Meet the Exascale Apps

By Gary Johnson

April 12, 2012

In what will be a three-decade span between gigascale and exascale computing, HPC capability will have increased by a factor of one billion, but the apps that are projected to use this enormous increase in capability look pretty much like the gigascale ones. Are we missing opportunities as we push the apex of HPC higher?

Gigascale to Terascale

In February of 1991, the Office of Science and Technology Policy released the first “Blue Book” supplement to the President’s FY 1992 Budget Request for the new High Performance Computing and Communications Program. It was entitled “Grand Challenges: High Performance Computing and Communications” and contained a listing of the computational science and engineering challenges then seen as drivers for federal expenditures on HPC. Figure 2 from that report is reproduced below.

 Petascale to Exascale

In preparation for the current attempt to secure federal funding for exascale computing, the Department of Energy conducted a series of workshops entitled “Scientific Grand Challenges Workshop Series”. While this series only focused on science and engineering areas of importance to DOE’s mission, that mission is broad enough to view the grand challenges discussed there as typical of the applications areas foreseen as drivers for the move to exascale.

With the use of a bit of poetic license to prevent the reader’s eyes from glazing over, the table below attempts to convey the general character to these early 1990s gigascale to terascale applications and the exascale applications considered for the 2018-2025 timeframe (depending on whose guess about the arrival of exascale computing one chooses).

We see that over a span of 28 to 35 years, depending on how you count, the applications list remains substantially the same. A few of the 90s applications have dropped off the list – either through success or loss of interest. A couple of well-established applications: Nuclear Physics and Nuclear Energy Systems have been added in response to renewed interest in nuclear energy. To be sure, the other areas listed – the ones surviving multiple decades – have grown in complexity and broadened in applicability. What seems to be missing is the addition of any fundamentally new applications.

Over the decades since the publication of that first Blue Book, “apexscale” HPC has grown in capability by a factor of 1,000,000. In another decade, when exascale machines occupy the apex, they will be a factor of 1,000,000,000 more capable than those early 90s machines. Certainly, this enormous increase must present the opportunity to do a few fundamentally new things.

Capability Computing Usage Modes

In general, as HPC grows in capability, it can be used in three distinct ways:

  • Do what we’re currently doing, but faster or cheaper;
  • Undertake the logical extension of what we’re currently doing to use additional computing capabilities; or
  • Use the new and vastly more capable resource to do something we hadn’t seriously considered trying before.

Clearly and justifiably, we are using apexscale HPC in the first two ways. But what about the third? Have we run out of new ideas? Certainly not. But getting new apps on the agenda seems to have been either remarkably hard or of surprisingly little interest.

Exascale Readiness

Whether any new application candidate is, from inception, “exascale ready” seems considerably less important than its potential scalability. We are, after all, living in an age of scalable computing. Observe that many of the gigascale apps of the early 90s have readily survived, and thrived on, the transition to petascale and (soon) exascale. Did we coincidentally choose the complete collection of applications with this sort of potential for scalability back then or could there be others lurking in the wings?

Opportunities

Thinking of what we hadn’t thought of is always difficult and fraught with peril (you don’t know what you don’t know). However, the commercial and open science worlds have provided us with a few possibilities.

Big Data

Although several federally-funded applications areas have well-established needs for data crunching (e.g., high-energy physics, bioinformatics, and national security), the current opportunity in “Big Data” comes from the commercial world. Think: Social Data Analysis, Personal Analytics, Biobank, the Quantified Self, 23andMe, Healthrageous, Integrated Personal Omics, MyLifeBits. These are probably just the tip of the big data iceberg.

IBM has already launched Watson, with (beyond Jeopardy) foci on health care and financial services. Cray and Sandia National Laboratories have started a Supercomputing Institute for Learning and Knowledge Systems. NeuStar and the University of Illinois Urbana-Champaign have created a Big Data Research Facility. The federal government is also getting onboard with its recently announced Big Data Initiative. In fact, it’s interesting to note that the “Blue Book” accompanying the President’s FY 2013 budget request is strongly focused on big data and not the grand challenges of earlier blue books.

So, Big Data is probably a “no brainer” for the new applications category. Some of it may not be exascale yet, but there’s lots of room to grow.

Brain in a Box

This new application candidate has been advocated by Henry Markram at the Swiss Federal Institute of Technology in Lausanne (EFPL). Its official title is the Human Brain Project (HBP).

As described in a recent Nature article, it’s “an effort to build a supercomputer simulation that integrates everything known about the human brain, from the structures of ion channels in neural cell membranes up to mechanisms behind conscious decision-making.” Markram’s precursor Blue Brain Project at EFPL estimates that this is an exascale application (see figure below).

IBM is also a player in the activity, with its cognitive computing project called Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE). This project claims that “By reproducing the structure and architecture of the brain—the way its elements receive sensory input, connect to each other, adapt these connections, and transmit motor output—the SyNAPSE project models computing systems that emulate the brain’s computing efficiency, size and power usage without being programmed.”

Thus, some form of simulation of the complete human brain seems like a keeper for our new applications short list.

Global-scale Systems

Under this heading, a couple of systems immediately come to mind: the global energy system and the global social system. Each seems worthy of a modeling effort.

In this vein, the European Commission has recently funded a “Big Science” pilot project, called FutureICT, “to understand and manage complex, global, socially interactive systems, with a focus on sustainability and resilience.” FutureICT intends to accomplish these goals “by developing new scientific approaches and combining these with the best established methods in areas like multi-scale computer modeling, social supercomputing, large-scale data mining and participatory platforms.” Sounds like there’s potential for an exascale application here.

To the best of our knowledge, there is no current effort to simulate the complete global energy system. However, given the critical nature of energy, from resource discovery and recovery, through transportation of energy materials, to production and distribution of energy, and disposition of by-products, it seems like having one or more full-scale, high- fidelity simulation tools on hand might be a good idea. Perhaps this will be part of the FutureICT project.

The Whole Planet

Thanks to a concerted international effort spanning a couple of decades, we now have some pretty good global climate models. This community effort has also set a shining example for “team science.”

Lately, the climate modeling community has begun using the term “Earth systems science,” as more phenomenology is added to the basic coupled ocean-atmosphere simulations. Laudable and valuable as these efforts may be, they still leave most of the planet out of the models. So, maybe we should model the whole planet.

The opportunity for such a whole planet model is made visible when one looks at the imagery of our Blue Marble. One immediately notices how thin the shell of the atmosphere is in comparison to the dimensions of our planet. The Earth’s volumetric mean radius is 6371 km. Current climate models reach about 30 km above the surface. The deepest point any ocean model needs to reach is about 12 km below the surface. So, our current modeling efforts are focused on a shell that is, at best, about 0.66 percent of the Earth’s radius. This shell represents about 1.96 percent of the Earth’s volume and 0.02 percent of its mass.

Note that the sort of whole planet model proposed here represents an extreme example of a multi-physics, multi-scale problem. The relevant temporal and spatial scales range from sub-millisecond molecular interactions to multi-millennia ice sheet models to million cubic kilometer modeling of the ionosphere.

The advantages of a fully integrated whole planet model are readily apparent and include applications for:

  • Disaster management and mitigation
  • Energy exploitation
  • Minerals exploration and recovery
  • Siting of critical facilities (e.g., nuclear power plants and waste repositories)
  • Understanding the impact of climate change on built infrastructure
  • Understanding the interactions among human, ecological and physical systems

The availability of such models would also serve to advance fundamental scientific understanding of our planet and its dynamics. Furthermore, undertaking to build such models would provide researchers in all of the relevant disciplines with a clear context for thinking about their research activities and how they contribute to the overall planet modeling effort.

Since the earth system models already in development will require trans-petascale computing capabilities, it is clear that exascale capability will be a bare minimum requirement for whole planet models.

The idea of building the sort of top-down whole planet model suggested here has also occurred to others. See, for example, the agenda of the Geneva-based International Centre for Earth Simulation (ICES). Furthermore, no discussion of this topic would be complete without paying homage to the ground-breaking efforts of Japan’s Earth Simulator Center.

Thinking outside the box

Making the case for new applications is a game that anyone can play. Here we have attempted to make the point that there may be worthwhile candidates lurking out there, beyond the view of our current exascale effort and its list of drivers.

If you don’t like these examples, please feel free to critique and improve them. If you have additional applications candidates, please make them known. The more frank and constructive discussion we have on this topic, the better and richer the future of HPC will be.

About the author

Gary M. Johnson is the founder of Computational Science Solutions, LLC, whose mission is to develop, advocate, and implement solutions for the global computational science and engineering community.

Dr. Johnson specializes in management of high performance computing, applied mathematics, and computational science research activities; advocacy, development, and management of high performance computing centers; development of national science and technology policy; and creation of education and research programs in computational engineering and science.

He has worked in Academia, Industry and Government. He has held full professorships at Colorado State University and George Mason University, been a researcher at United Technologies Research Center, and worked for the Department of Defense, NASA, and the Department of Energy.

He is a graduate of the U.S. Air Force Academy; holds advanced degrees from Caltech and the von Karman Institute; and has a Ph.D. in applied sciences from the University of Brussels.

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!

Infographic Highlights Career of Admiral Grace Murray Hopper

December 5, 2016

Dr. Grace Murray Hopper (December 9, 1906 – January 1, 1992) was an early pioneer of computer science and one of the most famous women achievers in a field dominated by men. Read more…

By Staff

Ganthier, Turkel on the Dell EMC Road Ahead

December 5, 2016

Who is Dell EMC and why should you care? Glad you asked is Jim Ganthier’s quick response. Ganthier is SVP for validated solutions and high performance computing for the new (even bigger) technology giant Dell EMC following Dell’s acquisition of EMC in September. In this case, says Ganthier, the blending of the two companies is a 1+1 = 5 proposition. Not bad math if you can pull it off. Read more…

By John Russell

AWS Embraces FPGAs, ‘Elastic’ GPUs

December 2, 2016

A new instance type rolled out this week by Amazon Web Services is based on customizable field programmable gate arrays that promise to strike a balance between performance and cost as emerging workloads create requirements often unmet by general-purpose processors. Read more…

By George Leopold

AWS Launches Massive 100 Petabyte ‘Sneakernet’

December 1, 2016

Amazon Web Services now offers a way to move data into its cloud by the truckload. Read more…

By Tiffany Trader

Weekly Twitter Roundup (Dec. 1, 2016)

December 1, 2016

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

HPC Career Notes (Dec. 2016)

December 1, 2016

In this monthly feature, we’ll keep you up-to-date on the latest career developments for individuals in the high performance computing community. Read more…

By Thomas Ayres

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

IBM and NSF Computing Pioneer Erich Bloch Dies at 91

November 30, 2016

Erich Bloch, a computational pioneer whose competitive zeal and commercial bent helped transform the National Science Foundation while he was its director, died last Friday at age 91. Bloch was a productive force to be reckoned. During his long stint at IBM prior to joining NSF Bloch spearheaded development of the “Stretch” supercomputer and IBM’s phenomenally successful System/360. Read more…

By John Russell

Ganthier, Turkel on the Dell EMC Road Ahead

December 5, 2016

Who is Dell EMC and why should you care? Glad you asked is Jim Ganthier’s quick response. Ganthier is SVP for validated solutions and high performance computing for the new (even bigger) technology giant Dell EMC following Dell’s acquisition of EMC in September. In this case, says Ganthier, the blending of the two companies is a 1+1 = 5 proposition. Not bad math if you can pull it off. Read more…

By John Russell

AWS Launches Massive 100 Petabyte ‘Sneakernet’

December 1, 2016

Amazon Web Services now offers a way to move data into its cloud by the truckload. 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

Seagate-led SAGE Project Delivers Update on Exascale Goals

November 29, 2016

Roughly a year and a half after its launch, the SAGE exascale storage project led by Seagate has delivered a substantive interim report – Data Storage for Extreme Scale. Read more…

By John Russell

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

HPE-SGI to Tackle Exascale and Enterprise Targets

November 22, 2016

At first blush, and maybe second blush too, Hewlett Packard Enterprise’s (HPE) purchase of SGI seems like an unambiguous win-win. SGI’s advanced shared memory technology, its popular UV product line (Hanna), deep vertical market expertise, and services-led go-to-market capability all give HPE a leg up in its drive to remake itself. Bear in mind HPE came into existence just a year ago with the split of Hewlett-Packard. The computer landscape, including HPC, is shifting with still unclear consequences. One wonders who’s next on the deal block following Dell’s recent merger with EMC. Read more…

By John Russell

Intel Details AI Hardware Strategy for Post-GPU Age

November 21, 2016

Last week at SC16, Intel revealed its product roadmap for embedding its processors with key capabilities and attributes needed to take artificial intelligence (AI) to the next level. Read more…

By Alex Woodie

SC Says Farewell to Salt Lake City, See You in Denver

November 18, 2016

After an intense four-day flurry of activity (and a cold snap that brought some actual snow flurries), the SC16 show floor closed yesterday (Thursday) and the always-extensive technical program wound down today. Read more…

By Tiffany Trader

Why 2016 Is the Most Important Year in HPC in Over Two Decades

August 23, 2016

In 1994, two NASA employees connected 16 commodity workstations together using a standard Ethernet LAN and installed open-source message passing software that allowed their number-crunching scientific application to run on the whole “cluster” of machines as if it were a single entity. Read more…

By Vincent Natoli, Stone Ridge Technology

IBM Advances Against x86 with Power9

August 30, 2016

After offering OpenPower Summit attendees a limited preview in April, IBM is unveiling further details of its next-gen CPU, Power9, which the tech mainstay is counting on to regain market share ceded to rival Intel. Read more…

By Tiffany Trader

AWS Beats Azure to K80 General Availability

September 30, 2016

Amazon Web Services has seeded its cloud with Nvidia Tesla K80 GPUs to meet the growing demand for accelerated computing across an increasingly-diverse range of workloads. The P2 instance family is a welcome addition for compute- and data-focused users who were growing frustrated with the performance limitations of Amazon's G2 instances, which are backed by three-year-old Nvidia GRID K520 graphics cards. Read more…

By Tiffany Trader

Think Fast – Is Neuromorphic Computing Set to Leap Forward?

August 15, 2016

Steadily advancing neuromorphic computing technology has created high expectations for this fundamentally different approach to computing. Read more…

By John Russell

The Exascale Computing Project Awards $39.8M to 22 Projects

September 7, 2016

The Department of Energy’s Exascale Computing Project (ECP) hit an important milestone today with the announcement of its first round of funding, moving the nation closer to its goal of reaching capable exascale computing by 2023. Read more…

By Tiffany Trader

HPE Gobbles SGI for Larger Slice of $11B HPC Pie

August 11, 2016

Hewlett Packard Enterprise (HPE) announced today that it will acquire rival HPC server maker SGI for $7.75 per share, or about $275 million, inclusive of cash and debt. The deal ends the seven-year reprieve that kept the SGI banner flying after Rackable Systems purchased the bankrupt Silicon Graphics Inc. for $25 million in 2009 and assumed the SGI brand. Bringing SGI into its fold bolsters HPE's high-performance computing and data analytics capabilities and expands its position... Read more…

By Tiffany Trader

ARM Unveils Scalable Vector Extension for HPC at Hot Chips

August 22, 2016

ARM and Fujitsu today announced a scalable vector extension (SVE) to the ARMv8-A architecture intended to enhance ARM capabilities in HPC workloads. Fujitsu is the lead silicon partner in the effort (so far) and will use ARM with SVE technology in its post K computer, Japan’s next flagship supercomputer planned for the 2020 timeframe. This is an important incremental step for ARM, which seeks to push more aggressively into mainstream and HPC server markets. Read more…

By John Russell

IBM Debuts Power8 Chip with NVLink and Three New Systems

September 8, 2016

Not long after revealing more details about its next-gen Power9 chip due in 2017, IBM today rolled out three new Power8-based Linux servers and a new version of its Power8 chip featuring Nvidia’s NVLink interconnect. Read more…

By John Russell

Leading Solution Providers

Vectors: How the Old Became New Again in Supercomputing

September 26, 2016

Vector instructions, once a powerful performance innovation of supercomputing in the 1970s and 1980s became an obsolete technology in the 1990s. But like the mythical phoenix bird, vector instructions have arisen from the ashes. Here is the history of a technology that went from new to old then back to new. Read more…

By Lynd Stringer

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

Intel Launches Silicon Photonics Chip, Previews Next-Gen Phi for AI

August 18, 2016

At the Intel Developer Forum, held in San Francisco this week, Intel Senior Vice President and General Manager Diane Bryant announced the launch of Intel's Silicon Photonics product line and teased a brand-new Phi product, codenamed "Knights Mill," aimed at machine learning workloads. Read more…

By Tiffany Trader

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

Beyond von Neumann, Neuromorphic Computing Steadily Advances

March 21, 2016

Neuromorphic computing – brain inspired computing – has long been a tantalizing goal. The human brain does with around 20 watts what supercomputers do with megawatts. And power consumption isn’t the only difference. Fundamentally, brains ‘think differently’ than the von Neumann architecture-based computers. While neuromorphic computing progress has been intriguing, it has still not proven very practical. Read more…

By John Russell

Dell EMC Engineers Strategy to Democratize HPC

September 29, 2016

The freshly minted Dell EMC division of Dell Technologies is on a mission to take HPC mainstream with a strategy that hinges on engineered solutions, beginning with a focus on three industry verticals: manufacturing, research and life sciences. "Unlike traditional HPC where everybody bought parts, assembled parts and ran the workloads and did iterative engineering, we want folks to focus on time to innovation and let us worry about the infrastructure," said Jim Ganthier, senior vice president, validated solutions organization at Dell EMC Converged Platforms Solution Division. Read more…

By Tiffany Trader

Container App ‘Singularity’ Eases Scientific Computing

October 20, 2016

HPC container platform Singularity is just six months out from its 1.0 release but already is making inroads across the HPC research landscape. It's in use at Lawrence Berkeley National Laboratory (LBNL), where Singularity founder Gregory Kurtzer has worked in the High Performance Computing Services (HPCS) group for 16 years. Read more…

By Tiffany Trader

Micron, Intel Prepare to Launch 3D XPoint Memory

August 16, 2016

Micron Technology used last week’s Flash Memory Summit to roll out its new line of 3D XPoint memory technology jointly developed with Intel while demonstrating the technology in solid-state drives. Micron claimed its Quantx line delivers PCI Express (PCIe) SSD performance with read latencies at less than 10 microseconds and writes at less than 20 microseconds. Read more…

By George Leopold

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