NVIDIA Debuts 70 CUDA Applications Supporting GPU Accelerators to Meet Demand for Faster SimulationsNVIDIA today announced that 70 more widely used applications have added support for GPU acceleration so far this year, bringing the total number available to researchers, engineers and designers to more than 200.
Three of the newest applications to offer GPU acceleration are:
- ANSYS® Fluent®: ANSYS Fluent enables engineers to develop more aerodynamic cars and planes, which can save millions of dollars in fuel costs, or improve thermal management and reliability of electronic integrated circuit packages. ANSYS Fluent has added a new beta solver with single GPU support to its market-leading NVIDIA® CUDA® applications, including ANSYS MechanicalTM.
- MSC® Nastran®: Used by nearly every automotive manufacturer worldwide, MSC Nastran is a GPU-accelerated structural mechanics simulation application that helps optimize noise, vibration and harshness (NVH) performance, which are among the most often directly perceived quality attributes of a vehicle.
- CHARMM: Widely used by scientists to study biological processes at the molecular level, CHARMM's GPU acceleration enables a more accurate study of key proteins involved in disease, as well as interactions with drug candidates, as a means to develop more effective treatments.
"GPU computing first gained momentum among researchers who could download CUDA to accelerate their own applications for scientific discovery and research," said Addison Snell, chief executive officer of Intersect360 Research. "We are now in a new era where more commercial software is GPU-optimized, providing accelerated options across the full spectrum of engineering and business computing."
A partial list of other GPU-accelerating applications shipping or in development include:
- Computer-aided Engineering: Abaqus/Standard, Agilent ADS & EMPro, ANSYS Mechanical, CST MWS, MSC Nastran, Marc, OpenFOAM solver libraries, RADIOSSTM
- Defense & Intelligence: DigitalGlobe Advanced Ortho Series, Exelis (ITT) ENVI, Incogna GIS, Intergraph Motion Video Analyst, MotionDSP Ikena ISR, PCI Geomatics GXL
- Media & Entertainment: Adobe CS6, Autodesk 3ds Max & Maya, Blackmagic DaVinci Resolve, Chaos V-Ray RT, Elemental Server, Telestream Vantage
- Oil & Gas: Acceleware AxRTM, ffA SVI Pro, Headwave Suite, Paradigm Echos RTM, Schlumberger Visage, WesternGeco Omega2 RTM
- Scientific Computing: AMBER, CHARMM, Chroma, FastROCS, GAMESS, GROMACS, GTC, WL-LSMS, MATLAB, MILC, NAMD, QUDA, VASP, VMD
- Weather & Climate Forecasting: COSMO, GEOS-5, HOMME, HYCOM, WRF, NEMO, NIM
A complete list is available at www.nvidia.com/teslaapps.
Most Accessible Parallel Processors
The advent of massively parallel GPU accelerators that are easily programmable in popular high-level languages or using auto-parallelizing compilers has given impetus to developers to maximize application performance.
Accelerators give developers a great degree of flexibility to take advantage of dramatic application speedups using familiar languages like C, C++ and Fortran, or using the directives-based OpenACC standard programming model.
Simple extensions to these high-level programming languages enable specifying parallelism using the NVIDIA CUDA parallel computing platform and programming model. Today the CUDA platform is supported by every NVIDIA GPU, resulting in a worldwide installed base of more than 415 million CUDA GPUs.
Learn more about accelerated computing and supported software applications at NVIDIA booth 2217 at SC12, Nov. 12-15.
Tesla GPU Family
| Features |
Tesla K20X |
Tesla K20 |
Tesla K10 |
Tesla M2090 |
Tesla M2075 |
| Number and Type of GPU |
1 Kepler GK110 |
2 Kepler GK104s |
1 Fermi GPU |
1 Fermi GPU |
| GPU Computing Applications |
Seismic processing, CFD, CAE, Financial computing, Computational chemistry and Physics, Data analytics, Satellite imaging, Weather modeling |
Seismic processing, signal and image processing, video analytics |
Seismic processing, CFD, CAE, Financial computing, Computational chemistry and Physics, Data analytics, Satellite imaging, Weather modeling |
| Peak double precision floating point performance |
1.31 Tflops |
1.17 Tflops |
190 Gigaflops
(95 Gflops per GPU) |
665 Gigaflops |
515 Gigaflops |
| Peak single precision floating point performance |
3.95 Tflops |
3.52 Tflops |
4577 Gigaflops
(2288 Gflops per GPU) |
1331 Gigaflops |
1030 Gigaflops |
| Memory bandwidth (ECC off) |
250 GB/sec |
208 GB/sec |
320 GB/sec
(160 GB/sec per GPU) |
177 GB/sec |
150 GB/sec |
| Memory size (GDDR5) |
6 GB |
5 GB |
8GB
(4 GB per GPU) |
6 GigaBytes |
6 GigaBytes |
| CUDA cores |
2688 |
2496 |
3072
(1536 per GPU) |
512 |
448 |
* Note: With ECC on, 12.5% of the GPU memory is used for ECC bits. For example, 6 GB total memory yields 5.25 GB of user available memory with ECC on.
About CUDA
CUDA is a parallel computing platform and programming model developed by NVIDIA. It enables dramatic increases in computing performance by harnessing the power of GPUs.
More information about NVIDIA CUDA GPUs is available at the Tesla® GPU website. 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. Follow us on Twitter at @NVIDIATesla.
About NVIDIA
NVIDIA (NASDAQ: NVDA) awakened the world to computer graphics when it invented the GPU in 1999. Today, its processors power a broad range of products from smartphones to supercomputers. NVIDIA's mobile processors are used in cell phones, tablets and auto infotainment systems. PC gamers rely on GPUs to enjoy spectacularly immersive worlds. Professionals use them to create 3D graphics and visual effects in movies and to design everything from golf clubs to jumbo jets. And researchers utilize GPUs to advance the frontiers of science with high performance computing. The company has more than 5,000 patents issued, allowed or filed, including ones covering ideas essential to modern computing. For more information, see https://www.nvidia.com/.
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