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NVIDIA Releases CUDA 5

Latest version features new tools, libraries and functions that enhance developer productivity.

| Published October 22, 2012

NVIDIA released the NVIDIA CUDA 5 production release, a new version of its pervasive parallel computing platform and programming model for accelerating scientific and engineering applications on GPUs. It can be downloaded for free from the NVIDIA Developer Zone website.

The new release includes support for dynamic parallelism, GPU-callable libraries, NVIDIA GPUDirect technology support for RDMA (remote direct memory access) and the NVIDIA Nsight Eclipse Edition integrated development environment (IDE).

GPU threads can dynamically spawn new threads, allowing the GPU to adapt to the data. By minimizing the back and forth with the CPU, dynamic parallelism simplifies parallel programming, and enables GPU acceleration of a broader set of algorithms, such as those used in adaptive mesh refinement and computational fluid dynamics applications.

A new CUDA BLAS Library allows developers to use dynamic parallelism for their own GPU-callable libraries. They can design plug-in APIs that allow other developers to extend the functionality of their kernels, and allow them to implement callbacks on the GPU to customize the functionality of third-party GPU-callable libraries.

GPUDirect technology enables direct communication between GPUs and other PCI-E devices, and supports direct memory access between network interface cards and the GPU. It also reduces MPISendRecv latency between GPU nodes in a cluster and improves overall application performance.
 
NVIDIA Nsight Eclipse Edition enables programmers to develop, debug and profile GPU applications within the Eclipse-based IDE on Linux and Mac OS X platforms. An integrated CUDA editor and CUDA samples speed the generation of CUDA code, and automatic code refactoring enables easy porting of CPU loops to CUDA kernels. An integrated expert analysis system provides automated performance analysis and step-by-step guidance to fix performance bottlenecks in the code, while syntax highlighting makes it easy to differentiate GPU code from CPU code.

For more information, visit NVIDIA.

Sources: Press materials received from the company and additional information gleaned from the company's website.
 

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