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Submission + - Nvidia Unlocks Computing Potential in Graphics Chi

Submitted by kog777 on Friday February 16, 2007 @09:05PM

kog777 writes: Graphics chipmaker Nvidia Corp. (Nasdaq: NVDA) said Friday that it has released a new software development tool that will allow programmers to tap into the power of its processors, opening the door to solve complex computing problems. The firm's core product, the graphics processor (GPU) has traditionally been used to create complex 3D images in games and design software. Over the years, the GPU has become increasingly adept in handling mathematically intense calculations — functions specifically needed for graphics. Nvidia's CUDA development kit, however, promises to bring those functions outside of gaming, empowering scientists and engineers with raw processing power. http://www.ibtimes.com/articles/20070216/nvidia-cu da-sdk.htm

Submission + - An Introduction to NVIDIA's CUDA for GPGPU

Submitted by Anonymous Coward on Friday February 16, 2007 @01:00PM

An anonymous reader writes: NVIDIA has released their new GPGPU architecture programming tools to a public beta. "CUDA" (Compute Unified Device Architecture) aims to harness the considerable parallel compute power of the modern GPU in the service of applications beyond games. Beyond3D has a go at explaining what CUDA is, how it works, and its likely future.

Submission + - beta version of CUDA is available

Submitted by Anonymous Coward on Friday February 16, 2007 @12:05PM

An anonymous reader writes: http://developer.nvidia.com/object/cuda.html he CUDA Toolkit is a complete software development solution for programming CUDA-enabled GPUs. The Toolkit includes standard FFT and BLAS libraries, a C-compiler for the NVIDIA GPU and a runtime driver. The CUDA runtime driver is a separate standalone driver that interoperates with OpenGL and Microsoft® DirectX® drivers from NVIDIA. CUDA technology is currently supported on the Linux and Microsoft® Windows® XP operating systems. The CUDA Developer SDK provides examples with source code to help you get started with CUDA. Examples include: Parallel bitonic sort Matrix multiplication Matrix transpose Performance profiling using timers Parallel prefix sum (scan) of large arrays Image convolution 1D DWT using Haar wavelet OpenGL and Direct3D graphics interoperation examples CUDA BLAS and FFT library usage examples CPU-GPU C- and C++-code integration

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