Consolidating HPC’s Gains

By Gary Johnson

August 13, 2013

Despite phenomenal progress in HPC over a sustained period of decades, a few issues limiting its effectiveness and acceptance remain.  Prominent among these are the repeatability, transportability, and openness of HPC applications.  As we prepare to move HPC to the exascale level, we should take the time and effort to consolidate HPC’s gains and deal with these residual issues from the early days of computational science.  Only then will we be ready to reap the benefits of more powerful HPC tools.

HPC Tools

Nearly fifty years ago, in 1964, the first computer generally acknowledged as a supercomputer – the CDC 6600 – was introduced.  At that time, there was no Linpack Benchmark or Top500 List but, by the measures in use then, it was able to sustain a performance level of about 500 Kiloflops.

In 1970, ARPAnet, the progenitor of the Internet came along.  A few years later, in 1973, Ethernet was invented.  In 1985, NSFnet was created and in the early 1990s it morphed into the Internet.  In 1990 the World Wide Web was born and in 1993 it was made visual by the release of the Mosaic web browser.  Also in 1993, the Top500 List was introduced and its top computer was a Thinking Machines CM-5, clocked at just under 60 Gigaflops.

In summary, HPC has existed for at least half a century and, in terms of HPC tools, we’ve had fairly capable supercomputers and networking for about 20 years.

HPC Applications

The concept of computational science came to public light no later than 1989, when our late friend and colleague, Ken Wilson, published his well-known Grand Challenges to Computational Science paper (unfortunately, it’s locked away behind a paywall).  So, both the HPC tools and the computational science concept for HPC applications gelled into something pretty close to their contemporary form a couple of decades ago. 

Originally, computational science was met with a fair amount of skepticism.  It was seen by some as just a collection of stunts, producing little more than pretty pictures – not the real stuff of science.  It was seen as lacking the rigor necessary to be on par with theory and experiment.  Computational science results were often criticized as one-off demos of unproven concepts. 

So, how effectively and convincingly have we been using HPC?

Repeatability, Transportability, Openness

Both theory and experiment share a few key attributes:

Repeatability (Recomputability)

 A result obtained once can be repeated arbitrarily many times, given the same assumptions (for a theory) or conditions (for an experiment).

Transportability (Reuse)

Results are not dependent on any particular theorist, experimentalist or specific apparatus.  They are transportable to other people and places – transcending any particular instance.

Openness

Results are open.  Theorists publish their theories and the corresponding proofs (if possible) or conjectures.  Experimentalists describe the conditions of their experiments and the details of their equipment and procedures.  These steps are taken to ensure the credibility of results by enabling their repeatability and transportability. 

HPC applications, as science, should also share these attributes – in order to rise above the early criticisms of computational science, and to be effective and convincing.

Current Status

Twenty years into the “modern era” of HPC applications, how are we doing?  Clearly, we’ve made our applications bigger and more complex.  Through improvements in the speed of both algorithms and hardware, our applications execute faster.  The concepts of Verification and Validation (V&V) and Uncertainty Quantification (UQ) for scientific codes have taken root – but perhaps not yet fully blossomed in general HPC practice. 

However, despite the laudable efforts of many of our HPC colleagues to solidify the standing of our field, significant issues with repeatability, transportability, and openness remain.  Here are a few recent developments:

Repeatability (Recomputability)

Ian Gent, Professor of Computer Science at the University of St Andrews, has recently published something he calls The Recomputation Manifesto.  It is described in a post of his at the Software Sustainability Institute.  The Manifesto contains six points (emphasis mine):

  1. Computational experiments should be recomputable for all time
  2. Recomputation of recomputable experiments should be very easy
  3. It should be easier to make experiments recomputable than not to
  4. Tools and repositories can help recomputation become standard
  5. The only way to ensure recomputability is to provide virtual machines
  6. Runtime performance is a secondary issue

The Manifesto is based on Gent’s views that:

The current state of experimental reproducibility in computer science is lamentable. The result is inevitable: experimental results enter the literature which are just wrong. I don’t mean that the results don’t generalise. I mean that an algorithm which was claimed to do something just does not do that thing: for example, if the original implementation was bugged and was in fact a different algorithm. I suspect this problem is common, and I know for certain that it has happened. Here’s an example from my own research area, discovered by my friend and tenacious pursuer of replication Patrick Prosser.

The full text of the Manifesto is available on arXiv.  Suffice it to say that Professor Gent’s concerns are well founded and extend beyond computer science to include HPC applications. 

Transportability (Reuse)

A group of investigators from Korea and the US have recently published a paper entitled An Evaluation of the Software System Dependency of a Global Atmospheric Model.  The abstract reads as follows (emphasis mine):

This study presents the dependency of the simulation results from a global atmospheric numerical model on machines with different hardware and software systems. The global model program (GMP) of the Global/Regional Integrated Model system (GRIMs) is tested on 10 different computer systems having different central processing unit (CPU) architectures or compilers. There exist differences in the results for different compilers, parallel libraries, and optimization levels, primarily due to the treatment of rounding errors by the different software systems. The system dependency, which is the standard deviation of the 500-hPa geopotential height averaged over the globe, increases with time. However, its fractional tendency, which is the change of the standard deviation relative to the value itself, remains nearly zero with time. In a seasonal prediction framework, the ensemble spread due to the differences in software system is comparable to the ensemble spread due to the differences in initial conditions that is used for the traditional ensemble forecasting.

The full paper is behind an American Meteorological Society paywall.  Based on my interpretation of the abstract, transportability (or reuse) is a non-trivial issue for this HPC application.  My guess is that this is not an isolated case.

Openness

A group of nine astrophysicists recently published a paper in arXiv entitled Practices in source code sharing in astrophysics.  In it, they write (emphasis mine):

While software and algorithms have become increasingly important in astronomy, the majority of authors who publish computational astronomy research do not share the source code they develop, making it difficult to replicate and reuse the work. In this paper we discuss the importance of sharing scientific source code with the entire astrophysics community, and propose that journals require authors to make their code publicly available when a paper is published. That is, we suggest that a paper that involves a computer program not be accepted for publication unless the source code becomes publicly available. The adoption of such a policy by editors, editorial boards, and reviewers will improve the ability to replicate scientific results, and will also make the computational astronomy methods more available to other researchers who wish to apply them to their data.

So, openness clearly also remains an issue for HPC applications. 

Note further that it’s not just the codes and their related parameters that should be publicly available – but also the scientific publications reporting on them.  If you’ve been keeping track, you’ve noted that two papers mentioned in this article are behind paywalls – Ken Wilson’s seminal paper on Grand Challenges to Computational Science (24 years later!) and the recent one on the Global Atmospheric Model (despite its obvious public policy implications).  The good news is that places like arXiv exist and the other publications mentioned here are out in the open.

Consolidating HPC’s Gains

HPC has come a long way.  Our tools have improved greatly.  For example, today’s fastest machine, China’s Tianhe-2, has been clocked at just under 34 Petaflops.  So roughly speaking, HPC performance has improved by a factor of about 600,000 in the past 20 years (and 68 billion in the past 50 years).  Current plans are to have exascale computers in place by the beginning of the next decade.

The rapid pace of improvement in HPC tools and their increasingly broader adoption by industry puts a lot of pressure on HPC applications – and on the financial resources available to support the whole HPC enterprise.  Certainly, HPC applications have grown in scale and become more complex and inclusive of more physical phenomena.  However, arguably, most petascale applications are still done in the old “hero mode” from the early days of computational science.  Most practitioners compute at the terascale – not the petascale – and only limited resources have been made available to help them catch up before the bar is raised to exascale.

So, while we’re working toward exascale HPC tools, perhaps we should consolidate the HPC applications gains we’ve made thus far – so that we’ll be ready to embrace exascale and exploit it fully.  Even if financial resources are scarce, this should be a high priority. 

In addition to bringing more HPC applications – and people – up to the petascale level, we should address the lingering issues of repeatability, transportability, openness discussed above.  If forced to pick one of these three to focus on, openness is probably the key.

If we publish openly and release the related source codes, repeatability and transportability should be solvable problems.  The venues for open publication already exist and are being used by some communities.  To complete this part of openness, just don’t allow your publications to be placed behind paywalls.  There is no good reason that scientific work (probably funded by public money) should be behind paywalls.  Once that bullet has been bitten, source codes must inevitably follow.

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!

TACC Helps ROSIE Bioscience Gateway Expand its Impact

April 26, 2017

Biomolecule structure prediction has long been challenging not least because the relevant software and workflows often require high-end HPC systems that many bioscience researchers lack easy access to. Read more…

By John Russell

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

IBM, Nvidia, Stone Ridge Claim Gas & Oil Simulation Record

April 25, 2017

IBM, Nvidia, and Stone Ridge Technology today reported setting the performance record for a “billion cell” oil and gas reservoir simulation. Read more…

By John Russell

ASC17 Makes Splash at Wuxi Supercomputing Center

April 24, 2017

A record-breaking twenty student teams plus scores of company representatives, media professionals, staff and student volunteers transformed a formerly empty hall inside the Wuxi Supercomputing Center into a bustling hub of HPC activity, kicking off day one of 2017 Asia Student Supercomputer Challenge (ASC17). Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

Remote Visualization Optimizing Life Sciences Operations and Care Delivery

As patients continually demand a better quality of care and increasingly complex workloads challenge healthcare organizations to innovate, investing in the right technologies is key to ensuring growth and success. Read more…

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of a new generation of chips designed specifically for deep learning workloads. Read more…

By Alex Woodie

Musk’s Latest Startup Eyes Brain-Computer Links

April 21, 2017

Elon Musk, the auto and space entrepreneur and severe critic of artificial intelligence, is forming a new venture that reportedly will seek to develop an interface between the human brain and computers. Read more…

By George Leopold

MIT Mathematician Spins Up 220,000-Core Google Compute Cluster

April 21, 2017

On Thursday, Google announced that MIT math professor and computational number theorist Andrew V. Sutherland had set a record for the largest Google Compute Engine (GCE) job. Sutherland ran the massive mathematics workload on 220,000 GCE cores using preemptible virtual machine instances. Read more…

By Tiffany Trader

NERSC Cori Shows the World How Many-Cores for the Masses Works

April 21, 2017

As its mission, the high performance computing center for the U.S. Department of Energy Office of Science, NERSC (the National Energy Research Supercomputer Center), supports a broad spectrum of forefront scientific research across diverse areas that includes climate, material science, chemistry, fusion energy, high-energy physics and many others. Read more…

By Rob Farber

Messina Update: The US Path to Exascale in 16 Slides

April 26, 2017

Paul Messina, director of the U.S. Exascale Computing Project, provided a wide-ranging review of ECP’s evolving plans last week at the HPC User Forum. Read more…

By John Russell

ASC17 Makes Splash at Wuxi Supercomputing Center

April 24, 2017

A record-breaking twenty student teams plus scores of company representatives, media professionals, staff and student volunteers transformed a formerly empty hall inside the Wuxi Supercomputing Center into a bustling hub of HPC activity, kicking off day one of 2017 Asia Student Supercomputer Challenge (ASC17). Read more…

By Tiffany Trader

Groq This: New AI Chips to Give GPUs a Run for Deep Learning Money

April 24, 2017

CPUs and GPUs, move over. Thanks to recent revelations surrounding Google’s new Tensor Processing Unit (TPU), the computing world appears to be on the cusp of a new generation of chips designed specifically for deep learning workloads. Read more…

By Alex Woodie

NERSC Cori Shows the World How Many-Cores for the Masses Works

April 21, 2017

As its mission, the high performance computing center for the U.S. Department of Energy Office of Science, NERSC (the National Energy Research Supercomputer Center), supports a broad spectrum of forefront scientific research across diverse areas that includes climate, material science, chemistry, fusion energy, high-energy physics and many others. Read more…

By Rob Farber

Hyperion (IDC) Paints a Bullish Picture of HPC Future

April 20, 2017

Hyperion Research – formerly IDC’s HPC group – yesterday painted a fascinating and complicated portrait of the HPC community’s health and prospects at the HPC User Forum held in Albuquerque, NM. HPC sales are up and growing ($22 billion, all HPC segments, 2016). Read more…

By John Russell

Knights Landing Processor with Omni-Path Makes Cloud Debut

April 18, 2017

HPC cloud specialist Rescale is partnering with Intel and HPC resource provider R Systems to offer first-ever cloud access to Xeon Phi "Knights Landing" processors. The infrastructure is based on the 68-core Intel Knights Landing processor with integrated Omni-Path fabric (the 7250F Xeon Phi). Read more…

By Tiffany Trader

CERN openlab Explores New CPU/FPGA Processing Solutions

April 14, 2017

Through a CERN openlab project known as the ‘High-Throughput Computing Collaboration,’ researchers are investigating the use of various Intel technologies in data filtering and data acquisition systems. Read more…

By Linda Barney

DOE Supercomputer Achieves Record 45-Qubit Quantum Simulation

April 13, 2017

In order to simulate larger and larger quantum systems and usher in an age of “quantum supremacy,” researchers are stretching the limits of today’s most advanced supercomputers. Read more…

By Tiffany Trader

Google Pulls Back the Covers on Its First Machine Learning Chip

April 6, 2017

This week Google released a report detailing the design and performance characteristics of the Tensor Processing Unit (TPU), its custom ASIC for the inference phase of neural networks (NN). Read more…

By Tiffany Trader

Quantum Bits: D-Wave and VW; Google Quantum Lab; IBM Expands Access

March 21, 2017

For a technology that’s usually characterized as far off and in a distant galaxy, quantum computing has been steadily picking up steam. Read more…

By John Russell

Trump Budget Targets NIH, DOE, and EPA; No Mention of NSF

March 16, 2017

President Trump’s proposed U.S. fiscal 2018 budget issued today sharply cuts science spending while bolstering military spending as he promised during the campaign. Read more…

By John Russell

HPC Compiler Company PathScale Seeks Life Raft

March 23, 2017

HPCwire has learned that HPC compiler company PathScale has fallen on difficult times and is asking the community for help or actively seeking a buyer for its assets. 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

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

In this contributed perspective piece, Intel’s Jim Jeffers makes the case that CPU-based visualization is now widely adopted and as such is no longer a contrarian view, but is rather an exascale requirement. Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

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

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

Leading Solution Providers

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

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

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

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is the successor to Caffe, the deep learning framework developed by Berkeley AI Research and community contributors. 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

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

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

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

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