Titlebook: High Performance Computing for Computational Science - VECPAR 2008; 8th International Co José M. Laginha M. Palma,Patrick R. Amestoy,João C

Intercept

colony

反感

CUR

Miquel Pericàs,Ricardo Chaves,Georgi N. Gaydadjiev,Stamatis Vassiliadis,Mateo Valeroem, and to normalize a floating-point result. In the latter case, the shift amount is the result of a leading bit count. This chapter covers all these use cases, studying their requirements and proposing relevant architectures.

未开化

Improving the Performance of a Verified Linear System Solver Using Optimized Libraries and Parallel oned problems. In this scenario, a parallel version of a self-verified solver for dense linear systems appears to be essential in order to solve bigger problems. Moreover, the major goal of this research is to provide a free, fast, reliable and accurate solver for dense linear systems.

音乐戏剧

modest

Tunable Parallel Experiments in a GridRPC Framework: Application to Linear Solversic analysis for instance) and one must focus on the wall-time completion. In this work we tackle the problem by using the . Grid middleware that integrates an adaptable . service to solve a set of experiments issued from the simulations of the . project.

赔偿

Constituent

Data Locality Aware Strategy for Two-Phase Collective I/O stores and has as main purpose the reduction of the number of communication involved in the I/O collective operation and, therefore, the improvement of the global execution time. Compared with Two-Phase I/O, LATP I/O obtains important improvements in most of the considered scenarios.

CRUDE

A Parallel Incremental Learning Algorithm for Neural Networks with Fault Tolerancele, due to memory limitations. The parallel algorithm presented in this paper is usable in any parallel system, and in particular, with large dynamical systems such as clusters and grids in which faults may occur. Finally, the quality and performances (without and with faults) of that algorithm are experimentally evaluated.