My Supercomputer is Bigger Than Yours!

By Andrew Jones

June 18, 2013

Once again, China officially has the fastest supercomputer in the world. All the careful preparations of marketing departments throughout the HPC community leading up to ISC were rendered mute, as the usual slurry of ISC’13 “me too” press releases were blown aside by the revelation of Tianhe-2’s 50+ petaFLOPS.

Chinese supercomputing had again sprung from rumors to deliver the harsh reality that the USA was no longer home to the most powerful known supercomputer in the world. China’s new supercomputer is not only faster than the USA’s leading contenders – it is twice as fast. And, to pour salt into the American wounds, Tianhe-2 is not a stunt machine with buckets of cheap FLOPS lashed together with just enough wet string to run Linpack. It is a custom designed supercomputer combining next generation Chinese interconnect technology with American CPUs and HPC coprocessors.

The USA’s HPC community seems unsure whether to hide under the duvet and try to reassure themselves of American HPC leadership by quoting other metrics, or to seize upon this opportunity to demonstrate to their government masters how other nations are aggressively pursuing supercomputing and thus focus their efforts on securing funding for exascale and other future HPC needs. Meanwhile, the European HPC community enjoys a hint of smugness that the USA’s leadership has been taken away, smothered by an uncomfortable knowledge that such a feat is unlikely to ever be achieved by Europe.

Having a more powerful supercomputer is not merely useful for “mine is bigger than yours” contests – a more powerful supercomputer can deliver more science and engineering in a given time than a smaller system through sheer capacity. It can enable major advances in science and engineering through capability – exploring the leading edge of what is possible with modeling and simulation at scale. It can inspire a generation of users to pursue computational science and engineering. It can inspire a computing technology industry and wider commercial applications of HPC.

Indeed, a more powerful supercomputer is so important that nearly everyone who has a supercomputer tries to find criteria such that theirs is the leading system in a given category, whether “fastest commercial system”, “biggest academically owned system”, or whatever.

Yes, size matters.

But – what if size did not matter? Pretend that all supercomputers were the same size and couldn’t be made bigger. Or perhaps they were all so big and cheap that any user could get as much resource as they needed with zero wait.

In this obtuse reality, the size of the supercomputer no longer correlates to the capacity or capability of science that can be achieved.

What would matter? Other parts of the ecosystem would become the enablers of computational leadership, to produce the leading edge science and engineering, and the resulting economic benefits. Software, people, applications, etc. would become the differentiators.

The researchers who could lead the way in a given computational field would no longer be the ones who had access to the biggest machine, but the ones who could make best use of the same machine size as everyone else. That might mean the most scalable code, or the fastest code for a given problem size, or the most robust and accurate code. It might mean the group who had the best skills strategy to ensure continued development of the computational skills within their group.

Companies could not secure a competitive advantage through computing capacity – they would have to seek better algorithms (scalable, accurate, validated, …) and better investment in the people (developers and users) who could turn computational applications into business results.

How might today’s familiar international competitiveness arguments change in this weird world? There would be no point urging governments to fund development of technology (our pretend world assumes hardware can never be the differentiator). Evoking national pride by deploying bigger systems than rival countries would be impossible. The national need to pursue competitiveness could only be serviced by supporting the development of algorithms, computational methods, scalable software engineering, scientific applications, etc. – and above all a pipeline of computationally aware people/skills.

How would a Top500 equivalent work in this world? (Because there would still be a natural human need to measure progress and compare with other computational users.) I have no answer to this – but it is probably a critical question (even in the real world, not just my pretend world) – how to measure supercomputing capability if not by anything directly correlated to size of the machine?

Instead of tracking roadmaps from hardware vendors, technology planning might consist more of algorithm roadmaps, software implementation roadmaps, recruitment & mentoring proposals, etc.

Ultimately, nothing in the ranking of supercomputing players would change – the richest countries and companies would still be the winners as they could invest more strongly in people (basic methods research, software engineering, science applications, etc.). Some countries/companies would “punch above their weight” – those who understood the need to invest in the right things and did so with more commitment than their rivals. (How is that different to the real world?)

Indeed, perhaps that is where my little “size doesn’t matter” experiment leads me – to conclude that the leadership in supercomputing (and thus the benefits to research, innovation, economic impact, etc.) will always belong to those who understand what supercomputing can do, along with how to do it better – and then act on that understanding.

 

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!

Google Launches New Machine Learning Journal

March 22, 2017

On Monday, Google announced plans to launch a new peer review journal and “ecosystem” Read more…

By John Russell

Swiss Researchers Peer Inside Chips with Improved X-Ray Imaging

March 22, 2017

Peering inside semiconductor chips using x-ray imaging isn’t new, but the technique hasn’t been especially good or easy to accomplish. Read more…

By John Russell

LANL Simulation Shows Massive Black Holes Break “Speed Limit”

March 21, 2017

A new computer simulation based on codes developed at Los Alamos National Laboratory is shedding light on how supermassive black holes could have formed in the early universe contrary to most prior models which impose a limit on how fast these massive ‘objects’ can form. Read more…

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

HPE Extreme Performance Solutions

HFT Firms Turn to Co-Location to Gain Competitive Advantage

High-frequency trading (HFT) is a high-speed, high-stakes world where every millisecond matters. Finding ways to execute trades faster than the competition translates directly to greater revenue for firms, brokerages, and exchanges. Read more…

Intel Ships Drives Based on 3-D XPoint Non-volatile Memory

March 20, 2017

Intel Corp. has begun shipping new storage drives based on its 3-D XPoint non-volatile memory technology as it targets data-driven workloads. Read more…

By George Leopold

Researchers Recreate ‘El Reno’ Tornado on Blue Waters Supercomputer

March 16, 2017

The United States experiences more tornadoes than any other country. About 1,200 tornadoes touch down each each year in the U.S. Read more…

By Tiffany Trader

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

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

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

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

US Supercomputing Leaders Tackle the China Question

March 15, 2017

Joint DOE-NSA report responds to the increased global pressures impacting the competitiveness of U.S. supercomputing. Read more…

By Tiffany Trader

New Japanese Supercomputing Project Targets Exascale

March 14, 2017

Another Japanese supercomputing project was revealed this week, this one from emerging supercomputer maker, ExaScaler Inc., and Keio University. The partners are working on an original supercomputer design with exascale aspirations. Read more…

By Tiffany Trader

Nvidia Debuts HGX-1 for Cloud; Announces Fujitsu AI Deal

March 9, 2017

On Monday Nvidia announced a major deal with Fujitsu to help build an AI supercomputer for RIKEN using 24 DGX-1 servers. Read more…

By John Russell

HPC4Mfg Advances State-of-the-Art for American Manufacturing

March 9, 2017

Last Friday (March 3, 2017), the High Performance Computing for Manufacturing (HPC4Mfg) program held an industry engagement day workshop in San Diego, bringing together members of the US manufacturing community, national laboratories and universities to discuss the role of high-performance computing as an innovation engine for American manufacturing. Read more…

By Tiffany Trader

AMD Expands Exascale Vision at IEEE HPC Symposium

March 7, 2017

With the race towards exascale heating up – for example, the Exascale Computing Program PathForward awards are expected soon – AMD delivered more details of its exascale vision at last month’s 23rd IEEE Symposium on High Performance Computer Architecture. The chipmaker presented an “Exascale Node Architecture (ENA) as the primary building block for exascale machine” including descriptions of component, interconnect, and packaging strategy along with simulation benchmarks to bolster its case. Read more…

By John Russell

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

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

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

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

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

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

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

Leading Solution Providers

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

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

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

US Supercomputing Leaders Tackle the China Question

March 15, 2017

Joint DOE-NSA report responds to the increased global pressures impacting the competitiveness of U.S. supercomputing. Read more…

By Tiffany Trader

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

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