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!

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

Nvidia P100 Shows 1.3-2.3x Speedup Over K80 GPU on Financial Apps

April 20, 2017

When it comes to the true performance of the latest silicon, every end user knows that the best processor is the one that works best for their application. 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…

Quantum Adds Global Smarts to StorNext File System

April 20, 2017

Companies that use Quantum’s StorNext platform to store massive amounts of data this week got a glimpse of new storage capabilities that should make it easier to access their data horde from anywhere in the world. Read more…

By Alex Woodie

Scaling an HPC Career in Nepal Can Be a Steep Climb

April 20, 2017

Umesh Upadhyaya works as an IT Associate at the International Centre for Integrated Mountain Development (ICIMOD) in Nepal, which supports the country’s one and only HPC facility. He is directly involved in an initiative that focuses on climate change and atmosphere modeling Read more…

By Nages Sieslack

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

Intel Open Sources All Lustre Work, Brent Gorda Exits

April 19, 2017

In a letter to the Lustre community posted on the Intel website, Vice President of Intel's Data Center Group Trish Damkroger writes that effective immediately the company will be contributing all Lustre development to the open source community. Damkroger also announced that Brent Gorda, General Manager, High Performance Data Division at Intel is leaving the company. 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

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

Penguin Takes a Run at the Big Cloud Providers

April 12, 2017

HPC specialist Penguin Computing recently re-ran benchmarks from a study of its larger brethren and says the results show its ‘public cloud’ – Penguin on Demand (POD) – is among the leaders in cost and performance. Read more…

By John Russell

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

HPC and the Colocation Datacenter – a Bridge Too Far?

April 7, 2017

A more standardised HPC platform approach is making the running of HPC projects within increasing financial reach. Read more…

By Clive Longbottom, Quocirca

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

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

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

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

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

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

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

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

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