Standard GPU Cluster Provides High Performance In The Mid-Range

May 4, 2009
Supercomputing continues to get faster, cheaper, and move available. Costs are dropping rapidly partially because of graphics processing units (GPUs) and their highly parallel architecture. NVidia continues to deliver high-performance Tesla GPU computing

Supercomputing continues to get faster, cheaper, and move available. Costs are dropping rapidly partially because of graphics processing units (GPUs) and their highly parallel architecture. NVidia continues to deliver high-performance Tesla GPU computing platforms for the masses with its Tesla Preconfigured Cluster.

The Tesla Preconfigured Cluster is designed as a scalable solution for midrange supercomputing applications. A typical four-node system starts with head node that’s the cluster controller. It is connected via a 24-port InfiniBand switch to the nodes that pair a 1U computer system with a four-GPU Tesla S1070 1U system. The S1070 connection is via x16 PCI Express links.

Pricing for this type of system starts around $50,000. It comes installed with Linux cluster software and CUDA cluster support. CUDA is NVidia’s programming environment for its GPUs (see “Go With The Flow—Dataflow, That Is”).

The Tesla S1070’s four 1.44-GHz GPUs house a total of 960 processing cores that have a peak double-precision floating-point performance of 345 GFLOPS. For single-precision work, the system delivers 4.14 TFLOPS. The four-node cluster delivers four times this performance. For many applications, the GPU approach offers significant benefits including lower power requirements, lower cost, and less space sometimes by more than a factor of 30.

Linux cluster software is available now, and Windows Server 2003/2008 will be available later in the year. Larger configurations are available.

NVidia

www.nvidia.com

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William G. Wong | Senior Content Director - Electronic Design and Microwaves & RF

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