Researchers Squeeze GPU Performance from 11 Big Science Apps

By Michael Feldman

July 18, 2012

The GPGPU faithful received another round of encouraging news this week. In a report  published this week, researchers documented that GPU-equipped supercomputers enabled application speedups between 1.4x and 6.1x across a range of well-known science codes. While those results aren’t the order of magnitude performance increases that were being bandied about in the early days of GPU computing, the researchers were encouraged that the technology is producing consistently good results with some of the most popular HPC science applications in the world.

The work was presented in March at the Accelerating Computational Science Symposium, an event devoted to understanding the use of hybrid supercomputers for scientific research. The ensuing report published by the Oak Ridge Leadership Computing Facility, detailed the performance GPU acceleration across the science application spectrum — biology, chemical physics, combustion, nuclear fission and fusion, material science, seismology, molecular dynamics, and climatology.

The 11 simulation codes tested –  S3D, Denovo, LAMMPS, WL-LSMS, CAM-SE, NAMD, Chroma, QMCPACK, SPECFEM-3D, GTC, and CP2K — are used by tens of thousands of researchers worldwide. NAMD alone has over 50 thousand users.

It should be noted that all of the principle participants at the symposium, including Oak Ridge National Laboratory (ORNL), the National Center for Supercomputing Applications (NCSA) and the Swiss National Supercomputing Center (CSCS), not to mention symposium sponsors Cray and NVIDIA, have a stake in proving the viability of GPU-accelerated supercomputing. The three supercomputing centers recently made substantial investments in GPU-based HPC, ORNL with its upcoming 20-plus-petaflop Titan system, NCSA with the 10-petaflop Blue Waters supercomputer, and CSCS with its currently installed 176-node Todi machine.

Titan, Blue Waters and Todi are all Cray supercomputers with varying amounts of AMD Opteron and NVIDIA Tesla horsepower, although none with greater than a 1:1 GPU-to-CPU ratio. That assumes a certain balance in the application between the sequential pieces of the code that would best be run on the CPU and the parallel components that would be candidates for the GPU. But applications can have very different needs in this regard, so that hardware ratio may not always be optimal. Vendors such as HP, Dell, Appro and others offer systems with much higher ratios of GPU to CPUs.

To level the playing field as much as possible, the performance runs for the science apps were made on CSCS’s Monte Rosa, a Cray XE6 machine equipped with two AMD “Interlagos” (Opteron 6200) CPUs per node, and TitanDev, a XK6 Titan-based testbed that consists of hybrid nodes, each of which contain one NVIDIA Fermi GPU and one Interlagos CPU . So in essence, the applications were tested on the same two systems, one of which replaced the second CPU with a GPU in each node. Here are the results:

Application

Performance

XK6 vs XE6

Software Framework

S3D

Turbulent combustion

1.4 OpenACC

NAMD

Molecular dynamics

1.4 CUDA

CP2K

Chemical physics

1.5  CUDA

CAM-SE

Community atmosphere model

1.5 PGI CUDA Fortran

WL-LSMS

Statistical mechanics of magnetic materials

1.6  CUDA

GTC/GTC-GPU

Plasma physics for fusion energy

 1.6  CUDA

 SPECFEM-3D

Seismology

 2.5  CUDA

 QMCPACK

Electronic structure of materials

 3.0  CUDA

 LAMMPS

Molecular dynamics

 3.2  CUDA

 Denovo

3D neutron transport for nuclear reactors

 3.3  CUDA

 Chroma

Lattice quantum chromodynamics

 6.1  CUDA

According to this, the Fermi GPU-equipped XK6 was able to extract between 140 and 610 percent of the application performance compared to the CPU-only XE6. As CSCS director Thomas Schulthess observed at the symposium, that takes into account the fact the Interlagos Opteron is a new x86 processor, while Fermi is a two-year-old design. The implication is that the upcoming Kepler K20 GPU, which is supposed to be available later this year (and which will be deployed in Titan and Blue Waters), should widen the CPU-GPU performance gap even more.

“It’s going to be interesting to see in the next few years if there’s going to be a small avalanche, or is a big avalanche coming that’s really going to revolutionize computational science.” said Schulthess.

Even though the researchers provided an apples-to-apples comparison from a hardware perspective, the application software implementation for the two architectures is, by definition, rather different. Although the report did not delve too deeply into the software frameworks, most of these GPU codes incorporated CUDA or CUDA-based libraries. Only two of the applications, CAM-SE and S3D, used a higher level programming approach: PGI’s CUDA Fortran compiler for CAM-SE and OpenACC directives (compiler unknown) for the S3D implementation. Neither of these did particularly well, relative to the performance increases for the other applications, but there are not enough examples here to make any generalizations.

The other thing to keep in mind is that is no guarantee that the code implementations for either the CPU-only or hybrid versions are optimal at extracting the maximum performance from the silicon. A Fermi-class Tesla M2090 module delivers 665 gigaflops of peak performance, which is about 5 or 6 times that of a high-end Opteron 6200. The only code that appeared to fully exploit the performance advantage of the GPU was Chroma, the code for high energy and nuclear physics. Since applications vary significantly in their potential to utilize a highly threaded architecture like a GPU, this should come as no surprise.

Another aspect that needs to be taken into account is power usage. Although the performance comparison between the two processors is a useful one, if codes can scale equally well on a CPU as a GPU, performance per watt becomes a more valid criteria. Since these GPU accelerators consume about twice the power of a high-end x86 under full load, that means each hybrid node uses 50 percent more power than the corresponding CPU-only one when those systems are running at peak.

That suggests that the GPU-accelerated version of these codes should probably run at least 1.5 times as fast in this configuration to keep performance per watt in line. (Note that half of these codes are clustered around that break-even point.) To be fair, that’s not precisely true, since when the graphics engine is not being fully utilized it won’t be drawing anything near its maximum wattage; in general the GPU is much more efficient at throughput computing than its CPU brethren. But the fact remains that the power-performance behavior of the codes needs to be factored in when you’re considering the advantages of GPU acceleration.

Another missing piece of this comparison is how well these same applications would run on NVIDIA’s HPC competition, namely Intel’s Xeon Phi (aka MIC) coprocessor and its very different software ecosystem. Of course, there is no Xeon Phi yet, so that comparison can’t yet be made. But by this time next year, teraflop-capable MIC and Kepler chips should be in crunching away at applications on production machines. At that point, the case for accelerated science codes could be even more compelling.

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!

PRACEdays Reflects Europe’s HPC Commitment

May 25, 2017

More than 250 attendees and participants came together for PRACEdays17 in Barcelona last week, part of the European HPC Summit Week 2017, held May 15-19 at t Read more…

By Tiffany Trader

Russian Researchers Claim First Quantum-Safe Blockchain

May 25, 2017

The Russian Quantum Center today announced it has overcome the threat of quantum cryptography by creating the first quantum-safe blockchain, securing cryptocurr Read more…

By Doug Black

Google Debuts TPU v2 and will Add to Google Cloud

May 25, 2017

Not long after stirring attention in the deep learning/AI community by revealing the details of its Tensor Processing Unit (TPU), Google last week announced the Read more…

By John Russell

Nvidia CEO Predicts AI ‘Cambrian Explosion’

May 25, 2017

The processing power and cloud access to developer tools used to train machine-learning models are making artificial intelligence ubiquitous across computing pl Read more…

By George Leopold

HPE Extreme Performance Solutions

Exploring the Three Models of Remote Visualization

The explosion of data and advancement of digital technologies are dramatically changing the way many companies do business. With the help of high performance computing (HPC) solutions and data analytics platforms, manufacturers are developing products faster, healthcare providers are improving patient care, and energy companies are improving planning, exploration, and production. Read more…

PGAS Use will Rise on New H/W Trends, Says Reinders

May 25, 2017

If you have not already tried using PGAS, it is time to consider adding PGAS to the programming techniques you know. Partitioned Global Array Space, commonly kn Read more…

By James Reinders

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

Hedge Funds (with Supercomputing help) Rank First Among Investors

May 22, 2017

In case you didn’t know, The Quants Run Wall Street Now, or so says a headline in today’s Wall Street Journal. Quant-run hedge funds now control the largest Read more…

By John Russell

IBM, D-Wave Report Quantum Computing Advances

May 18, 2017

IBM said this week it has built and tested a pair of quantum computing processors, including a prototype of a commercial version. That progress follows an an Read more…

By George Leopold

PRACEdays Reflects Europe’s HPC Commitment

May 25, 2017

More than 250 attendees and participants came together for PRACEdays17 in Barcelona last week, part of the European HPC Summit Week 2017, held May 15-19 at t Read more…

By Tiffany Trader

PGAS Use will Rise on New H/W Trends, Says Reinders

May 25, 2017

If you have not already tried using PGAS, it is time to consider adding PGAS to the programming techniques you know. Partitioned Global Array Space, commonly kn Read more…

By James Reinders

Exascale Escapes 2018 Budget Axe; Rest of Science Suffers

May 23, 2017

President Trump's proposed $4.1 trillion FY 2018 budget is good for U.S. exascale computing development, but grim for the rest of science and technology spend Read more…

By Tiffany Trader

Cray Offers Supercomputing as a Service, Targets Biotechs First

May 16, 2017

Leading supercomputer vendor Cray and datacenter/cloud provider the Markley Group today announced plans to jointly deliver supercomputing as a service. The init Read more…

By John Russell

HPE’s Memory-centric The Machine Coming into View, Opens ARMs to 3rd-party Developers

May 16, 2017

Announced three years ago, HPE’s The Machine is said to be the largest R&D program in the venerable company’s history, one that could be progressing tow Read more…

By Doug Black

What’s Up with Hyperion as It Transitions From IDC?

May 15, 2017

If you’re wondering what’s happening with Hyperion Research – formerly the IDC HPC group – apparently you are not alone, says Steve Conway, now senior V Read more…

By John Russell

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

HPE Launches Servers, Services, and Collaboration at GTC

May 10, 2017

Hewlett Packard Enterprise (HPE) today launched a new liquid cooled GPU-driven Apollo platform based on SGI ICE architecture, a new collaboration with NVIDIA, a Read more…

By John Russell

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. Just how close real-wo 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 cam Read more…

By John Russell

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 Read more…

By Tiffany Trader

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 a Read more…

By Tiffany Trader

CPU-based Visualization Positions for Exascale Supercomputing

March 16, 2017

Since our first formal product releases of OSPRay and OpenSWR libraries in 2016, CPU-based Software Defined Visualization (SDVis) has achieved wide-spread adopt Read more…

By Jim Jeffers, Principal Engineer and Engineering Leader, Intel

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Last week, Google reported that its custom ASIC Tensor Processing Unit (TPU) was 15-30x faster for inferencing workloads than Nvidia's K80 GPU (see our coverage Read more…

By Tiffany Trader

Nvidia’s Mammoth Volta GPU Aims High for AI, HPC

May 10, 2017

At Nvidia's GPU Technology Conference (GTC17) in San Jose, Calif., this morning, CEO Jensen Huang announced the company's much-anticipated Volta architecture a Read more…

By Tiffany Trader

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 ne Read more…

By Tiffany Trader

Leading Solution Providers

Facebook Open Sources Caffe2; Nvidia, Intel Rush to Optimize

April 18, 2017

From its F8 developer conference in San Jose, Calif., today, Facebook announced Caffe2, a new open-source, cross-platform framework for deep learning. Caffe2 is 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 w Read more…

By Tiffany Trader

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 Read more…

By Steve Campbell

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 Eng Read more…

By Tiffany Trader

US Supercomputing Leaders Tackle the China Question

March 15, 2017

As China continues to prove its supercomputing mettle via the Top500 list and the forward march of its ambitious plans to stand up an exascale machine by 2020, 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 networ Read more…

By Tiffany Trader

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 advance Read more…

By Tiffany Trader

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" process Read more…

By Tiffany Trader

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