By John Shalf, Sudip Dosanjh, John Morrison (auth.), José M. Laginha M. Palma, Michel Daydé, Osni Marques, João Correia Lopes (eds.)
This booklet constitutes the completely refereed post-conference
proceedings of the ninth foreign convention on excessive functionality Computing for Computational technological know-how, VECPAR 2010, held in Berkeley, CA, united states, in June 2010.
The 34 revised complete papers offered including 5 invited contributions have been conscientiously chosen in the course of rounds of reviewing and revision. The papers are prepared in topical sections on linear algebra and solvers on rising architectures, large-scale simulations, parallel and dispensed computing,
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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Additional info for High Performance Computing for Computational Science – VECPAR 2010: 9th International conference, Berkeley, CA, USA, June 22-25, 2010, Revised Selected Papers
M. A. G. Boman References 1. : Performance of a parallel algebraic multilevel preconditioner for stabilized ﬁnite element semiconductor device modeling. Journal of Computational Physics 228(17), 6250–6267 (2009) 2.
Speedups for passive barrier (PB) and active barrier (AB) for 2, 4, and 8 threads. Fig. 4. Eﬀects of diﬀerent barriers on Istanbul system: thread aﬃnity on, good data locality. Speedups for passive barrier (PB) and active barrier (AB) for 2, 6, and 12 threads. (a) Nehalem (b) Istanbul Fig. 5. Eﬀects of diﬀerent barriers: sizes 5000 and 10000, thread aﬃnity on, bad data locality. Runtimes for passive barrier (PB) and active barrier (AB). 2 Thread Aﬃnity Next, we examine the eﬀects of thread aﬃnity on the scalability of our triangular solve algorithm.
Programmers working in the efficiency layer are generally experts in achieving high performance from the underlying hardware, but are not necessarily knowledgeable of any given application domain. Programmers working in the productivity layer are generally knowledgeable about an application domain, but are less concerned with hardware details. The patterns bridge these two layers. Efficiency programmers develop libraries and frameworks that efficiently implement the standard patterns, and productivity programmers can decompose an application into patterns and use high-level languages to compose corresponding libraries and frameworks to form applications.