In this chapter, we show how to improve the efficiency of sorting on the GPU by making full use of the GPU's computational resources. Furthermore, because reading back data from the GPU to the CPU to perform operations such as sorting is inefficient, sorting the data on the GPU is preferable.īuck and Purcell 2004 showed how the parallel bitonic merge sort algorithm could be used to sort data on the GPU. Given that the GPU can outperform the CPU both for memory-bound and compute-bound algorithms, finding ways to sort efficiently on the GPU is important. Although implementing sorting algorithms on the CPU is relatively straightforward-mostly a matter of choosing a particular sorting algorithm to use-sorting on the GPU is less easily implemented because the GPU is effectively a highly parallel single-instruction, multiple-data (SIMD) architecture. Being able to efficiently sort large amounts of data is a critical operation. Sorting is one of the most important algorithmic building blocks in computer science. The CD content, including demos and content, is available on the web and for download. You can purchase a beautifully printed version of this book, and others in the series, at a 30% discount courtesy of InformIT and Addison-Wesley. GPU Gems 2 GPU Gems 2 is now available, right here, online.
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