Solving parallel problems by OTMP model

Since the early stages of parallel computing, one of the most common solutions to in troduce parallelism has been to extend a sequential language with some sort of parallel version of the for construct, commonly denoted as forall construct. Although similar syntax, these forall loops di er in their semantics and implementations. The High Performance Fortran (HPF) and OpenMP versions are, likely, among the most popular. This paper presents yet another forall loop extension for the C language. In this work, we introduce a parallel computation model: One Thread Multiple Processor Model (OTMP). This model proposes an abstract machine, a programming model and cost model. The programming model de nes another forall loop construct, the theorical machine aims for both homogeneous shared and distributed memory computers, and the cost model allo ws the prediction of the performance of a program. OTMP does not only in tegrates and extends sequential programming, but also includes and expands the message passing programming model. The model allows and exploits any nested levels of parallelism, taking advan tage of situations where there are several small nested loops.Eje: Procesamiento distribuido y paralelo (PDP)Red de Universidades con Carreras en Informática (RedUNCI

Solving parallel problems by OTMP model

Since the early stages of parallel computing, one of the most common solutions to in troduce parallelism has been to extend a sequential language with some sort of parallel version of the for construct, commonly denoted as forall construct. Although similar syntax, these forall loops di er in their semantics and implementations. The High Performance Fortran (HPF) and OpenMP versions are, likely, among the most popular. This paper presents yet another forall loop extension for the C language. In this work, we introduce a parallel computation model: One Thread Multiple Processor Model (OTMP). This model proposes an abstract machine, a programming model and cost model. The programming model de nes another forall loop construct, the theorical machine aims for both homogeneous shared and distributed memory computers, and the cost model allo ws the prediction of the performance of a program. OTMP does not only in tegrates and extends sequential programming, but also includes and expands the message passing programming model. The model allows and exploits any nested levels of parallelism, taking advan tage of situations where there are several small nested loops.Eje: Procesamiento distribuido y paralelo (PDP)Red de Universidades con Carreras en Informática (RedUNCI

Direct Linear Solvers for Vector and Parallel Computers

We consider direct methods for the numerical solution of linear systems with unsymmetric sparse matrices. Different strategies for the determination of the pivots are studied. For solving several linear systems with the same pattern structure we generate a pseudo code, that can be interpreted repeatedly to compute the solutions of these systems. The pseudo code can be advantageously adapted to vector and parallel computers. For that we have to find out the instructions of the pseudo code which are independent of each other. Based on this information, one can determine vector instructions for the pseudo code operations (vectorization) or spread the operations among different processors (parallelization). The methods are successfully used on vector and parallel computers for the circuit simulation of VLSI circuits as well as for the dynamic process simulation of complex chemical production plants

Generic communication in parallel computation

The design of parallel programs requires fancy solutions that are not present in sequential programming. Thus, a designer of parallel applications is concerned with the problem of ensuring the correct behavior of all the processes that the program comprises. There are different solutions to each problem, but the question is to find one, that is general. One possibility is allowing the use of asynchronous groups of processors. We present a general methodology to derive efficient parallel divide and conquer algorithms. Algorithms belonging to this class allow the arbitrary division of the processor subsets, easing the opportunities of the underlying software to divide the network in independent sub networks, minimizing the impact of the traffic in the rest of the network in the predicted cost. This methodology is defined by OTMP model and its expressiveness is exemplified through three divide and conquer programs.Eje: IV - Workshop de procesamiento distribuido y paraleloRed de Universidades con Carreras en Informática (RedUNCI

Generic communication in parallel computation

The design of parallel programs requires fancy solutions that are not present in sequential programming. Thus, a designer of parallel applications is concerned with the problem of ensuring the correct behavior of all the processes that the program comprises. There are different solutions to each problem, but the question is to find one, that is general. One possibility is allowing the use of asynchronous groups of processors. We present a general methodology to derive efficient parallel divide and conquer algorithms. Algorithms belonging to this class allow the arbitrary division of the processor subsets, easing the opportunities of the underlying software to divide the network in independent sub networks, minimizing the impact of the traffic in the rest of the network in the predicted cost. This methodology is defined by OTMP model and its expressiveness is exemplified through three divide and conquer programs.Eje: IV - Workshop de procesamiento distribuido y paraleloRed de Universidades con Carreras en Informática (RedUNCI

A class of parallel multiple-front algorithms on subdomains

A class of parallel multiple-front solution algorithms is developed for solving linear systems arising from discretization of boundary value problems and evolution problems. The basic substructuring approach and frontal algorithm on each subdomain are first modified to ensure stable factorization in situations where ill-conditioning may occur due to differing material properties or the use of high degree finite elements ( p methods). Next, the method is implemented on distributed-memory multiprocessor systems with the final reduced (small) Schur complement problem solved on a single processor. A novel algorithm that implements a recursive partitioning approach on the subdomain interfaces is then developed. Both algorithms are implemented and compared in a least-squares finite-element scheme for viscous incompressible flow computation using h - and p -finite element schemes. Copyright © 2003 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/34536/1/627_ftp.pd

El modelo de computación colectiva: una metodología eficiente para la ampliación del modelo de libreria de paso de mensajes con paralelismo de datos anidados

Se propone el Modelo de Computación Colectiva para la traslación eficiente de algoritmos con paralelismo de datos anidados sobre arquitecturas paralelas reales. El modelo viene caracterizado por una tripleta (M. Div. Col) donde M representa la plataforma paralela, Div es el conjunto de funciones de división y Col el conjunto de funciones colectivas. Una función se dice colectiva cuando es realizada por todos los procesadores del conjunto actual. Los conjuntos de procesadores pueden ser divididos utilizando las funciones de Div. Se hace una propuesta para una implementación eficiente de los procesos de división con la idea subyacente a de que cada uno de los procesadores de uno de los conjuntos producto de la escisión mantiene una relación con uno (o más) de los procesadores en los otros subconjuntos. Esta relación determina las comunicaciones de los resultados producto de la tarea realizada por el conjunto al que el procesador pertenece. Esta estructura de división da lugar a patrones de comunicaciones que se asemejan a los de un hipercubo. La dimensión viene determinada por el número de divisiones demandadas mientras que la aricidad en cada dimensión es igual al número de subconjuntos solicitados. A semejanza de lo que ocurre en un hipercubo k-ario convencional, una dimensión divide al conjunto en k subconjuntos comunicados a través de la dimensión. Sin embargo, los subconjuntos opuestos según una dimensión no tienen porqué tener el mismo cardinal. A estas estructuras resultantes se las ha denominado Hipercubos Dinámicos. Se presenta una clasificación de problemas paralelos en función de las características de los datos de entrada y de salida de los mismos con respecto a la visión que de ellos tienen los procesadores de la máquina. La nomenclatura introducida se utiliza pra caracterizar los problemas que se pesentan en la memoria. Se aportan ejemplos de algoritmos tanto del tipo de los que se han denominado de Computación Colectiva como de Computación Colectiva Común. Este último tipo de algoritmos resuelven un tipo concreto de problemas según la clasificación introducida. Para ambos tipos de algoritmos se estudian diferentes formas de introducir equilibrado de la carga de trabajo y los resultados que produce cada una de ellas. Se presenta también una herramienta, La Laguna C, que representa una implementación concreta de las ideas subyacentes al Modelo de Computación Colectiva y se exponen los resultados computacionales obtenidos para varios algoritmos en diferentes arquitectura