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The Tesla M-Class GPU M2090 Computing Module from Nvidia is based on the CUDA architecture code named "Fermi". The Tesla GPU computing module is fast parallel computing processor for high performance computing (HPC). Tesla GPU's high performance makes it ideal for seismic processing, biochemistry simulations, weather and climate modeling, signal processing, computational finance, CAE, CFD, and data analytics.
Accelerate the science with NVIDIA Tesla 20-series GPU. A companion processor to the CPU in the server, Tesla GPU speeds up HPC applications by 10 x. Based on the Fermi architecture, this GPU features up to 665 gigaflops of double precision performance, 1 teraflop of single precision performance, ECC memory error protection, and L1 and L2 caches. The Tesla M-class GPU module is integrated into GPU-CPU servers from OEM. This gives data center IT staff much greater choice in how to deploy GPU, with a wide variety of rackmount and blade systems and with remote monitoring and management capabilities, while enabling large data center, scale-out deployments.
Hundreds of CUDA Cores
Delivers up to 665 Gigaflops of double-precision peak performance in each GPU and enables servers from leading OEMs to deliver more than a teraflop of double-precision performance per 1 RU of space. Single precision peak performance is over one Teraflop per GPU.
ECC Memory Error Protection
Meets the critical requirement for computing accuracy and reliability in datacenters and supercomputing centers. Internal register files, L1/L2 caches, shared memory, and external DRAM all are ECC protected.
UP to 6GB Of GDDR5 Memory Per GPU
Maximizes performance and reduces data transfers by keeping larger data sets in local memory that is attached directly to the GPU.
System Monitoring Features
Integrates the GPU subsystem with the host system's monitoring and management capabilities such as IPMI or OEM-proprietary tools. IT staff can thus manage the GPU processors in the computing system using widely used cluster/grid management solutions.
L1 and L2 Caches as Part of the NVIDIA Parallel Data-cache
Accelerates algorithms such as physics solvers, ray-tracing and sparse matrix multiplication where data addresses are not known beforehand.
NVIDIA Giga-thread Engine
Maximizes the throughput by faster context switching that is 10 times faster than previous architecture, concurrent kernel execution, and improved thread block scheduling.
Asynchronous Transfer with Dual DMA Engines
Turbo-charges system performance by transferring data over the PCIe bus while the computing cores are crunching other data.
Flexible Programming Environment with Broad Support of Programming Languages and APIS
Choose C, C++, OpenCL, DirectCompute, or Fortran to express application parallelism and take advantage of the innovative "Fermi" architecture.
Table of Contents
Peak Double Precision: 665 Gigaflops Peak Single Precision: 1331 Gigaflops
6 GB GDDR5
Memory Clock: 1.85 GHz Memory I/o: 384-bit GDDR5
24 pcs 128 M x 16 GDDR5 SDRAM
8-pin PCIe power connector 6-pin PCIe power connector
<= 225 W
Supported Operating System
Linux and Windows 64-bit
177 GB/s (ECC off)
With ECC on, 12.5% of the GPU memory is used for ECC bits. So for example, 3 GB total memory yields 2.625 GB of user available memory with ECC on.