APPLICATIONS

NVIDIA Corporation and Cray today announced the availability of NVIDIA Tesla C1060 GPU Computing processors in the new Cray CX1 line of supercomputers. With "ease-of-everything" features and the ability to fit into a standard office setting, the Cray CX1 product reflects NVIDIA and Cray's goal to drive high productivity computing solutions into a broad array of markets including financial services, oil and gas, life sciences, government and academic. "NVIDIA GPU computing technology can powerfully accelerate computational capability on important classes of applications. The Cray CX1 system is a flexible platform for individual scientists, workgroups and departments, and coupled with NVIDIA's Tesla computing processors and Quadro GPUs, the Cray CX1 delivers an industry-defining supercomputer," said Ian Miller, senior vice president of sales and marketing at Cray. Each Tesla processor has hundreds of processor cores that deliver nearly one teraflop of peak computing performance. The Cray CX1 delivers up to four teraflops of performance, right at the deskside, when configured with four Tesla processors. "Many organizations today are investigating ways to augment the computational resources of their cluster and increase productivity," said Andy Keane, general manager of the GPU Computing business at NVIDIA. "The Cray CX1 combined with NVIDIA Tesla GPUs makes massive compute power accessible to scientists and engineers, transforming their workflow and enabling them to get results fast, dramatically increasing the pace of discovery." Using GPUs, researchers have reported up to 100x speed-up on applications in fields such as molecular dynamics, quantum chemistry, finite element analysis, particle simulation, and electronic design automation. The highly parallel architecture of the GPU has been made accessible for these industries through the NVIDIA CUDA architecture. With NVIDIA's award-winning C-compiler and software development kit (SDK) for developing parallel computing applications on GPUs, developers can exploit the GPU's parallel computing architecture and automatically distribute computing work across tens of thousands of threads and hundreds of processor cores, NVIDIA said.