- Nvidia Tesla
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nVidia Tesla 
The Tesla graphics processing unit (GPU) is nVidia's third brand of GPUs. It is based on high-end GPUs from the G80 (and on), as well as the Quadro lineup. Tesla is nVidia's first dedicated General Purpose GPU. The Tesla series takes its name from pioneering electrical engineer Nikola Tesla.
Contents
Tesla overview
Because of their very high computational power (measured in floating point operations per second or FLOPS) compared to previous microprocessors, the Tesla products target the high performance computing market.[1] The lack of ability to output images to a display[2] is the main difference between Tesla products and ordinary video cards. For equivalent single precision output, Fermi-based nVidia Geforce cards have four times less dual-precision performance. Tesla products primarily operate[3]:
- in simulations and in large scale calculations (especially floating-point calculations)
- for high-end image generation for applications in professional and scientific fields
with the use of OpenCL or CUDA.
As of 2011[update] nVidia Teslas power the second-fastest supercomputer in the world, Tianhe-1A, in Tianjin, China.
Specifications and configurations
Configuration Model # of GPUs Core clock
in MHz (each)Shaders Memory Processing Power (peak)
GFLOPs[4]Compute capability4 TDP watts Form factor
and featuresThread Processors (total) Clock in MHz (each) Bandwidth max (GB/s) Bus type Bus width (bit, each GPU) Total size (MiB) Clock (MHz) Single Precision(SP) Total(MUL+ADD+SF) Single Precision(SP) MAD(MUL+ADD) Double Precision(DP) FMA GPU Computing
Processor1C870 1 600 128 1350 76.8 GDDR3 384 1536 1600 518.4 345.6 0 1.0 170.9 Full-height video card Deskside Supercomputer1 D870 2 600 2 × 128 (256) 1350 153.6 GDDR3 384 3072 1600 1036.8 691.2 0 1.0 Deskside system or Rack unit GPU Computing
Server1S870 4 600 4 × 128 (512) 1350 307.2 GDDR3 384 6144 1600 2073.6 1382.4 0 1.0 1U Rack C1060
Computing Processor 2C1060 1 602 240 1300 102.4 GDDR3 512 4096 1600 933.12 622.08 77.76 1.3 187.8 2 slot video card S1075 1U [5]
GPU Computing
Server3,4S1070 4 602 4 × 240 (960) 1440 409.6 GDDR3 512 16384 1600 4147.2 2764.8 345.6 1.3 1U Rack
IEEE 754-2008 capabilitiesC2050/C2070
GPU Computing ProcessorC2050/C2070 1 575 448 1150 144 GDDR5 384 3072/61445 3000 1288 1030.46 515.2 2.0 238/247 Full-height video card
IEEE 754-2008 FMA capabilitiesM2050
GPU Computing ModuleM2050 1 575 448 1150 148.4 GDDR5 384 30725 3092 1288 1030.46 515.2 2.0 225 Computing Module
IEEE 754-2008 FMA capabilitiesM2070/M2070Q[6]
GPU Computing ModuleM2070/M2070Q 1 575 448 1150 150.336 GDDR5 384 61445 3132 1288 1030.46 515.2 2.0 225 Computing Module
IEEE 754-2008 FMA capabilitiesM2090[7][8][9]
GPU Computing ModuleM2090 1 650 512 1300 177 GDDR5 384 61445 1850 1331 ? 665 2.0 225 Computing Module
IEEE 754-2008 FMA capabilitiesS2050 1U
GPU Computing
SystemS2050 4 575 4 × 448 (1792) 1150 4 × 148.4 (593.6) GDDR5 384 122885 3092 5152 4121.66 2060.8 2.0 900 1U Rack
IEEE 754-2008 FMA capabilitiesNotes
- 1 Specifications not specified by NVIDIA are assumed to be based on the GeForce 8800GTX
- 2 Specifications not specified by NVIDIA are assumed to be based on the GeForce GTX 285
- 3 A host system/server is required to connect to the 1U GPU computing server by the PCI Express card (similar set-up as the Nvidia Quadro Plex)
- 4 Core architecture version according to the CUDA programming guide.
- 5 With ECC on, a portion of the dedicated memory is used for ECC bits, so the available user memory is reduced by 12.5%. (e.g. 3 GB total memory yields 2.625 GB of user available memory.)
- 6 Fermi implements the new fused multiply–add (FMA) instruction for both 32-bit single-precision and 64-bit double-precision floating point numbers (GT200 supported FMA only in double precision) that improves upon multiply-add by retaining full precision in the intermediate stage.[10]
- For the basic specifications of Tesla, refer to the GPU Computing Processor specifications.
- Performance figures are for single-precision except where noted.
- NVIDIA Tesla Supercomputers are also available with up to 8x Fermi GPUs from Manufacturers.
See also
- Nvidia Tesla Personal Supercomputer
- GeForce 8 series
- GeForce 200 Series
- GeForce 400 Series
- GeForce 500 Series
- CUDA
- GPGPU
- OpenCL
- Stream Processing
References
- ^ High Performance Computing - Supercomputing with Tesla GPUs
- ^ VR-Zone report
- ^ Tesla Technical Brief (PDF)
- ^ Nvidia Announces Tesla 20 Series
- ^ Difference between Tesla S1070 and S1075
- ^ NVidia Tesla M2050 & M2070/M2070Q Specs Online
- ^ TESLA M2090 Product brief
- ^ http://www.nvidia.com/docs/IO/43395/Tesla-M2090-Board-Specification.pdf
- ^ http://www.nvidia.com/docs/IO/105880/DS-Tesla-M-Class-Aug11.pdf
- ^ NVIDIA Fermi Compute Architecture Whitepaper.pdfPDF (855KiB), Page 13 of 22
External links
NVIDIA Product Overview and Technical Brief
- NVIDIA's Tesla homepage
- Nvidia Tesla C2050 / C2070 GPU Computing Processor
- Nvidia Tesla S2050 GPU Computing System
- Nvidia Tesla C1060 Computing Processor
- Nvidia Tesla S1070
- Nvidia Tesla M1060 Processor
- Nvidia Parallel Nsight
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- Video cards
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