oday, scientific research is carried out on supercomputing clusters, a shared resource that consumes hundreds of kilowatts of power and costs millions of dollars to build and maintain. As a result, researchers must fight for time on these resources, slowing their work and delaying results. NVIDIA and its worldwide partners today announced the availability of the GPU-based Tesla™ Personal Supercomputer, which delivers the equivalent computing power of a cluster, at 1/100th of the price and in a form factor of a standard desktop workstation.
“We’ve all heard ‘desktop supercomputer’ claims in the past, but this time it’s for real,” said Burton Smith, Microsoft Technical Fellow. “NVIDIA and its partners will be delivering outstanding performance and broad applicability to the mainstream marketplace. Heterogeneous computing, where GPUs work in tandem with CPUs, is what makes such a breakthrough possible.”
Priced like a conventional PC workstation, yet delivering 250 times the processing power, researchers now have the horsepower to perform complex, data-intensive computations right at their desk, processing more data faster and cutting time to discovery.
“GPUs have evolved to the point where many real world applications are easily implemented on them and run significantly faster than on multi-core systems,” said Prof. Jack Dongarra, director of the Innovative Computing Laboratory at the University of Tennessee and author of LINPACK. “Future computing architectures will be hybrid systems with parallel-core GPUs working in tandem with multi-core CPUs."
Leading institutions including MIT, the Max Planck Institute, University of Illinois at Urbana-Champaign, Cambridge University, and others are already advancing their research using GPU-based personal supercomputers.
“GPU based systems enable us to run life science codes in minutes rather than the hours it took earlier. This exceptional speedup has the ability to accelerate the discovery of potentially life-saving anti-cancer drugs,” said Jack Collins, manager of scientific computing and program development at the Advanced Biomedical Computing Center in Frederick Md., operated by SAIC-Frederick, Inc.
At the core of the GPU-based Tesla Personal Supercomputer is the Tesla C1060 GPU Computing Processor which is based on the NVIDIA® CUDA™ parallel computing architecture. CUDA enables developers and researchers to harness the massively parallel computational power of Tesla through industry standard C.
BAD NVIDIA! NO COOKIE! Why?
From the press release of the Top500 of Nov 2008:
The entry level to the list moved up to the 12.64 Tflop/s mark on the Linpack benchmark, compared to 9.0 Tflop/s six months ago.
That means you still need to cluster those boxen to make the list. Being a supercomputer is a moving target. Now. Would I LOVE to rty to build a true HPC system from this? Oh yeah. Will I get to?
*punches in nvidia reps number*
*puts down phone*
Hrmph. They gotta have caller ID.
Just kidding. However, I really hate this HPC on a Desktop marketing BS everyone keeps doing. REALLY hate it.