Nvidia has released a new version of its Cuda parallel computing platform, which the company states will make it easier for computational biologists, chemists, physicists, geophysicists, other researchers and engineers to advance their simulations and computational work by using GPUs. Key enhancements include a re-designed visual profiler with automated performance analysis, providing an easier path to application acceleration, and a new compiler, based on the widely-used LLVM open-source compiler infrastructure, delivering up to 10 per cent speed up in application performance.
‘The new visual profiler is amazing,’ said Joshua Anderson, lead developer of the HOOMD-blue open source molecular dynamics project. ‘With just a few clicks, it performs an automated performance analysis of your application, highlights likely problem areas, and then provides links to best-practice suggestions on improving them. It makes it quick and easy for virtually all developers to accelerate a broad range of applications.’
The size of the Nvidia Performance Primitives (NPP) library has also been doubled with the addition of hundreds of new imaging and signal processing functions. This enables virtually any developer using image or signal processing algorithms to benefit from GPU acceleration with the simple addition of library calls into their application. The updated NPP library can be used for a wide variety of image and signal processing algorithms, ranging from basic filtering to advanced workflows.