外文翻译--GPU集群的混合并行编程

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1、1700单词， 9100 英文字符， 3100 汉字 出处： Yang C T, Huang C L, Lin C F, et al. Hybrid Parallel Programming on GPU ClustersC/ International Symposium on Parallel and Distributed Processing with Applications. IEEE Computer Society, 2010:142-147. 附录 1 Hybrid Parallel Programming on GPU Clusters Abstract Nowadays,

2、 NVIDIA,s CUDA is a general purpose scalable parallel programming model for writing highly parallel applications. It provides several key abstractions - a hierarchy of thread blocks, shared memory, and barrier synchronization. This model has proven quite successful at programming multithreaded many

3、core GPUs and scales transparently to hundreds of cores: scientists throughout industry and academia are already using CUDA to achieve dramatic speedups on production and research codes. In this paper, we propose a hybrid parallel programming approach using hybrid CUDA and MPI programming, which par

4、tition loop iterations according to the number of C1060 GPU nodes in a GPU cluster which consists of one C1060 and one S1070. Loop iterations assigned to one MPI process are processed in parallel by CUDA run by the processor cores in the same computational node. Keywords: CUDA, GPU, MPI, OpenMP, hyb

5、rid, parallel programming I. INTRODUCTION Nowadays, NVIDIAs CUDA is a general purpose scalable parallel programming model for writing highly parallel applications. It provides several key abstractions - a hierarchy of thread blocks, shared memory, and barrier synchronization. This model has proven q

6、uite successful at programming multithreaded many core GPUs and scales transparently to hundreds of cores: scientists throughout industry and academia are already using CUDA to achieve dramatic speedups on production and research codes. In NVDIA the CUDA chip, all to the core of hundreds of ways to construct their chips, in here we will try to use NVIDIA to provide computing equipment for parallel computing. This paper proposes a solution to not only simplify the use of hardware accelerati