildings, defining evacuation procedures for trains, planes and ships, and even the spread of wildfires. Read more…

HiPEAC Keynote: In-Memory Computing Steps Closer to Practical Reality

January 21, 2021

Pursuit of in-memory computing has long been an active area with recent progress showing promise. Just how in-memory computing works, how close it is to practical application, and what are some of the key opportunities a Read more…

By John Russell

Nvidia Rolls Out Certified Server Program Targeting AI Applications

January 26, 2021

Nvidia today launched a certified systems program in which participating vendors can offer Nvidia-certified servers with up to eight A100 GPUs. Separate support Read more…

By John Russell

Red Hat’s Disruption of CentOS Unleashes Storm of Dissent

January 22, 2021

Five weeks after angering much of the CentOS Linux developer community by unveiling controversial changes to the no-cost CentOS operating system, Red Hat has un Read more…

By Todd R. Weiss

HiPEAC Keynote: In-Memory Computing Steps Closer to Practical Reality

January 21, 2021

Pursuit of in-memory computing has long been an active area with recent progress showing promise. Just how in-memory computing works, how close it is to practic Read more…

By John Russell

HiPEAC’s Vision for a New Cyber Era, a ‘Continuum of Computing’

January 21, 2021

Earlier this week (Jan. 19), HiPEAC — the European Network on High Performance and Embedded Architecture and Compilation — published the 8th edition of the HiPEAC Vision, detailing an increasingly interconnected computing landscape where complex tasks are carried out across multiple... Read more…

By Tiffany Trader

Saudi Aramco Unveils Dammam 7, Its New Top Ten Supercomputer

January 21, 2021

By revenue, oil and gas giant Saudi Aramco is one of the largest companies in the world, and it has historically employed commensurate amounts of supercomputing Read more…

By Oliver Peckham

President-elect Biden Taps Eric Lander and Deep Team on Science Policy

January 19, 2021

Last Friday U.S. President-elect Joe Biden named The Broad Institute founding director and president Eric Lander as his science advisor and as director of the Office of Science and Technology Policy. Lander, 63, is a mathematician by training and distinguished life sciences... Read more…

By John Russell

Pat Gelsinger Returns to Intel as CEO

January 14, 2021

The Intel board of directors has appointed a new CEO. Intel alum Pat Gelsinger is leaving his post as CEO of VMware to rejoin the company that he parted ways with 11 years ago. Gelsinger will succeed Bob Swan, who will remain CEO until Feb. 15. Gelsinger previously spent 30 years... Read more…

By Tiffany Trader

Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?

January 13, 2021

The rapid adoption of Julia, the open source, high level programing language with roots at MIT, shows no sign of slowing according to data from Julialang.org. I Read more…

By John Russell

Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?

January 13, 2021

The rapid adoption of Julia, the open source, high level programing language with roots at MIT, shows no sign of slowing according to data from Julialang.org. I Read more…

By John Russell

Esperanto Unveils ML Chip with Nearly 1,100 RISC-V Cores

December 8, 2020

At the RISC-V Summit today, Art Swift, CEO of Esperanto Technologies, announced a new, RISC-V based chip aimed at machine learning and containing nearly 1,100 low-power cores based on the open-source RISC-V architecture. Esperanto Technologies, headquartered in... Read more…

By Oliver Peckham

Azure Scaled to Record 86,400 Cores for Molecular Dynamics

November 20, 2020

A new record for HPC scaling on the public cloud has been achieved on Microsoft Azure. Led by Dr. Jer-Ming Chia, the cloud provider partnered with the Beckman I Read more…

By Oliver Peckham

NICS Unleashes ‘Kraken’ Supercomputer

April 4, 2008

A Cray XT4 supercomputer, dubbed Kraken, is scheduled to come online in mid-summer at the National Institute for Computational Sciences (NICS). The soon-to-be petascale system, and the resulting NICS organization, are the result of an NSF Track II award of $65 million to the University of Tennessee and its partners to provide next-generation supercomputing for the nation's science community. Read more…

Is the Nvidia A100 GPU Performance Worth a Hardware Upgrade?

October 16, 2020

Over the last decade, accelerators have seen an increasing rate of adoption in high-performance computing (HPC) platforms, and in the June 2020 Top500 list, eig Read more…

By Hartwig Anzt, Ahmad Abdelfattah and Jack Dongarra

Aurora’s Troubles Move Frontier into Pole Exascale Position

October 1, 2020

Intel’s 7nm node delay has raised questions about the status of the Aurora supercomputer that was scheduled to be stood up at Argonne National Laboratory next year. Aurora was in the running to be the United States’ first exascale supercomputer although it was on a contemporaneous timeline with... Read more…

By Tiffany Trader

10nm, 7nm, 5nm…. Should the Chip Nanometer Metric Be Replaced?

June 1, 2020

The biggest cool factor in server chips is the nanometer. AMD beating Intel to a CPU built on a 7nm process node* – with 5nm and 3nm on the way – has been i Read more…

By Doug Black

Programming the Soon-to-Be World’s Fastest Supercomputer, Frontier

January 5, 2021

What’s it like designing an app for the world’s fastest supercomputer, set to come online in the United States in 2021? The University of Delaware’s Sunita Chandrasekaran is leading an elite international team in just that task. Chandrasekaran, assistant professor of computer and information sciences, recently was named... Read more…

By Tracey Bryant

Leading Solution Providers

Contributors

Top500: Fugaku Keeps Crown, Nvidia’s Selene Climbs to #5

November 16, 2020

With the publication of the 56th Top500 list today from SC20's virtual proceedings, Japan's Fugaku supercomputer – now fully deployed – notches another win, Read more…

By Tiffany Trader

Texas A&M Announces Flagship ‘Grace’ Supercomputer

November 9, 2020

Texas A&M University has announced its next flagship system: Grace. The new supercomputer, named for legendary programming pioneer Grace Hopper, is replacing the Ada system (itself named for mathematician Ada Lovelace) as the primary workhorse for Texas A&M’s High Performance Research Computing (HPRC). Read more…

By Oliver Peckham

At Oak Ridge, ‘End of Life’ Sometimes Isn’t

October 31, 2020

Sometimes, the old dog actually does go live on a farm. HPC systems are often cursed with short lifespans, as they are continually supplanted by the latest and Read more…

By Oliver Peckham

Gordon Bell Special Prize Goes to Massive SARS-CoV-2 Simulations

November 19, 2020

2020 has proven a harrowing year – but it has produced remarkable heroes. To that end, this year, the Association for Computing Machinery (ACM) introduced the Read more…

By Oliver Peckham

Nvidia and EuroHPC Team for Four Supercomputers, Including Massive ‘Leonardo’ System

October 15, 2020

The EuroHPC Joint Undertaking (JU) serves as Europe’s concerted supercomputing play, currently comprising 32 member states and billions of euros in funding. I Read more…

By Oliver Peckham

Intel Xe-HP GPU Deployed for Aurora Exascale Development

November 17, 2020

At SC20, Intel announced that it is making its Xe-HP high performance discrete GPUs available to early access developers. Notably, the new chips have been deplo Read more…

By Tiffany Trader

Nvidia-Arm Deal a Boon for RISC-V?

October 26, 2020

The $40 billion blockbuster acquisition deal that will bring chipmaker Arm into the Nvidia corporate family could provide a boost for the competing RISC-V architecture. As regulators in the U.S., China and the European Union begin scrutinizing the impact of the blockbuster deal on semiconductor industry competition and innovation, the deal has at the very least... Read more…

By George Leopold

HPE, AMD and EuroHPC Partner for Pre-Exascale LUMI Supercomputer

October 21, 2020

Not even a week after Nvidia announced that it would be providing hardware for the first four of the eight planned EuroHPC systems, HPE and AMD are announcing a Read more…

By Oliver Peckham

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