Abstract. Solvers for linear equation systems are commonly used in many different kinds of real applications, which deal with large matrices. Nevertheless, two key problems appear to limit the use of linear system solvers to a more extensive range of real applications: computing power and solution correctness. In a previous work, we proposed a method that employs high performance computing techniques together with verified computing techniques in order to eliminate the problems mentioned above. This paper presents an optimization of a previously proposed parallel self-verified method for solving dense linear systems of equations. Basically, improvements are related to the way communication primitives were employed and to the identification of the points in the algorithm in which mathematical accuracy is needed to achieve reliable results.
Abstract. This paper presents a theoretical performance analysis of a parallel implementation of a tool called Performance Evaluation for Parallel Systems (PEPS). This software tool is used to analyze Stochastic Automata Networks (SAN) models. In its sequential version, the execution time becomes impracticable when analyzing large SAN models. A parallel version of PEPS using distributed memory is proposed and modelled with SAN formalism. After, the sequential PEPS itself is applied to predict the performance of this model.
One of the main problems in the high performance computing area is to find the best strategy to parallelize an application. In this context, the use of analytical methods to evaluate the performance behavior before the real implementation of such applications seems to be an interesting alternative and can help to identify better directions for the implementation strategies. In this work, the Stochastic Automata Network (SAN) formalism is adopted to model and evaluate the performance of parallel applications. The methodology used is based on the construction of generic SAN models to describe classical parallel programming patterns, like Master/Slave, Pipeline and Divide and Conquer. Those models are adapted to represent cases of a real application through the definition of input parameters values. Finally, we present a comparison between the results of the SAN models and a real application, aiming at verifying the accuracy of the adopted technique.
Simpósio em Sistemas Computacionais978-0-7695-4614-8/11 $26.00
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