Computing on GPUs
The increasing power of GPUs has led to the intent to transfer computing load from CPUs to GPUs. A first example has been the porting of computing intensive algorithms like e.g. ray-tracing algorithms from CPU to GPU. Through the Compute Unified Device Architecture (CUDA [4]) GPUs can also be used to increase computing speed for High Performance Computing applications. In this paper different parallelization strategies for different processor architectures are presented. They are compared and first experiences using GPUs for a collection of numerical applications are given.
https://www.dynalook.com/conferences/european-conf-2009/N-II-03.pdf/view
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Computing on GPUs
The increasing power of GPUs has led to the intent to transfer computing load from CPUs to GPUs. A first example has been the porting of computing intensive algorithms like e.g. ray-tracing algorithms from CPU to GPU. Through the Compute Unified Device Architecture (CUDA [4]) GPUs can also be used to increase computing speed for High Performance Computing applications. In this paper different parallelization strategies for different processor architectures are presented. They are compared and first experiences using GPUs for a collection of numerical applications are given.
N-II-03.pdf
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