The HPC Triple Crown

By Gary Johnson

November 28, 2012

The semi-annual HPC “500 list” time and its attendant fall iron horse racing season are upon us. Thanks to the hard work of the list keepers, we currently enjoy three major ones to review, compare and contrast: TOP500, Green500 and Graph 500. Each focuses on a distinct aspect of HPC – number crunching, energy efficiency, and data crunching, respectively – and together they allow us to construct our own type of Triple Crown. Since new race results were recently announced, let’s take a look at the current standings.

In racing with real horses, a Triple Crown consists of a series of three races for three-year-old thoroughbred horses. Winning all three of these races is considered to be the greatest accomplishment of a horse’s career. Various nations, where thoroughbred racing is popular, each have their own Triple Crown series. Since HPC is a global sport, we enjoy the simplicity of only having one Triple Crown: our three 500 lists – the TOP, Green and Graph 500s. How do our HPC iron horses do in competing to win this Triple Crown? Let’s review the past year’s races and then look at the Fall 2012 competition.

Last fall, strictly speaking, there was no Triple Crown winner. No single machine occupied the top spot on all three rankings, but Tsubame clearly was the best all-around performer. Of those machines competing in all three races, Tsubame ranked closest to the top overall (see Number Crunching, Data Crunching and Energy Efficiency: the HPC Hat Trick). To refresh your memories, there’s a graphic below depicting the outcome. Also, you may wish to consult the three lists – TOP500, Green500, and Graph 500 – to learn more about the machines behind the nicknames used here.

Note that three of the fall 2011 top five TOP500 computers were missing from the comparison: K Computer (#1), Tianhe-1A (#2), and Nebulae (#4). This was because they were not included in the Graph 500. However, because of the relatively low ranking of those three machines on the Green500, the outcome of the comparison was not affected.

As discussed in the previously cited article, the constraint on this three-way comparison was the population of the Graph 500 list. While there is essentially complete overlap and obvious mapping between TOP500 and Green500 machine entries, for the Graph 500 this is not the case. The Graph 500 list was and is a work in progress. The fall 2011 version contained only 49 distinct computers and did not provide any mapping of these to either the TOP500 or the Green500 list. Nonetheless, it was possible to locate at least 19 Graph 500 computers on the other two lists – the ones named in the graphic above.

It would be nice to have not only a winner of our semi-annual iron horse races, but also a Triple Crown winner as well. Perhaps there are alternative definitions of the competition that would improve the odds of this happening.

Suppose we considered both “individual” and “team” competitions and a couple of “winning” categories within each competition. For example:

Individual Triple Crown

Class A Winner: Specific machine in first place on all three lists

Class B Winner: Specific machine present on all three lists & highest average rank

So, the individual competition looks as one might expect, except we admit the possibility of a Class B winner, based on highest average rank across all three lists. This pretty much ensures an individual winner even if one machine doesn’t glean the top spot on every list. In the fall of 2011, Tsubame would have been the Class B winner.

For the team competition, suppose we looked not for a specific machine (for example, Hopper) to win but instead for a type of machine (such as the Cray XE6)? Then, since the most attention is paid to the top of the lists, we might define “winning” a particular race to be having the greatest number of that machine type in the top 10 on that list. We could also then form aggregate scores for the winners in each list race by summing across all three lists.

Team Triple Crown

Class A Winner: Machine type present in the top 10 on each list & highest aggregate score

Class B Winner: Machine type present in the top 10 on at least one list & highest aggregate score

If we review the fall 2011 500 lists using our new definitions, we find:

Individual Competition

Class A Winner: None

Class B Winner: Tsubame

The input data for the team competition are summarized in the table below. The maximum number of occurrences of a particular machine type in the top 10 for each 500 list is shown in red.

Table1

These data yield:

Team Competition

Class A Winner: Tsubame (a team with one member)

Class B Winner: IBM Blue Gene/Q

If you think that just looking at the top 10 spots on the 500 lists is too restrictive, then use your own cutoff or include the whole of each list. Designing the competition is a game anyone can play. We’ve chosen the top 10 because that part of the 500 lists seems to draw the most attention from the HPC community.

Let’s see how events played out in the summer 2012 races.

Here once again we see that the small population of the Graph 500 constrains the comparisons. To be sure, the list is expanding. The summer 2012 version contained 89 entries, which was almost twice as many as the previous list, but we were still only able to clearly identify 19 of them as being included in the other two lists. Using those 19, the comparisons are shown in the graphic below.

Individual Competition

Class A Winner: None

Class B Winner: Sequoia (by a nose over Mira)

Once again, the input data for the team competition are summarized in the yable below.

Table2

This data yields:

Team Competition

Class A Winner: IBM Blue Gene/Q

Class B Winner: IBM Blue Gene/Q

The IBM Blue Gene/Q is the summer 2012 Class A and Class B Triple Crown team competition winner. Sequoia – an IBM Blue Gene/Q system at Lawrence Livermore National Laboratory – is the Class B Triple Crown individual competition winner and also finished first on both the TOP500 and the Graph 500. Another Blue Gene/Q system beat Sequoia out for the top spot on the Green500. Sequoia finished in 20th place on the Green500, having been beaten by 19 other Blue Gene/Q systems.

Another Blue Gene/Q system – Mira, located at the Argonne National Laboratory – was nosed out by Sequoia, in a photo finish, in the individual competition. So, machines from the IBM Stables dominated the Summer 2012 competitions.

Now, on to the current race results.

In November 2012, the population of the Graph 500 list expanded fairly substantially. This version contains 124 entries, almost 40 percent more than the previous one. While we were only able to clearly identify 41 of them as being included in the other two 500 lists, this is more than twice as many as our previous comparisons contained. For consistency with those previous comparisons, we’ve taken the top 19 of those 41 and compared their rankings on all three lists in the graphic below. We’ve also maintained the same vertical axis scale used previously, for ease in viewing across all three graphics.

Fall 2012 Graph

Individual Competition

Class A Winner: None

Class B Winner: JuQueen

The input data for the team competition are summarized in the table below.

Table3

These data yield:

Team Competition

Class A Winner: IBM Blue Gene/Q

Class B Winner: IBM Blue Gene/Q

The IBM Blue Gene/Q is the fall 2012 Class A and Class B Triple Crown team competition winner. JuQueen – an IBM Blue Gene/Q system at Forschungszentrum Jülich (FZJ) in Germany – is the Class B Triple Crown individual competition winner and thus becomes the first European winner of the Class B individual competition. Also notable is that every system in the Top 10 for this Triple Crown competition is an IBM Blue Gene/Q (JuQueen through Sakura). So, once again, machines from the IBM stables dominated the fall 2012 races.

Where, in all of this, is Titan, you might ask. Well, unfortunately, Titan, a Cray XK7 and the TOP500 race winner, did not compete in the Graph 500.

We’ve summarized the results of all HPC Triple Crown competitions in the table below and highlighted the row containing the new results.

Table4

So far, no machine has won the Class A individual competition and only two machine types have won the Class A team competition – Tsubame and Blue Gene/Q. Here’s a table of those machines which have come closest to winning the Class A individual competition.

Date

Machine

Ranking on “500 List”

Average Ranking

Top500

Green500

Graph500

Fall 2010

Intrepid

13

29

1

14.3

Summer 2011

Hopper

8

42

4

18

Jugene

12

40

2

18

Fall 2011

Tsubame

5

10

3

6

Summer 2012

Sequoia

1

20

1

7.3

Fall 2012

JuQueen

5

5

3

4.3

Will some iron horse win the Class A individual Triple Crown next time, or is placing first on all three 500 lists just too hard? Will the IBM stables continue to dominate or will Titan or some other machine steal some of the glory? Will the Graph 500 continue to grow and include more of the machines on the other two 500 lists? We’ll see what happens next summer.

In real horse racing, winning the Triple Crown accords prestige to horses, their humans and stables; adds economic value to the winners and their offspring; and provides entertainment for racing fans. Our iron horse races serve just about the same purposes. So, HPC racing fans, do your own analysis of the race results, take a look at the iron horses likely to race well next time, get ready to place your bets, and enjoy the upcoming summer 2013 races!

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!

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

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

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

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