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New PDF release: Algorithms and Architectures for Parallel Processing: 14th

By Xian-he Sun, Wenyu Qu, Ivan Stojmenovic, Wanlei Zhou, Zhiyang Li, Hua Guo, Geyong Min, Tingting Yang, Yulei Wu, Lei Liu (eds.)

This quantity set LNCS 8630 and 8631 constitutes the complaints of the 14th overseas convention on Algorithms and Architectures for Parallel Processing, ICA3PP 2014, held in Dalian, China, in August 2014. The 70 revised papers provided within the volumes have been chosen from 285 submissions. the 1st quantity includes chosen papers of the most convention and papers of the first foreign Workshop on rising issues in instant and cellular Computing, ETWMC 2014, the fifth overseas Workshop on clever conversation Networks, IntelNet 2014, and the fifth overseas Workshop on instant Networks and Multimedia, WNM 2014. the second one quantity contains chosen papers of the most convention and papers of the Workshop on Computing, conversation and regulate applied sciences in clever Transportation method, 3C in ITS 2014, and the Workshop on safeguard and privateness in computing device and community platforms, SPCNS 2014.

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Read or Download Algorithms and Architectures for Parallel Processing: 14th International Conference, ICA3PP 2014, Dalian, China, August 24-27, 2014. Proceedings, Part I PDF

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Additional info for Algorithms and Architectures for Parallel Processing: 14th International Conference, ICA3PP 2014, Dalian, China, August 24-27, 2014. Proceedings, Part I

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Previous work such as time skewing[16] can make a stencil computation compute bound by making use of data locality between different time-steps. But for real-world climate models including mpiPOM, the code is usually tens to hundreds of thousand lines and analyzing the dependency manually is tough. An automate tool to further optimize the mpiPOM and the gpuPOM is a part of future work. 4 S. Xu et al. Scalability In the weak-scaling experiment, we test our communication overlapping design used in the gpuPOM and compare it with another two communication method as shown in Fig.

For nb = 8 in our case, we may totally unroll the loop j. It is trivial to replace scalar instructions with SIMD instructions for sum and nnz. However, it is difficult to vectorize the load of vector x without SIMD gather instructions, which is only available on AVX2 and AVX-512 instruction sets. Therefore, we use scalar instructions to access the column index array cols and the vector x. Then, we compose each individual value of vector x to SIMD vectors. On top of that, we can use SIMD multiplication and addition instructions for line 6 in Algorithm 3.

To fully overlap the boundary operations and MPI communications with computation, we adopt the data decomposition method shown in Fig. 2. The data Porting the Princeton Ocean Model to GPUs 7 North Halo(stream 1) North Part (stream 2) East Halo (stream 1) (stream 1) (stream 0) East Part West Part (stream 1) (stream 1) West Halo 32 Inner Part South Part (stream 2) South Halo(stream 1) Fig. 2. Data decomposition in the gpuPOM Rank0: GPU0 stream1 stream2 stream3 Inner Part East/West part Halo Comm.

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