NVIDIA today released a new version of its CUDA parallel computing platform, which will make it easier for computational biologists, chemists, physicists, geophysicists, other researchers, and engineers to advance their simulations and computational work by using GPUs.
The new NVIDIA(R) CUDA(R) parallel computing platform features three key enhancements that make parallel programing with GPUs easier, more accessible and faster. These include:
Re-designed Visual Profiler with automated performance analysis,
easier path to application acceleration
New compiler, based on the widely-used LLVM open-source compiler
infrastructure, delivering up to 10 percent speed up in application
Hundreds of new imaging and signal processing functions, doubling the
size of the NVIDIA Performance Primitives (NPP) library
"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 LLVM complier gave me an almost immediate 10 percent performance speed up, just by recompiling my existing real-time financial risk analysis code," said Gilles Civario, senior software architect at the Irish Centre for High-End Computing. "I can only imagine the additional performance gains I can achieve with additional tuning using the new CUDA release."
Among the new features of the latest CUDA parallel computing platform release -- available free of charge on the NVIDIA developer web site at http://developer.nvidia.com/getcuda -- are:
New Visual Profiler - Easiest path to performance optimization The new Visual Profiler makes it easy for developers at all experience levels to optimize their code for maximum performance. Featuring automated performance analysis and an expert guidance system that delivers step-by-step optimization suggestions, the Visual Profiler identifies application performance bottlenecks and recommends actions, with links to the optimization guides. Using the new Visual Profiler, performance bottlenecks are easily identified and actionable.
LLVM Compiler - Instant 10 percent increase in application performance LLVM is a widely-used open-source compiler infrastructure featuring a modular design that makes it easy to add support for new programming languages and processor architectures. Using the new LLVM-based CUDA compiler, developers can achieve up to 10 percent additional performance gains on existing GPU-accelerated applications with a simple recompile. In addition, LLVM's modular design allows third-party software tool developers to provide a custom LLVM solution for non-NVIDIA processor architectures, enabling CUDA applications to run across NVIDIA GPUs, as well as those from other vendors.
New Image, Signal Processing Library Functions - "Drop-in" Acceleration with NPP Library NVIDIA has doubled the size of its NPP library, with the addition of hundreds of new image and signal processing functions. This enables virtually any developer using image or signal processing algorithms to easily gain the benefit of 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.
About CUDA CUDA is NVIDIA's parallel computing platform and programming model, which enables dramatic increases in computing performance by harnessing the power of GPUs. NVIDIA CUDA GPUs support all GPU computing programming models, APIs, and languages, including CUDA C/C++/Fortran, OpenCL, and DirectCompute. More than 500 universities and institutions worldwide teach the CUDA programming model within their curriculum. In addition, the CUDA parallel programming platform has been downloaded more than 1.2 million times to date.
For more information on the NVIDIA CUDA parallel computing platform visit the CUDA web site at www.nvidia.com/cuda.