Blue Waters Opts Out of TOP500

By Tiffany Trader

November 16, 2012

The NCSA Blue Waters system is one of the fastest supercomputers in the world, but it won’t be appearing on the TOP500 list – nor will it be taking part in the HPC Challenge (HPCC) awards. While it’s generally understood that there are an unknown number of classified and commercial systems that don’t show up on the list, this is the first time an open science system has opted out in such a fashion.

According to the folks at the National Center for Supercomputing Applications (NCSA), there’s a good reason for this. In the days leading up to the 24th annual Supercomputing Conference (SC12) in Salt Lake City, HPCwire spoke with Blue Waters Project Director Bill Kramer to find out what went into this decision.

HPCwire: How long has Blue Waters been up and running? Would there have been enough time to run Linpack benchmark and submit to the TOP500 list?

Bill Kramer: Oh sure, and we would have had good results if we had chosen to run it. We even had an early science system that was a resource in the US academic world going back to January last year, and we chose not to submit that for the June list.

The system has been up and running full-scale applications in test mode and debugging and scaling platforms and so on from mid-summer on, and particularly since Linpack is such a simple test and does not require I/O, we had plenty of time to run the test.

In fact we have run the test across the entire system and the HPCC test as well, so this was a very conscious decision not to do it – it does not reflect any problems or issues.

HPCwire: Did you get the results you would have expected and are you going to release them?

Kramer: We don’t see any reason to publicize it, but there were requirements in the contract. These tests obtained very good results, but we’d rather exercise the system with real applications. For example, there are some full-scale science codes that have run over 25,000 nodes for multiple days, and they’re actually doing a science problem as opposed to a trivial problem.

We’d much rather use real applications with all the I/O and everything else in there to vet the system and accomplish a real result along the way and those are at least as stressful on the system as Linpack would be because they exercise all parts of the system not just the floating point units. Our focus is reflecting what the real scientists do not a very small subset of what some teams do.

HPCwire: So the contract with Cray did specify Linpack?

Kramer: HPCC was specified [editor’s note: HPCC includes Linpack], and that was one of hundreds of points – all of the others are much more relevant tests. For historical purposes, that was in there from the original NSF release, so we are meeting that, but it’s not relevant to whether the system is a quality system for sustained performance.

HPCwire: Are you releasing the HPCC results?

Kramer: No, and for the same reason. It’s better, but still doesn’t really reflect what to expect for real sustained performance for real applications. It’s better because it has multiple categories, but HPCC still lacks anything that has to do what to do with I/O, which is one of the major bottlenecks, so testing interconnect and testing memory performance.

Our challenge is not with Linpack as a benchmark and not with having a list, our concern is using a very simplified benchmark that has value in its own right, but not for the purpose of indicating usefulness of the system, or productivity of the system or effectiveness of the system.

HPCwire: How and when was the decision arrived at?

Kramer: Our entire project focus has been on sustained petascale performance, and it’s not one-dimensional, it’s not peak performance, it’s not Linpack performance – it’s performance for sustained real-world applications. If you go back to the original NSF solicitation, they encapsulated that into a set of six applications that they projected far forward to the challenging scientific problems that required this type of system and they set their metric to solving that problem within a certain amount of wall-clock time.

Going back to the very beginning, the philosophical nature of how this project came to be was all about delivering effective petascale computing. The investment strategy was to have a very large amount of memory, a very large amount of storage rather than trying to obtain a high single metric.

As we progressed, we have with National Science Foundation and many reviews developed a much more meaningful metric from our point of view called the Sustained Petascale Performance (SPP) test. The way we crafted that was by going to the science teams that we know and have been working with on the system and getting their real applications and their real science problems and using those as the measure of performance.

There are 12 application combinations that we are using to establish the performance of the system over a sustained petaflop in addition to the original NSF six applications. So we are actually going back to first principles: what are the scientists trying to do and making sure they’re able to do their required work within a reasonable amount of elapsed time.

The other part of this is enabling a diverse science base. The NSF, computational and data analytics community have a diverse portfolio of science, arguably the most diverse, and that diverse portfolio requires systems that perform well on that wide range of codes.

That’s really what our measures are and that’s what we remain focused on, so the decision to not list it is very consistent with what the project’s been about and what NSF’s goals have been going back to day one. The decision was made well before we needed to do any work to even submit the early system back in last January. It’s been a long–term process; it was made mutually by the university and NSF as being the right thing to do for the real goals of our project, and we’re very comfortable with it.

Next >>

HPCwire: Do you think we need a ranking system?

Kramer: I think lists are good, and I think as a focused, purposed benchmark, Linpack is good. I think the TOP500 list, though, combines those two things in a way that was interesting at some point, a while ago, but that now in some ways may be doing detriment to the community.

I have no trouble with lists and I think actually the community needs some idea of how we’re progressing, but we really need to be clear on what these lists mean, so for example, for much of the high-level systems on TOP500, what really determines how high they are is how much money is spent, not how well they perform on real applications.

There have been systems that never really get out to perform on real applications, but are on the list. There are ways to submit systems well before they are able to run many scientific or engineering applications. The historical nature of the list is perturbed by those other attributes and maybe those are what the lists measure. I can say for sure it doesn’t measure the progress in real sustained performance because there’s a severe disconnect between what the list says and what real sustained performance measures indicate.

HPCwire: Do we need something new or could we improve our current metrics to your satisfaction?

Kramer: I think there are ways to improve on relevance under the Linpack measurement. The people who put together the original list and maintain the list also talk about these things. Everybody’s afraid to take the first step. In the hallways everybody talks about the issues and the risks for misinterpretation for people who are not in our community, but then everyone says, “but I have to do it.”

Well we’re fortunate enough that we don’t have to do it, and we’re talking the first step by saying this is enough, we need to go to do something else. We are committed to working with others in the community to come up with a better way to describe how effective supercomputing is for solving unsolvable problems and that’s really the important thing.

HPCwire: If the benchmarks are very complex or we have too many of them, is that practical for a wide range of systems?

Kramer: Yes, I’m convinced it is. The NAS parallel benchmarks were very effective in their time. I’m not saying that they’re the right ones now, but in their time period, for a decade or so… There were eight tests that everybody ran. They were pseudo-applications; they didn’t have I/O in them for example, and I/O was less of a challenge in those days, but they gave you a much better picture of what you could expect out of systems.

Other benchmark suites that have between 8–12 tests are being used. The DoD has a pretty good suite that represents a reasonable workload. NERSC has a good persistence suite that has evolved over time, but I think there are enough proofs of existence that yes, you can have a much more dynamic set of things. HPCC might be a place to go leverage with those codes, but that’s also still difficult to figure how it translates into real world applications and how much you can get out of that.

If you look at the graph of real measured performance, say with the NERSC suite of codes, and look at that through 15 years of history and you look at the TOP500 lists, you see that there’s a strong disconnect between what really is achievable with systems and what the list says.

The list also correlates with the amount of funding available to pay for things. The challenges that bottleneck real performance are not being addressed. So I think yes, you can craft those processes in a tractable amount of time that is portable and expandable and that’s been done several different ways.

Next >>

HPCwire: Who are you directing this statement at? What outcome are you hoping for?

Kramer: Blue Waters is a leader in the community in many different ways, and this was another way we felt we could lead to get a more explicit dialogue going in the community about whether this is the way we want to use our metric for say exascale computing and whether this is still relevant.

HPCwire: What about push-back, both in general and your vendors, Cray and NVIDIA?

Kramer: We’ve been very clear with all of our partners and others who may have been partners, that spending tremendous effort to get a number on a list is not indicative of what’s really important to the project is not our priority so we’ve been very open with the partners and they have no objection to this.

HPCwire: In an article on the NCSA website, you write that “the TOP500 list and its associated Linpack have multiple serious problems,” and you’ve covered some of those already, would you like to highlight the ones you feel are most problematic?

Kramer: The main concerns are that it does not give an indication of value and particularly it doesn’t give an indication of value for sustained performance. Value is really the potential of a system to do work divided by its cost, so you can’t tell anything about the value; all you can tell is if you spend a lot of money on a system, you can get up high on the list.

Blue Waters is a project that is spending a significant amount of money, but it’s going into a very balanced system, not one that could have high FLOPS rates. I can tell you that if we had put all our money into peak performance and Linpack, we would have been number one on the list, for sure, for awhile.

If I had not done the investment in the world’s largest memory or the world’s most intense storage system, and just said I want to have the most number of peak FLOPS that directly translate into Linpack FLOPS that directly translates to this number and I don’t care about how hard it is for the science community to make use of those and how many science projects get disenfranchised because they’re not able to use GPUs at scale for a while, then we easily could have been on the top of the list for a number of cycles.

But that’s not our mission. It’s not what we designed our system for and it’s not what many people design their systems for. It could have led to a very poor choice for the real mission by paying attention to where the position is on the TOP500 list.

There are other aspects: the fact that you spend an awful lot of effort on getting something to work that you use once and throw away essentially all that effort. Some places have had to spend multiple weeks or months trying to get a number instead of doing science and engineering.

The improvements that we’re going to make to these SPP codes are actually improvements that go back to the science teams, so it’s a permanent improvement rather than a lot of that effort just going into a test case. It’s not a good way of allocating resources because you can’t reuse those resources.

HPCwire: Why now?

Kramer: The algorithmic space, the application space has changed dramatically from when the major implementation issues were dense linear algebra. There are many more things that are at least as important if not more important now in the way that systems are designed and what we’re trying to deal with.

Many methods have gone to sparse rather than dense, for example. As an indicator of what is really important in a system – we’re saying it’s time to relook at that and it’s not in the mission of our project to continue in that mode.

Last year at Supercomputing, there was a theme of sustained-performance and there were many parties that took part in this discussion. There were panel sessions and papers, etc. and this year, we hope we’ll be able to start the dialogue about how we do a better job of metrics that we can easily explain, but are much more much more meaningful for the real missions of our HPC systems.

Maybe by SC13 there’s a way to report back to the community – a better way that parts of the community, or hopefully the whole community, can say … after 20 years of doing it this way it’s time to do something different.

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!

Weekly Twitter Roundup (Jan. 12, 2017)

January 12, 2017

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

NSF Seeks Input on Cyberinfrastructure Advances Needed

January 12, 2017

In cased you missed it, the National Science Foundation posted a “Dear Colleague Letter” (DCL) late last week seeking input on needs for the next generation of cyberinfrastructure to support science and engineering. Read more…

By John Russell

NSF Approves Bridges Phase 2 Upgrade for Broader Research Use

January 12, 2017

The recently completed phase 2 upgrade of the Bridges supercomputer at the Pittsburgh Supercomputing Center (PSC) has been approved by the National Science Foundation (NSF) making it now available for research allocations to the national scientific community, according to an announcement posted this week on the XSEDE web site. Read more…

By John Russell

Clemson Software Optimizes Big Data Transfers

January 11, 2017

Data-intensive science is not a new phenomenon as the high-energy physics and astrophysics communities can certainly attest, but today more and more scientists are facing steep data and throughput challenges fueled by soaring data volumes and the demands of global-scale collaboration. Read more…

By Tiffany Trader

HPE Extreme Performance Solutions

Remote Visualization: An Integral Technology for Upstream Oil & Gas

As the exploration and production (E&P) of natural resources evolves into an even more complex and vital task, visualization technology has become integral for the upstream oil and gas industry. Read more…

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

UberCloud Cites Progress in HPC Cloud Computing

January 10, 2017

200 HPC cloud experiments, 80 case studies, and a ton of hands-on experience gained, that’s the harvest of four years of UberCloud HPC Experiments. Read more…

By Wolfgang Gentzsch and Burak Yenier

A Conversation with Women in HPC Director Toni Collis

January 6, 2017

In this SC16 video interview, HPCwire Managing Editor Tiffany Trader sits down with Toni Collis, the director and founder of the Women in HPC (WHPC) network, to discuss the strides made since the organization’s debut in 2014. Read more…

By Tiffany Trader

FPGA-Based Genome Processor Bundles Storage

January 6, 2017

Bio-processor developer Edico Genome is collaborating with storage specialist Dell EMC to bundle computing and storage for analyzing gene-sequencing data. Read more…

By George Leopold

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

UberCloud Cites Progress in HPC Cloud Computing

January 10, 2017

200 HPC cloud experiments, 80 case studies, and a ton of hands-on experience gained, that’s the harvest of four years of UberCloud HPC Experiments. Read more…

By Wolfgang Gentzsch and Burak Yenier

A Conversation with Women in HPC Director Toni Collis

January 6, 2017

In this SC16 video interview, HPCwire Managing Editor Tiffany Trader sits down with Toni Collis, the director and founder of the Women in HPC (WHPC) network, to discuss the strides made since the organization’s debut in 2014. 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

Fast Rewind: 2016 Was a Wild Ride for HPC

December 23, 2016

Some years quietly sneak by – 2016 not so much. It’s safe to say there are always forces reshaping the HPC landscape but this year’s bunch seemed like a noisy lot. Among the noisemakers: TaihuLight, DGX-1/Pascal, Dell EMC & HPE-SGI et al., KNL to market, OPA-IB chest thumping, Fujitsu-ARM, new U.S. President-elect, BREXIT, JR’s Intel Exit, Exascale (whatever that means now), NCSA@30, whither NSCI, Deep Learning mania, HPC identity crisis…You get the picture. Read more…

By John Russell

AWI Uses New Cray Cluster for Earth Sciences and Bioinformatics

December 22, 2016

The Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI), headquartered in Bremerhaven, Germany, is one of the country's premier research institutes within the Helmholtz Association of German Research Centres, and is an internationally respected center of expertise for polar and marine research. In November 2015, AWI awarded Cray a contract to install a cluster supercomputer that would help the institute accelerate time to discovery. Now the effort is starting to pay off. Read more…

By Linda Barney

Addison Snell: The ‘Wild West’ of HPC Disaggregation

December 16, 2016

We caught up with Addison Snell, CEO of HPC industry watcher Intersect360, at SC16 last month, and Snell had his expected, extensive list of insights into trends driving advanced-scale technology in both the commercial and research sectors. Read more…

By Doug Black

KNUPATH Hermosa-based Commercial Boards Expected in Q1 2017

December 15, 2016

Last June tech start-up KnuEdge emerged from stealth mode to begin spreading the word about its new processor and fabric technology that’s been roughly a decade in the making. Read more…

By John Russell

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

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

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

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

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

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

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

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

Leading Solution Providers

D-Wave SC16 Update: What’s Bo Ewald Saying These Days

November 18, 2016

Tucked in a back section of the SC16 exhibit hall, quantum computing pioneer D-Wave has been talking up its new 2000-qubit processor announced in September. Forget for a moment the criticism sometimes aimed at D-Wave. This small Canadian company has sold several machines including, for example, ones to Lockheed and NASA, and has worked with Google on mapping machine learning problems to quantum computing. In July Los Alamos National Laboratory took possession of a 1000-quibit D-Wave 2X system that LANL ordered a year ago around the time of SC15. Read more…

By John Russell

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

New Genomics Pipeline Combines AWS, Local HPC, and Supercomputing

September 22, 2016

Declining DNA sequencing costs and the rush to do whole genome sequencing (WGS) of large cohort populations – think 5000 subjects now, but many more thousands soon – presents a formidable computational challenge to researchers attempting to make sense of large cohort datasets. Read more…

By John Russell

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

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

Dell Knights Landing Machine Sets New STAC Records

November 2, 2016

The Securities Technology Analysis Center, commonly known as STAC, has released a new report characterizing the performance of the Knight Landing-based Dell PowerEdge C6320p server on the STAC-A2 benchmarking suite, widely used by the financial services industry to test and evaluate computing platforms. The Dell machine has set new records for both the baseline Greeks benchmark and the large Greeks benchmark. Read more…

By Tiffany Trader

Deep Learning Paves Way for Better Diagnostics

September 19, 2016

Stanford researchers are leveraging GPU-based machines in the Amazon EC2 cloud to run deep learning workloads with the goal of improving diagnostics for a chronic eye disease, called diabetic retinopathy. The disease is a complication of diabetes that can lead to blindness if blood sugar is poorly controlled. It affects about 45 percent of diabetics and 100 million people worldwide, many in developing nations. Read more…

By Tiffany Trader

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