Review and analysis of dense linear system solver package for distributed memory machines

A dense linear system solver package recently developed at the University of Texas at Austin for distributed memory machine (e.g. Intel Paragon) has been reviewed and analyzed. The package contains about 45 software routines, some written in FORTRAN, and some in C-language, and forms the basis for parallel/distributed solutions of systems of linear equations encountered in many problems of scientific and engineering nature. The package, being studied by the Computer Applications Branch of the Analysis and Computation Division, may provide a significant computational resource for NASA scientists and engineers in parallel/distributed computing. Since the package is new and not well tested or documented, many of its underlying concepts and implementations were unclear; our task was to review, analyze, and critique the package as a step in the process that will enable scientists and engineers to apply it to the solution of their problems. All routines in the package were reviewed and analyzed. Underlying theory or concepts which exist in the form of published papers or technical reports, or memos, were either obtained from the author, or from the scientific literature; and general algorithms, explanations, examples, and critiques have been provided to explain the workings of these programs. Wherever the things were still unclear, communications were made with the developer (author), either by telephone or by electronic mail, to understand the workings of the routines. Whenever possible, tests were made to verify the concepts and logic employed in their implementations. A detailed report is being separately documented to explain the workings of these routines

Brze paralelne molekularne simulacije

We have developed and built several clusters of Personal Computers (PCs) that we use to perform parallel molecular simulations of chemically, physically, and biologically relevant systems. We describe the distinguishing networking topology of our clusters that enable them to perform classical and quantum mechanical computer simulations faster than standard PC clusters. Several techniques that we have used in parallelizing simulation programs are described. We employed these clusters for simulations of several different molecular systems. Also the computational performance of these simulations on our PC clusters is presented.Razvijeno je i izgrađeno par grozdova osobnih računala (PC) za izvođenje paralelnih molekularnih simulacija raznih kemijskih, fizikalnih i biološki relevantnih sustava. Osebujna topologija umrežavanja ovih grozdova je, u odnosu na standardne PC grozdove, u stanju brže izvoditi klasične i kvantnomehaničke simulacije. Opisano je više tehnika za paraleliziranje simulacijskih programa koji su zatim primjenjeni na niz molekularnih sustava. Diskutirana je tako|er računalna učinkovitost simulacija na predlo`enim PC grozdovima

Scheduling Associative Reductions with Homogeneous Costs when Overlapping Communications and Computations

Reduction is a core operation in parallel computing. Optimizing its cost has a high potential impact on the application execution time, particularly in MPI and MapReduce computations. In this paper, we propose an optimal algorithm for scheduling associative reductions. We focus on the case where communications and computations can be overlapped to fully exploit resources. Our algorithm greedily builds a spanning tree by starting from the sink and by adding a parent at each iteration. Bounds on the completion time of optimal schedules are then characterized. To show the algorithm extensibility, we adapt it to model variations in which either communication or computation resources are limited. Moreover, we study two specific spanning trees: while the binomial tree is optimal when there is either no transfer or no computation, the Fibonacci tree is optimal when the transfer cost is equal to the computation cost. Finally, approximation ratios of strategies that are derived from those trees are drawn.L'opération de réduction est centrale au calcul parallèle. Optimiser son coût peut avoir un fort impact sur le temps d'exécution d'une application, en particulier dans le cas de MPI ou de MapReduce. Dans ce rapport, nous proposons une solution optimale pour ordonnancer des réductions associatives. Nous considérons que les communications et les calculs peuvent se recouvrir afin d'exploiter pleinement les ressources. Notre algorithme construit gloutonnement un arbre couvrant en commençant par le puits et en rajoutant un parent à chaque itération. Des bornes sur les temps d'exécution d'ordonnancements optimaux sont ensuite caractérisées. Pour montrer l'extensibilité de l'algorithme, nous l'adaptons à des variations du modèles dans lesquelles les communications ou les calculs sont limités. D'autre part, nous étudions deux arbres couvrants spécifiques: tandis que l'arbre binomial est optimal lorsqu'il n'y a soit aucun calcul, soit aucune communication, l'arbre de Fibonacci est optimal lorsque les temps de transfert et les temps de calcul sont égaux. Finalement, les facteurs d'approximation des stratégies dérivées de ces arbres sont déterminés

The 1993 NASA-ODU American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program

Since 1964, the National Aeronautics and Space Administration has supported a program of summer faculty fellowships for engineering and science educators. In a series of collaborations between NASA research and development centers and nearby universities, engineering faculty members spend 10 weeks working with professional peers on research. The Summer Faculty Program Committee of the American Society for Engineering Education supervises the programs. Objectives are: to further the professional knowledge of qualified engineering and science faculty members; to stimulate and exchange ideas between participants and NASA; to enrich and refresh the research and teaching activities of participants' institutions; and to contribute to the research objectives of the NASA center