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Graphics processing units(GPUs) provide a low cost platform for accelerating high performance computations. New programming languages, such as CUDA and OpenCL, make GPU programming attractive to programmers. However, programming GPUs is still a cumbersome task for two reasons, tedious performance optimizations and lack of portability. First, optimizing an algorithm for a specific GPU is a time-consuming...
Many applications meet certain programming patterns like pipeline, fork-join, do-all etc. While tools such as OS threads and OpenMP allow programmers only to express task or data parallelism, special support for programming patterns is distinctly lacking. Intel threading building blocks (TBB) is developed to address this problem, but its scheduler is general and not optimized for any of its parallel...
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...
Multimedia and some scientific applications have achieved good performance on the stream processor architecture by employing the stream programming model. In order to find out the way to accelerate the symmetric cryptograph on stream processor, we implement and analyze cryptograph algorithms on different stream processors in this paper. Four cipher algorithms including RC5, AES, TWOFISH and 3DES in...
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