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!

Nvidia’s Jensen Delivers Vision for the New HPC

November 14, 2018

For nearly two hours on Monday at SC18, Jensen Huang, CEO of Nvidia, presented his expansive view of the future of HPC (and computing in general) as only he can do. Animated. Backstopped by a stream of data charts, produ Read more…

By John Russell

New Panasas High Performance Storage Straddles Commercial-Traditional HPC

November 13, 2018

High performance storage vendor Panasas has launched a new version of its ActiveStor product line this morning featuring what the company said is the industry’s first plug-and-play, portable parallel file system that d Read more…

By Doug Black

SC18 Student Cluster Competition – Revealing the Field

November 13, 2018

It’s November again and we’re almost ready for the kick-off of one of the greatest computer sports events in the world – the SC Student Cluster Competition. This is the twelfth time that teams of university undergr Read more…

By Dan Olds

HPE Extreme Performance Solutions

AI Can Be Scary. But Choosing the Wrong Partners Can Be Mortifying!

As you continue to dive deeper into AI, you will discover it is more than just deep learning. AI is an extremely complex set of machine learning, deep learning, reinforcement, and analytics algorithms with varying compute, storage, memory, and communications needs. Read more…

IBM Accelerated Insights

New Data Management Techniques for Intelligent Simulations

The trend in high performance supercomputer design has evolved – from providing maximum compute capability for complex scalable science applications, to capacity computing utilizing efficient, cost-effective computing power for solving a small number of large problems or a large number of small problems. Read more…

US Leads Supercomputing with #1, #2 Systems & Petascale Arm

November 12, 2018

The 31st Supercomputing Conference (SC) - commemorating 30 years since the first Supercomputing in 1988 - kicked off in Dallas yesterday, taking over the Kay Bailey Hutchison Convention Center and much of the surrounding Read more…

By Tiffany Trader

Nvidia’s Jensen Delivers Vision for the New HPC

November 14, 2018

For nearly two hours on Monday at SC18, Jensen Huang, CEO of Nvidia, presented his expansive view of the future of HPC (and computing in general) as only he can Read more…

By John Russell

New Panasas High Performance Storage Straddles Commercial-Traditional HPC

November 13, 2018

High performance storage vendor Panasas has launched a new version of its ActiveStor product line this morning featuring what the company said is the industry Read more…

By Doug Black

SC18 Student Cluster Competition – Revealing the Field

November 13, 2018

It’s November again and we’re almost ready for the kick-off of one of the greatest computer sports events in the world – the SC Student Cluster Competitio Read more…

By Dan Olds

US Leads Supercomputing with #1, #2 Systems & Petascale Arm

November 12, 2018

The 31st Supercomputing Conference (SC) - commemorating 30 years since the first Supercomputing in 1988 - kicked off in Dallas yesterday, taking over the Kay Ba Read more…

By Tiffany Trader

OpenACC Talks Up Summit and Community Momentum at SC18

November 12, 2018

OpenACC – the directives-based parallel programing model for optimizing applications on heterogeneous architectures – is showcasing user traction and HPC im Read more…

By John Russell

How ASCI Revolutionized the World of High-Performance Computing and Advanced Modeling and Simulation

November 9, 2018

The 1993 Supercomputing Conference was held in Portland, Oregon. That conference and it’s show floor provided a good snapshot of the uncertainty that U.S. supercomputing was facing in the early 1990s. Many of the companies exhibiting that year would soon be gone, either bankrupt or acquired by somebody else. Read more…

By Alex R. Larzelere

At SC18: GM, Boeing, Deere, BP Talk Enterprise HPC Strategies

November 9, 2018

SC18 in Dallas (Nov.11-16) will feature an impressive series of sessions focused on the enterprise HPC deployments at some of the largest industrial companies: Read more…

By Doug Black

SC 30th Anniversary Perennials 1988-2018

November 8, 2018

Many conferences try, fewer succeed. Thirty years ago, no one knew if the first SC would also be the last. Thirty years later, we know it’s the biggest annual Read more…

By Doug Black & Tiffany Trader

Cray Unveils Shasta, Lands NERSC-9 Contract

October 30, 2018

Cray revealed today the details of its next-gen supercomputing architecture, Shasta, selected to be the next flagship system at NERSC. We've known of the code-name "Shasta" since the Argonne slice of the CORAL project was announced in 2015 and although the details of that plan have changed considerably, Cray didn't slow down its timeline for Shasta. Read more…

By Tiffany Trader

TACC Wins Next NSF-funded Major Supercomputer

July 30, 2018

The Texas Advanced Computing Center (TACC) has won the next NSF-funded big supercomputer beating out rivals including the National Center for Supercomputing Ap Read more…

By John Russell

IBM at Hot Chips: What’s Next for Power

August 23, 2018

With processor, memory and networking technologies all racing to fill in for an ailing Moore’s law, the era of the heterogeneous datacenter is well underway, Read more…

By Tiffany Trader

Requiem for a Phi: Knights Landing Discontinued

July 25, 2018

On Monday, Intel made public its end of life strategy for the Knights Landing "KNL" Phi product set. The announcement makes official what has already been wide Read more…

By Tiffany Trader

CERN Project Sees Orders-of-Magnitude Speedup with AI Approach

August 14, 2018

An award-winning effort at CERN has demonstrated potential to significantly change how the physics based modeling and simulation communities view machine learni Read more…

By Rob Farber

House Passes $1.275B National Quantum Initiative

September 17, 2018

Last Thursday the U.S. House of Representatives passed the National Quantum Initiative Act (NQIA) intended to accelerate quantum computing research and developm Read more…

By John Russell

Summit Supercomputer is Already Making its Mark on Science

September 20, 2018

Summit, now the fastest supercomputer in the world, is quickly making its mark in science – five of the six finalists just announced for the prestigious 2018 Read more…

By John Russell

New Deep Learning Algorithm Solves Rubik’s Cube

July 25, 2018

Solving (and attempting to solve) Rubik’s Cube has delighted millions of puzzle lovers since 1974 when the cube was invented by Hungarian sculptor and archite Read more…

By John Russell

Leading Solution Providers

TACC’s ‘Frontera’ Supercomputer Expands Horizon for Extreme-Scale Science

August 29, 2018

The National Science Foundation and the Texas Advanced Computing Center announced today that a new system, called Frontera, will overtake Stampede 2 as the fast Read more…

By Tiffany Trader

US Leads Supercomputing with #1, #2 Systems & Petascale Arm

November 12, 2018

The 31st Supercomputing Conference (SC) - commemorating 30 years since the first Supercomputing in 1988 - kicked off in Dallas yesterday, taking over the Kay Ba Read more…

By Tiffany Trader

HPE No. 1, IBM Surges, in ‘Bucking Bronco’ High Performance Server Market

September 27, 2018

Riding healthy U.S. and global economies, strong demand for AI-capable hardware and other tailwind trends, the high performance computing server market jumped 28 percent in the second quarter 2018 to $3.7 billion, up from $2.9 billion for the same period last year, according to industry analyst firm Hyperion Research. Read more…

By Doug Black

Intel Announces Cooper Lake, Advances AI Strategy

August 9, 2018

Intel's chief datacenter exec Navin Shenoy kicked off the company's Data-Centric Innovation Summit Wednesday, the day-long program devoted to Intel's datacenter Read more…

By Tiffany Trader

Germany Celebrates Launch of Two Fastest Supercomputers

September 26, 2018

The new high-performance computer SuperMUC-NG at the Leibniz Supercomputing Center (LRZ) in Garching is the fastest computer in Germany and one of the fastest i Read more…

By Tiffany Trader

Houston to Field Massive, ‘Geophysically Configured’ Cloud Supercomputer

October 11, 2018

Based on some news stories out today, one might get the impression that the next system to crack number one on the Top500 would be an industrial oil and gas mon Read more…

By Tiffany Trader

D-Wave Breaks New Ground in Quantum Simulation

July 16, 2018

Last Friday D-Wave scientists and colleagues published work in Science which they say represents the first fulfillment of Richard Feynman’s 1982 notion that Read more…

By John Russell

MLPerf – Will New Machine Learning Benchmark Help Propel AI Forward?

May 2, 2018

Let the AI benchmarking wars begin. Today, a diverse group from academia and industry – Google, Baidu, Intel, AMD, Harvard, and Stanford among them – releas Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This