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Memory optimization is an important strategy to gain high performance for sequence alignment implemented by CUDA on GPGPU. Smith-Waterman (SW) algorithm is the most sensitive algorithm widely used for local sequence alignment but very time consuming. Although several parallel methods have been used in some studies and shown good performances, advantages of GPGPU memory hierarchy are still not fully...
GPUs have recently been used to accelerate data-parallel applications for they provide easier programmability and increased generality while maintaining the tremendous memory bandwidth and computational power. Most of those applications use CPU as a controller who decides when GPUs run the computing-intensive tasks. This CPU-control-GPU-compute pattern wastes much of CPU's computational power. In...
Recently many studies have been made to map cryptography algorithms onto graphics processors (GPU), and gained great performances. This paper does not focus on the performance of a specific program exploited by using all kinds of optimization methods algorithmically, but the intrinsic reason which lies in GPU architectural features for this performance improvement. Thus we present a study of several...
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