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Scientists and mathematicians are increasingly realizing the computational benefits of using modern, multi-core architectures. In response to this, manufacturers of traditional desktop Graphics-Processing Units (GPUs) have evolved their architectures to create desktop and server GPGPUs (General Purpose Graphics Processing Units). These GPGPUs are quickly becoming the platform of choice for many high-performance, highly parallel applications. GPGPUs are also commodity hardware products commonly available in many desktop and laptop computers, making them rather inexpensive. The tools to program them are easily available as well; Nvidia's Compute Unified Device Architecture (CUDA) package, for example, provides a small set of extensions to the C programming language, allowing for straightforward implementation of parallel algorithms on GPGPUs.
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