High Performance Computing for Computational Science - by Horst D. Simon (auth.), Michel Daydé, Osni Marques, Kengo

By Horst D. Simon (auth.), Michel Daydé, Osni Marques, Kengo Nakajima (eds.)

This ebook constitutes the completely refereed post-conference complaints of the tenth foreign convention on excessive functionality Computing for Computational technological know-how, VECPAR 2012, held in Kope, Japan, in July 2012. The 28 papers awarded including 7 invited talks have been conscientiously chosen in the course of rounds of reviewing and revision. The papers are prepared in topical sections on CPU computing, purposes, finite aspect strategy from a variety of viewpoints, cloud and visualization functionality, process and instruments for complex medical computing, algorithms and knowledge research, parallel iterative solvers on multicore architectures.

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Extra info for High Performance Computing for Computational Science - VECPAR 2012: 10th International Conference, Kope, Japan, July 17-20, 2012, Revised Selected Papers

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The preconditioner matrix M , for our problem, is sparse. We compare our results to the standard Block Incomplete Cholesky preconditioner (for which M = LLT ). We apply the block structure to A and generate L as suggested in [10]. The Block Incomplete Cholesky preconditioners in our results are suffixed with a number like 2n, 4n etc. which denotes the block-size. So for example in a Block-IC preconditioner with blocksize 8n where the matrix −1 . A has N = n × n unknowns the preconditioner will be named like MBlk−IC(8n) Since the data parallelism in Block Incomplete schemes is limited by the blocksize (refer [1] for details) we turn our attention to preconditioners that have more inherent parallelism.

After each step, one column of tiles shifts from the GPUs to the CPUs.                  (a) Task Graph Scheduling (b) Work Partitioning Fig. 1. Scheduling the task graph of the LU factorization, with fine-grained tasks on the critical path being dispatched to individual CPU cores and coarse-grained tasks outside of the critical path being dispatched to GPU devices The implementation relies on a number of state-of-the-art solutions such as: tile data layout, block-cyclic data distribution, parallel recursive panel factorization, GPU kernel autotuning, the technique of lookahead, the use of superscalar scheduling and communication-computation overlapping.

Computed results by ZZ-EFSI on K computer ZZ-HIFU is a code deveeloped by Dr. Okita and others to solve sound propagattion equation to simulate ultra sound in human body for design of cancer treatment shoown in figure 5[4]. Its effectivee performance is about 20 percent using 10,000 nodes oof K computer. Grand Challenge in Life Science on K Computer 21 Fig. 5. Target of ZZ-HIFU UT-Heart is a heart simulation software developed by Prof. Hisada and his group in many years in The University of Tokyo[5].

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