By Tim Anderson, The Register
Intel and NVIDIA are battling for the hearts and minds of developers in massively parallel computing.
Intel has been saying for years that concurrency rather than clock speed is the future of high performance computing, yet it has been slow to provide the mass of low-power, high-efficiency CPU cores needed to take full advantage of that insight.
Another angle on this is that GPUs are already designed for power-efficient massively parallel computing, and back in 2006 NVIDIA exploited its potential for general-purpose computing with its CUDA architecture, adding shared memory and other features to the GPU and providing supporting libraries and the CUDA SDK. CUDA is primarily a set of extensions to C, though there are wrappers for other languages.
At NVIDIA’s GPU Technology Conference in San Jose, California, last week, the company announced new editions of its Tesla GPU accelerator boards based on its “Kepler” architecture. These boards are designed for accelerating general-purpose computing rather than for driving displays. The Tesla K10, available now, has two Kepler GK104 GPUs, 3,072 cores in total, and performs at up to 4,577 gigaflops (2,288 gigaflops per GPU).