NVIDIA-Tesla-K10-GPU mit Leistungsrekord bei wissenschaftlichen Simulationen

Weitere Informationen:  
Susanna Tatár

Gebhardt Böhles Public Relations GmbH
Boris Böhles
Geschäftsführender Gesellschafter
Tel. +49 (0) 89 9230 6709 22

Hamburg, ISC ‘12, 18. Juni 2012. Die NVIDIA-Tesla-K10-GPU erreicht Leistungsrekorde bei populären High-Performance-Computing-Anwendungen wie seismische Berechnungen, Biowissenschaften und Videoverarbeitung. NVIDIA hat heute dazu Benchmarks veröffentlicht.

Lesen Sie mehr dazu in unten angefügter englischer Pressemitteilung.

NVIDIA Tesla K10 GPU Hits New Performance
Milestones for Scientific Simulation

World’s Fastest Single-Precision Accelerator, Tesla K10 GPU Accelerates Widely Used Defense, Seismic, Life Sciences, and Video Applications

HAMBURG, Germany—ISC’12—June 18, 2012—NVIDIA® Tesla® K10 GPUs offer performance breakthroughs on popular high performance computing (HPC) applications – ranging from seismic processing to life sciences to video processing – according to new benchmarks NVIDIA released today.

Based on the new NVIDIA Kepler™ computing architecture, the Tesla K10 GPU delivers the industry’s highest single precision performance (4.58 teraflops) and highest memory bandwidth (320 GB/sec) in a single accelerator. This is 12 times higher single precision flops and 6.4 times higher memory bandwidth than the latest-generation Intel Sandy Bridge CPUs1 .

The Tesla K10 GPU outperforms CPUs and previous-generation GPUs across the board on the most popular, compute-intensive applications for four key market segments, including:

  • Defense: video analytics, video stabilization, orthorectification, computer vision
  • Life and material sciences: molecular dynamics
  • Oil and gas: seismic processing, reverse time migration
  • Media and entertainment: video editing, video rendering/transcoding, ray tracing

“A distinct advantage of the Tesla K10 GPUs is that it excels in two key areas that have a dramatic impact on overall application performance: floating point operation and memory bandwidth,” said Sumit Gupta, senior director of Tesla business at NVIDIA. “Together, these enable the K10 GPU to deliver substantial out-of-the-box performance increases for the top science, engineering and commercial applications with little or no effort on the part of the developer.”

New Performance Records on AMBER and LAMMPS
On AMBER, a leading biomolecular simulation software application, four Tesla K10 GPUs achieved world record performance, delivering far superior results than what was available on multiple racks of servers just a few years ago.2

The Tesla system achieved performance of 76 nanoseconds of computer simulation time in a day for a 23,558 atom molecule, outstripping the previous record set with four Tesla M2090s last year, providing supercomputing performance to thousands of individual researchers to fuel further innovation in such areas as new drug discovery and more effective materials.

“In biomolecular science, adding a few more nanoseconds of simulation time can make a world of difference in the ability of researchers to study and better understand the behavior of complex biological systems,” said Ross Walker, assistant research professor, San Diego Supercomputing Center. “It still blows my mind that a single Tesla K10 outperforms some of the largest CPU clusters. The benefit it offers researchers is tremendous, enabling them to accelerate the search for new and better treatments for a host of diseases and disorders.”

The Tesla K10 GPU also delivers the highest performance on LAMMPS, another application widely used by the life sciences research community. Running the LAMMPS Lennard Jones Liquid Benchmark, a single Tesla K10 GPU outperforms a Tesla M2090 GPU by 80 percent, delivering the equivalent performance of a cluster with 64 x86 CPUs.3

Accelerating the Search for Energy
NVIDIA Tesla GPUs continue to deliver the highest performance on reverse time migration (RTM) applications for seismic processing in the oil and gas exploration industry, and for image processing in the computer vision industry. Petrobras, the national oil and gas company of Brazil, achieved an 1.8x speed up on its RTM application on the Tesla K10 GPU, as compared to a Tesla M2090 GPU within the same power envelope.

NVIDIA Tesla K10 GPUs are available from leading OEMs, including Appro Supercomputer Solutions, Dell, HP, IBM, SGI and Supermicro, as well as through NVIDIA distribution partners. More information about the Tesla K10 is available on the NVIDIA Tesla website.

About NVIDIA Tesla GPUs
NVIDIA Tesla GPUs are massively parallel accelerators based on the NVIDIA CUDA® parallel computing platform. Tesla GPUs are designed from the ground up for power-efficient, high performance computing, computational science and supercomputing, delivering dramatically higher application acceleration for a range of scientific and commercial applications than a CPU-only approach.

To learn more about CUDA or download the latest version, visit the CUDA website. More NVIDIA news, company and product information, videos, images and other information is available at the NVIDIA newsroom. You can also follow us on Twitter (@NVIDIATesla).

NVIDIA (NASDAQ: NVDA) hat durch die Erfindung des Grafikprozessors (GPU) im Jahr 1999 die Computergrafik revolutioniert. Heute setzt eine Vielzahl von Produkten auf NVIDIA-Prozessoren wie Smartphones bis hin zu Supercomputern. NVIDIAs Mobilprozessoren kommen in Mobiltelefonen, Tablets und Infotainment-Systemen im Automobilbereich zum Einsatz.  PC-Gamer schwören auf NVIDIA-GPUs, die für spektakuläre Spielewelten sorgen. Professionelle Anwender entwerfen mit ihrer Hilfe 3D-Grafiklösungen und visuelle Effekte für die Filmindustrie und Produkte wie Fahrzeuge, Gebäude oder komplette Landschaften. Darüber hinaus nutzen Forscher High-Performance-Computer mit NVIDIA-GPUs, um in der Wissenschaft neue Meilensteine zu setzen. Das Unternehmen hält weltweit über 5.000 Patente. Weitere Informationen finden Sie unter


1 Compared to Intel Xeon Processor E5-2690
2 Server system is 4 nodes, each node configuration with a single Tesla K10 GPUs, Dual Intel Xeon X5670, 72 GB DDR3 memory.  For large cluster performance benchmark: //
3 //