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Nvidia today unveiled a system for high-performance computing that uses four graphics processors to provide 1 teraflop of computing power, and multiple units can be easily combined to form a GPU-based computing cluster. The system competes with CPU-based clusters that employ Intel or AMD chips, but […]

teslaNvidia today unveiled a system for high-performance computing that uses four graphics processors to provide 1 teraflop of computing power, and multiple units can be easily combined to form a GPU-based computing cluster. The system competes with CPU-based clusters that employ Intel or AMD chips, but offers faster performance on some tasks while using less energy, Nvidia says. The system uses about 800 watts, meaning a 1-petaflop computer composed of GPUs would consume about half the power of Jaguar, the No. 2 fastest supercomputer on the planet.

Nvidia has signed deals with several HPC system manufactures, such as Appro, to get its Tesla S1070 into the hands of researchers and scientists. As Nvidia has focused on scientific computing, it has made GPU-based cards to be used in HPC systems, for desktop supercomputers and even for some corporate settings. This is its first GPU-system that comes pre-assembled, so companies or researchers don’t have to build the GPU clusters themselves. Its graphics processors, which excel at parallel processing for CAD design, creating software algorithms or Monte Carlo simulations, are used in several industries. Today’s announcement also underscores an emerging trend of using a broader range of processors in HPC and even data centers as the tradeoff between performance and energy becomes a bigger consideration.

  1. Second Life will still be laggy on it. :P

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  2. [...] Here are the three things that could keep Nvidia on top, without accounting for growth in its scientific and mobile computing products, which the company is also expected to ramp up this [...]

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  3. [...] Because Elemental takes advantage of Nvidia’s CUDA processing language, its server will only run on Nvidia chips for the time being. The company will also make the software available for customers to run on their own Nvidia-based hardware. [...]

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  4. [...] using DSPs to build things like a low-power supercomputer at Lawrence Berkeley National Lab, and computers taking advantage of GPUs or architectures like IBM’s Cell for parallel computing jobs, I should really know [...]

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  5. [...] is moving further into the business of selling hardware, rather than just chips, with its new reality server that uses its graphics processors to offer [...]

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  6. [...] learned from Larrabee and develop a coprocessor for the high-performance computing market, where accelerators in the form of GPUs and processors like IBM’s cell have gained [...]

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