NVIDIA Tesla K20-K20X GPU Accelerators Benchmarks Application Performance Technical Brief

NVIDIA Tesla® K20-K20X GPU Accelerators ­

Benchmarks

Teraflop

Single Precision Performance 3.0

2.0

1.0

0.0 0.35

0.89 (SGEMM)

2.9

Xeon E5-2687W Tesla M2090 Tesla K20X (Fermi)

5x

0x

CP2K- Quantum Chemistry

15x�

10x�

K20 with Hyper-Q K20 without Hyper-Q

2.5x

0 5 10 15 20

Number of GPUs

NVIDIA® changed the high performance computing (HPC) landscape by introducing its Fermi­ based GPUs that delivered an impressive leap in performance and energy-efficiency while offering a parallel programming model, CUDA ®, as an extension to industry standard languages like C, C++, and Fortran.

Now NVIDIA has introduced the new Tesla® K20-K20X GPU Accelerators that further advance the HPC industry. Built on the revolutionary Kepler™ architecture, these accelerators redefine the standard for energy-efficient computing and feature innovative technologies like SMX, Hyper-Q, and Dynamic Parallelism to boost application performance by up to 10x.

SMX: 3x More Performance P

er Watt

The new SMX (Next Generation Streaming Multiprocessor) is an architectural innovation

designed from t he ground-up to deliver high efficiency performance. With SMX at its core,

Tesla K20/K20X accelerators deliver the industry’s highest single and double precision performance- 3.95 teraflops and 1.31 teraflops respectively for Tesla K20X- at an unprecedented 93% computational efficiency.

1.5

1.0

0.5

0.0 0.17

0.43

1.22

Xeon E5-2687WTesla M2090 (Fermi) Tesla K20X

Teraflop Double Precision Performance

(DGEMM)

Hyper-Q: Easy S

peed-up for Legacy MPI

Codes

Many legacy MPI codes don’t generate enough work to fully occupy the GPU because they were written for CPU cores. Rather than requiring developers to refactor their codes to put more

workload per MPI process, the Hyper-Q f eature

reduces efforts considerably because developers can now throw up to 32 MPI processes with small and medium-sized workloads at a shared GPU. To illustrate the power of Hyper-Q, we picked a traditionally difficult code for GPUs called CP2K, a

popular MPI-based quantum c hemistry code,

showing more than 2x performance improvement when 16 MPI ranks are run on the shared GPU.

Tesla K20X Performance Advantage over Sandy Bridge CPUs

Quicksort

Without Dynamic Parallelism

2x

With Dynamic Parallelism

0 5 10

Dynamic Parallelism: Simplifying

arallel P

rogramming

P

Dynamic Parallelism allows the GPU to operate more autonomously from the CPU by generating new work for itself at run­ time, from inside a kernel. The concept is simple, but the impact is powerful: it can make programming easier, particularly for algorithms traditionally considered difficult such as divide-and-conquer problems. To showcase its potential, we used Dynamic Parallelism on Quicksort, a

well-Problem Size (Million of Elements)

known algorithm for sorting methods, to

reduce the lines of CUDA code in half while improving performance by 2x.

K20X Benchmarks by Application

Accelerating Key Scientific Applications by up to 10x

Today, hundreds of applications take advantage of GPU acceleration, spanning all scientific disciplines and engineering domains, and the number of applications continues to grow. In the past year alone, the number of CUDA-accelerated applications has grown by over 60%. When Tesla K20 GPU Accelerators are added to servers with Sandy Bridge CPUs, CUDA-enabled applications are typically accelerated up to 10x. The K20X benchmark below shows single node performance of leading applications in various science domains.

CPU results: Dual socket E5-2687w; GPU results: Dual socket E5-2687w + 2 Tesla K20X GPUs *MATLAB results comparing one i7-2600K CPU vs. Tesla K20 GPU

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2xCPU 2xCPU+ 2xCPU+ 2xCPU+ 2xCPU+

1xM2090 2xM2090 1xK20X 2xK20X

CPU r esults: Dual socket E5-2687w; GPU r esults: Dual socket E5-2687w + 2 Tesla K20X GPUs, 64GB per node

Here’s a closer look at three of the applications listed above.

Chroma: High Energy &

Nuclear Physics

Chroma is used by scientists to test alternatives to the Standard Model of physics in order to develop a deeper understanding of the fundamental properties of matter and energy. The benchmark below show significant performance boost when adding one or two Tesla K20X GPU Accelerators to dual socket CPUs.

SPECFEM3D: Earth Sciences

Researchers use SPECFEM3D for a deeper understanding of the phenomena that shape seismic activity, thereby allowing engineers to assess potential hazards and to build structural countermeasures. This code was a Gordon Bell winner in 2008 prior to the GPU work, so it was a highly tuned code even before the recent work to accelerate on GPUs.

256x128 3D Spatial Discretization

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CPU r esults: Dual socket E5-2687w; GPU r esults: Dual socket E5-2687w + 2 Tesla K20X GPUs, 64GB per node

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1xM2090 2xM2090 1xK20X 2xK20X

CPU results: Dual socket E5-2687w; GPU results: Dual socket E5-2687w + 2 Tesla K20X GPUs, 64GB per node

AMBER: Molecular Dynamics

Molecular dynamics (MD) allows the study of biological and chemical systems at the atomistic

level on timescales from f emtoseconds to milliseconds. Numerous software packages exist for

conducting MD simulations of which one of the most widely used is AMBER. With the Tesla K20X GPU Accelerators, acceleration by up to 80% is observed on AMBER, over compared to Tesla M2090 GPUs.

32 Atom Emsemble of Iron (Fe)

30x

20x

6

10x

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Cray XK7 -Tesla K20X Cray XK7- CPU

0 20 40 60 80 100 120 140 160 180 200

Large Cluster Scaling for GPU-Accelerated Applications

Large clusters around the world are increasingly deploying GPUs to accelerate their workload.

In the Top500 list, from J une 2011 to June 2012, number of GPU-accelerated systems grew by

over 400%.

Whether an application is GPU-accelerated or not, designing code to scale well across many nodes has never been an easy task. However, many GPU-accelerated applications are scaling well as developers work on extracting more parallelism by allocating more parallel work within a node and use MPI to distribute GPU workloads.

WL-LSMS: Material Science

WL-LSMS simulates the magnetic behavior of materials at the atomic and nanoscale in order to design lightweight, powerful magnetic components for highly efficient electric motors,

generators, and magnetic storage devices. WL-LSMS was a Gordon Bell winner in 2009, so it was already a highly-tuned CPU code prior to the recent work to accelerate on GPUs.

x

QMCPACK: Material S

cience

QMCPACK is used by researchers to develop new insights into condensed matter physics, materials science, and chemistry by using more physically accurate methods. For large

systems on the order of 200 electrons or more, simulations that traditionally would take upwards of 12 hours or more can now run on the order of 3 hours per simulation.

Compute

_3x3x1

_Graphite

1500000

CrayXK7­TeslaK20X CrayXK7­CPU

1000000

4x

500000

0

0 500 1000 1500 2000 2500

#ofCompute Nodes

ns/day

100x

STMV

2.0

1.5

1.0

0.5

0.0

CrayXK7­ K20X

CrayXK7­ CPU

4x

128 256 512 768

#ofComputeNodes

NAMD: Molecular Dynamics

NAMD (Not (just) Another Molecular Dynamics program) is a free-of-charge molecular dynamics simulation package written using the Charm++ parallel programming model, noted for its parallel efficiency and often used to simulate large systems (millions of atoms). NAMD also scales well across many nodes accelerated with GPUs.

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Copyright

© 2012 NVIDIA Corporation. All rights reserved. NVIDIA, the NVIDIA logo, Tesla, Kepler, and CUDA are trademarks and / or registered trademarks of NVIDIA Corporation. All company and product names are trademarks or registered trademarks of the respective owners with which they are associated. Features, pricing, availability, and specifications are all subject to change without notice. NOV 2012