Download High Performance Computing: 30th International Conference, by Julian M. Kunkel, Thomas Ludwig PDF

By Julian M. Kunkel, Thomas Ludwig

This e-book constitutes the refereed court cases of the thirtieth foreign convention, ISC excessive functionality 2015, [formerly referred to as the overseas Supercomputing convention] held in Frankfurt, Germany, in July 2015.

The 27 revised complete papers awarded including 10 brief papers have been conscientiously reviewed and chosen from sixty seven submissions. The papers disguise the subsequent issues: reasonable information facilities, scalable functions, advances in algorithms, clinical libraries, programming types, architectures, functionality types and research, automated functionality optimization, parallel I/O and effort efficiency.

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Extra resources for High Performance Computing: 30th International Conference, ISC High Performance 2015, Frankfurt, Germany, July 12-16, 2015, Proceedings

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A number of software packages were used for the experiments. 14. A Framework for Batched and GPU-Resident Factorization Algorithms 41 level 3 level 2 el lev 1 Fig. 4. The shape of the matrix T for different level of the recursion during the QR decomposition. Regarding energy use, we note that in this particular setup the CPU and the GPU have about the same theoretical power draw. In particular, the Thermal Design Power (TDP) of the Intel Sandy Bridge is 115 W per socket, or 230 W in total, while the TDP of the K40c GPU is 235 W.

With the birth of new technologies, it is undoubted that the intra-chip and the interchip communication capability could and should be improved. By then, performance comparisons between different MM methods should be re-evaluated to find out the best-practice algorithm on novel architectures. References 1. : Toward an optimal algorithm for matrix multiplication. SIAM News 38, 1–3 (2005) 2. : The Theory of Matrices: with Applications. Academic Press, Waltham (1985) 3. : Dynamic programming and fast matrix multiplication.

Matrix multiplication (MM) is one of the core problems in the high performance computing domain and its efficiency impacts performances of almost all matrix problems. The high-density multi-GPU architecture escalates the complexities of such classical problem, though it greatly exceeds the capacities of previous homogeneous multicore architectures. In order to fully exploit the potential of such multi-accelerator architectures for multiplying matrices, we systematically evaluate the performances of two prevailing tilebased MM algorithms, standard and Strassen.

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