Research On GPU Stream Computing And Its Applications

The current trend of commodity processors is towards developing mulit-core/many-core processors. By increasing the number of processor cores, the peak performances are keeping high-speed improvement. The latest graphics processor units (GPUs) are capable of achieving tera FLOPS in theory, which is superior to the commodity multi-core CPUs. This paper focuses on the research of stream computing model on GPU and its applications. The research is supported in part by National Natural Science Foundation (60803054), Zhejiang Provincial Natural Science Foundation (Y1100069) and AMD-Zhejiang University cooperation project. The main contributions are:1. We achieved the score calculation of biological sequence alignment on NVIDIA CUDA. Designed and implemented a new parallel algorithm named Diamond Tiled Wavefront algorithm, which can achieve the efficiency 1.7 times the traditional Tiled Wavefront algorithm's, better utilize the GPU parallelism, and faster return the local maximum match value of two sequences.2. We achieved the accurate full alignment of biological sequence alignment on CUDA. As we know, the stream sequence alignment algorithm we designed and implemented was the first GPU parallel algorithm that returns the accurate full alignment results.3. We achieved a new parallel algorithm of 3D convex hull generation on ATI Stream. The algorithm implemented map, queue and vector data structures in GPU. We also introduced other stream algorithms that used to aid and to optimize the algorithms mentioned above.4. We achieved the above algorithms in NVIDIA GeForce GTX285 and ATI Radeon 5870 GPU, using CUDA and OpenCL separately. And a series of models were tested.Our algorithms are general accelerating techniques on GPU, and can be extended to other problems in bioinformatics and geometric processing fields.