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!

ISC 2019 Student Cluster Competition: Meet the Teams!

June 25, 2019

Finally! The videos have been rendered, the statistics compiled, and the story lines set. It’s time to share with you the incredible event that was the ISC 2019 Student Cluster Competition. So what’s a Student Clu Read more…

By Dan Olds

What’s New in HPC Research: Rock Art, Protein Design, Genome Assembly & More

June 25, 2019

In this bimonthly 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

Azure Benchmarks HC-series Across 20,000 cores for HPC

June 25, 2019

Cloud provider Microsoft Azure’s push into HPC continues to gain momentum. In a blog last week, Evan Burness, principal program manager, Azure HPC, announced HC-series Virtual Machine are now available in West US 2 and Read more…

By John Russell

HPE Extreme Performance Solutions

HPE and Intel® Omni-Path Architecture: How to Power a Cloud

Learn how HPE and Intel® Omni-Path Architecture provide critical infrastructure for leading Nordic HPC provider’s HPCFLOW cloud service.

For decades, HPE has been at the forefront of high-performance computing, and we’ve powered some of the fastest and most robust supercomputers in the world. Read more…

IBM Accelerated Insights

Rediscovering the Value of the Past

Some people would like to forget their past, perhaps for good reasons. But for business or research organizations, preserving institutional memory can be the key to thriving in the future. Read more…

MLPerf Expands Toolset; Launches Inferencing Suite

June 24, 2019

MLPerf today launched a benchmark suite for inferencing, v0.5, which joins the MLPerf training suite launched a little over a year ago. The new inferencing benchmark, which has been anticipated, covers models applicable Read more…

By John Russell

ISC 2019 Student Cluster Competition: Meet the Teams!

June 25, 2019

Finally! The videos have been rendered, the statistics compiled, and the story lines set. It’s time to share with you the incredible event that was the ISC 20 Read more…

By Dan Olds

MLPerf Expands Toolset; Launches Inferencing Suite

June 24, 2019

MLPerf today launched a benchmark suite for inferencing, v0.5, which joins the MLPerf training suite launched a little over a year ago. The new inferencing benc Read more…

By John Russell

Is Weather and Climate Prediction the Perfect ‘Pilot’ for Exascale?

June 21, 2019

At ISC 2019 this week, Peter Bauer – deputy director of research for the European Centre for Medium-Range Weather Forecasts (ECMWF) – outlined an ambitious Read more…

By Oliver Peckham

ISC Keynote: Thomas Sterling’s Take on Whither HPC

June 20, 2019

Entertaining, insightful, and unafraid to launch the occasional verbal ICBM, HPC pioneer Thomas Sterling delivered his 16th annual closing keynote at ISC yesterday. He explored, among other things: exascale machinations; quantum’s bubbling money pot; Arm’s new HPC viability; Europe’s... Read more…

By John Russell

IBM Claims No. 1 Commercial Supercomputer with Total Oil & Gas System 

June 20, 2019

IBM can now boast not only the two most powerful supercomputers in the world, it also has claimed the top spot for a supercomputer used in a commercial setting. Read more…

By Staff Report

HPC on Pace for 5-Year 6.8% CAGR; Guess Which Hyperscaler Spent $10B on IT Last Year?

June 20, 2019

In the neck-and-neck horse race for HPC server market share, HPE has hung on to a slim, shrinking lead over Dell EMC – but if server and storage market shares Read more…

By Doug Black

ISC 2019 Research Paper Award Winners Announced

June 19, 2019

At the 2019 International Supercomputing Conference (ISC) in Frankfurt this week, the ISC committee awarded the event's top prizes for outstanding research pape Read more…

By Oliver Peckham

ISC Keynote: The Algorithms of Life – Scientific Computing for Systems Biology

June 19, 2019

Systems biology has existed loosely under many definitions for a couple of decades. It’s the notion of describing living systems using first-principle physics Read more…

By John Russell

High Performance (Potato) Chips

May 5, 2006

In this article, we focus on how Procter & Gamble is using high performance computing to create some common, everyday supermarket products. Tom Lange, a 27-year veteran of the company, tells us how P&G models products, processes and production systems for the betterment of consumer package goods. Read more…

By Michael Feldman

Cray, AMD to Extend DOE’s Exascale Frontier

May 7, 2019

Cray and AMD are coming back to Oak Ridge National Laboratory to partner on the world’s largest and most expensive supercomputer. The Department of Energy’s Read more…

By Tiffany Trader

Graphene Surprises Again, This Time for Quantum Computing

May 8, 2019

Graphene is fascinating stuff with promise for use in a seeming endless number of applications. This month researchers from the University of Vienna and Institu Read more…

By John Russell

Why Nvidia Bought Mellanox: ‘Future Datacenters Will Be…Like High Performance Computers’

March 14, 2019

“Future datacenters of all kinds will be built like high performance computers,” said Nvidia CEO Jensen Huang during a phone briefing on Monday after Nvidia revealed scooping up the high performance networking company Mellanox for $6.9 billion. Read more…

By Tiffany Trader

AMD Verifies Its Largest 7nm Chip Design in Ten Hours

June 5, 2019

AMD announced last week that its engineers had successfully executed the first physical verification of its largest 7nm chip design – in just ten hours. The AMD Radeon Instinct Vega20 – which boasts 13.2 billion transistors – was tested using a TSMC-certified Calibre nmDRC software platform from Mentor. Read more…

By Oliver Peckham

It’s Official: Aurora on Track to Be First US Exascale Computer in 2021

March 18, 2019

The U.S. Department of Energy along with Intel and Cray confirmed today that an Intel/Cray supercomputer, "Aurora," capable of sustained performance of one exaf Read more…

By Tiffany Trader

Deep Learning Competitors Stalk Nvidia

May 14, 2019

There is no shortage of processing architectures emerging to accelerate deep learning workloads, with two more options emerging this week to challenge GPU leader Nvidia. First, Intel researchers claimed a new deep learning record for image classification on the ResNet-50 convolutional neural network. Separately, Israeli AI chip startup Hailo.ai... Read more…

By George Leopold

TSMC and Samsung Moving to 5nm; Whither Moore’s Law?

June 12, 2019

With reports that Taiwan Semiconductor Manufacturing Co. (TMSC) and Samsung are moving quickly to 5nm manufacturing, it’s a good time to again ponder whither goes the venerable Moore’s law. Shrinking feature size has of course been the primary hallmark of achieving Moore’s law... Read more…

By John Russell

Leading Solution Providers

ISC 2019 Virtual Booth Video Tour

CRAY
CRAY
DDN
DDN
DELL EMC
DELL EMC
ONE STOP SYSTEMS
ONE STOP SYSTEMS
PANASAS
PANASAS
VERNE GLOBAL
VERNE GLOBAL

Nvidia Embraces Arm, Declares Intent to Accelerate All CPU Architectures

June 17, 2019

As the Top500 list was being announced at ISC in Frankfurt today with an upgraded petascale Arm supercomputer in the top third of the list, Nvidia announced its Read more…

By Tiffany Trader

The Case Against ‘The Case Against Quantum Computing’

January 9, 2019

It’s not easy to be a physicist. Richard Feynman (basically the Jimi Hendrix of physicists) once said: “The first principle is that you must not fool yourse Read more…

By Ben Criger

Top500 Purely Petaflops; US Maintains Performance Lead

June 17, 2019

With the kick-off of the International Supercomputing Conference (ISC) in Frankfurt this morning, the 53rd Top500 list made its debut, and this one's for petafl Read more…

By Tiffany Trader

Cray – and the Cray Brand – to Be Positioned at Tip of HPE’s HPC Spear

May 22, 2019

More so than with most acquisitions of this kind, HPE’s purchase of Cray for $1.3 billion, announced last week, seems to have elements of that overused, often Read more…

By Doug Black and Tiffany Trader

Intel Launches Cascade Lake Xeons with Up to 56 Cores

April 2, 2019

At Intel's Data-Centric Innovation Day in San Francisco (April 2), the company unveiled its second-generation Xeon Scalable (Cascade Lake) family and debuted it Read more…

By Tiffany Trader

Announcing four new HPC capabilities in Google Cloud Platform

April 15, 2019

When you’re running compute-bound or memory-bound applications for high performance computing or large, data-dependent machine learning training workloads on Read more…

By Wyatt Gorman, HPC Specialist, Google Cloud; Brad Calder, VP of Engineering, Google Cloud; Bart Sano, VP of Platforms, Google Cloud

In Wake of Nvidia-Mellanox: Xilinx to Acquire Solarflare

April 25, 2019

With echoes of Nvidia’s recent acquisition of Mellanox, FPGA maker Xilinx has announced a definitive agreement to acquire Solarflare Communications, provider Read more…

By Doug Black

Nvidia Claims 6000x Speed-Up for Stock Trading Backtest Benchmark

May 13, 2019

A stock trading backtesting algorithm used by hedge funds to simulate trading variants has received a massive, GPU-based performance boost, according to Nvidia, Read more…

By Doug Black

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