Parallel scientific computing with message-passing toolboxes

Los usuarios de Entornos de Computación Científica (SCE, por sus siglas en inglés) siempre requieren mayor potencia de cálculo para sus aplicaciones. Utilizando las herramientas propuestas, los usuarios de las conocidas plataformas Matlab® y Octave, en un cluster de computadores, pueden paralelizar sus aplicaciones interpretadas utilizando paso de mensajes, como el proporcionado por PVM (Parallel Virtual Machine) o MPI (Message Passing Interface). Para muchas aplicaciones SCE es posible encontrar un esquema de paralelización con ganancia en velocidad casi lineal. Estas herramientas son interfaces prácticamente exhaustivas a las correspondientes librerías, soportan todos los tipos de datos compatibles en el SCE base y se han diseñado teniendo en cuenta el rendimiento y la facilidad de mantenimiento. En este artículo se resumen trabajos anteriores, su repercusión, y algunos resultados obtenidos por usuarios finales. Con base en la herramienta más reciente, la Toolbox MPI para Octave, se describen brevemente sus características principales, y se presenta un estudio de caso, el conjunto de Mandelbrotusers of Scientific Computing Environments (SCE) always demand more computing power for their CPu-intensive SCE applications. using the proposed toolboxes, users of the well-known Matlab® and Octave platforms in a computer cluster can parallelize their interpreted applications using the native multi-computer programming paradigm of message-passing, such as that provided by PVM (Parallel Virtual Machine) and MPI (Message Passing Inter-face). For many SCE applications, a parallelization scheme can be found so that the resulting speedup is nearly linear on the number of computers used. The toolboxes are almost compre-hensive interfaces to the corresponding libraries, they support all the compatible data types in the base SCE and they have been designed with performance and maintainability in mind. In this paper, we summarize our previous work, its repercussion, and some results obtained by end-users. Focusing on our most recent MPI Toolbox for Octave, we briefly describe its main features, and introduce a case study: the Mandelbrot se

Parallel implementation of a VQ-based text-independent speaker identification

This study presents parallel implementation of a vector quantization (VQ) based text-independent speaker identification system that uses Mel-frequency cepstrum coefficients (MFCC) for feature extraction, Linde-BuzoGray (LBG) VQ algorithm for pattern matching and Euclidean distance for match score calculation. Comparing meaningful characteristics of voice samples and matching them with similar ones requires large amount of transformations and comparisons, which result in large memory usage and disk access. When the cost of computations is considered, it states the main motivation for a parallel speaker identification implementation, where the parallelism is achieved using domain decomposition. In this paper, we present a set of experiments using the YOHO speaker corpus and observe the effects of several parameters as VQ size, number of MFCC filter banks and threshold value. First we focus on the serial algorithm and improve the algorithm to give the best success rates and provide a strong base for parallel implementation, where a clear performance improvement on speedup is obtained