High Performance Computing for Computational Science – by John Shalf, Sudip Dosanjh, John Morrison (auth.), José M.

By John Shalf, Sudip Dosanjh, John Morrison (auth.), José M. Laginha M. Palma, Michel Daydé, Osni Marques, João Correia Lopes (eds.)

This e-book constitutes the completely refereed post-conference
proceedings of the ninth overseas convention on excessive functionality Computing for Computational technology, VECPAR 2010, held in Berkeley, CA, united states, in June 2010.

The 34 revised complete papers provided including 5 invited contributions have been rigorously chosen in the course of rounds of reviewing and revision. The papers are equipped in topical sections on linear algebra and solvers on rising architectures, large-scale simulations, parallel and dispensed computing,
numerical algorithms.

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Read or Download High Performance Computing for Computational Science – VECPAR 2010: 9th International conference, Berkeley, CA, USA, June 22-25, 2010, Revised Selected Papers PDF

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Extra resources for High Performance Computing for Computational Science – VECPAR 2010: 9th International conference, Berkeley, CA, USA, June 22-25, 2010, Revised Selected Papers

Sample text

L − 1, in a series of matrix-vector products updates immediately before computing x˜l . Both algorithms have the same operation counts but have different memory access patterns for the matrices and vectors. In particular, the forward-looking algorithm exploits the temporal locality of the previously calculated x˜i that are used in the matrix-vector products while the backward-looking algorithm exploits the temporal locality of x˜i that are being determined/stored. While both algorithms have different advantages, we chose to implement the backward-looking algorithm, since we were able to use our compressed row storage matrices in a more natural manner.

However, there are several counter examples where the speedup slightly decreases. Fig. 12. Application matrices. Effects of binding threads to cores by setting thread affinity on Nehalem system: active barrier. Speedups for algorithm when setting thread affinity (TA) and not setting thread affinity (NTA) for 2, 4, and 8 threads. Fig. 13. Application matrices. Effects of binding threads to cores by setting thread affinity on Istanbul system: active barrier. Speedups for algorithm when setting thread affinity (TA) and not setting thread affinity (NTA) for 2, 6, and 12 threads.

The morphology of our heart model is reconstructed from human multidetector computed tomography data and discretized using the finite element method. For the electrophysiology simulation, a composite voxel mesh with fine voxels in and around the heart and coarse voxels covering the torso is adopted to solve the bidomain equation. Following the excitation of a sinus node, the simulator reproduces the excitation propagation and depolarization of the membrane potentials of virtual cells sequentially in the atria, conduction system, and ventricles.

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