Ada as a language for programming clusters of SMPs

This paper presents a new idea of developing parallel programs for clusters of SMP nodes using the Ada programming language. We show how to implement OpenMP in Ada and simplify programming of distributed memory applications using remote subprograms calls instead of complicated message passing

Evaluating linear recursive filters on clusters of workstations

The aim of this paper is to show that the recently developed high performance algorithm for solving linear recurrence systems with constant coefficients together with the new BLAS-based algorithm for narrow-banded triangular Toeplitz matrix-vector multiplication allow to evaluate linear recursive filters efficiently, even on clusters of workstations. The results of experiments performed on a cluster of twelve Linux workstations are also presented. The performance of the algorithm is comparable with the performance of two processors of Cray SV-1 for such kind of recursive problems

OpenMP parser for Ada

This paper describes OpenMP parser for Ada, which is meant to make parallel programming in Ada simpler. We present different approaches to parallel programming and advantages of OpenMP solution. Next, implemented directives and clauses are described, and appropriated examples given. General look at parsing algorithm is presented in another part of this article. Finally, we present the source code of sequential program written in OpenMP Ada transformed into the parallel program by presented parser

Professionally designed and developed OpenMP parser for the Ada programming language using flex

This paper describes a new version of OpenMP parser for Ada. AdaOMP consists of: OpenMP compiler for Ada and Ada package with OpenMP routines and variables. We show how compiler writing can be improved by the use of professional tool - flex - lexical analyzer, for kernel creation. In this paper we focus on describing steps leading to parser creation. We will explain some implementation details and present the results of using the OpenMP parser on sequential programs

Hybrid implementation of the fastICA algorithm for high-density EEG using the capabilities of the Intel architecture and CUDA programming

High-density electroencephalographic (EEG) systems are utilized in the study of the human brain and its underlying behaviors. However, working with EEG data requires a well-cleaned signal, which is often achieved through the use of independent component analysis (ICA) methods. The calculation time for these types of algorithms is the longer the more data we have. This article presents a hybrid implementation of the fastICA algorithm that uses parallel programming techniques (libraries and extensions of the Intel processors and CUDA programming), which results in a significant acceleration of execution time on selected architectures

Fast solver for Toeplitz bidiagonal systems of linear equations

We present a new efficient parallel algorithm for solving the first order linear recurrence systems with constant coefficients which is equivalent to the problem of solving Toeplitz bidiagonal systems of linear equations. The algorithm is formulated in the terms of level 1 and 2 BLAS (Basic Linear Algebra Subprograms) routines AXPY and GER. We also discuss its platform-independent implementation with OpenMP and finally present the results of experiments performed on a dual processor Pentium III computer running under Linux operating system with Altas as an efficient implementation of BLAS. The sequential version of the algorithm is up to 2.5 times faster than a simple sequential algorithm. 1