Revisiting Matrix Product on Master-Worker Platforms

This paper is aimed at designing efficient parallel matrix-product algorithms for heterogeneous master-worker platforms. While matrix-product is well-understood for homogeneous 2D-arrays of processors (e.g., Cannon algorithm and ScaLAPACK outer product algorithm), there are three key hypotheses that render our work original and innovative: - Centralized data. We assume that all matrix files originate from, and must be returned to, the master. - Heterogeneous star-shaped platforms. We target fully heterogeneous platforms, where computational resources have different computing powers. - Limited memory. Because we investigate the parallelization of large problems, we cannot assume that full matrix panels can be stored in the worker memories and re-used for subsequent updates (as in ScaLAPACK). We have devised efficient algorithms for resource selection (deciding which workers to enroll) and communication ordering (both for input and result messages), and we report a set of numerical experiments on various platforms at Ecole Normale Superieure de Lyon and the University of Tennessee. However, we point out that in this first version of the report, experiments are limited to homogeneous platforms

Task matching and scheduling in heterogeneous computing environments using a genetic-algorithm-based approach

Includes bibliographical references (pages 21-22).To exploit a heterogeneous computing (HC) environment, an application task may be decomposed into subtasks that have data dependencies. Subtask matching and scheduling consists of assigning subtasks to machines, ordering subtask execution for each machine, and ordering intermachine data transfers. The goal is to achieve the minimal completion time for the task. A heuristic approach based on a genetic algorithm is developed to do matching and scheduling in HC environments. It is assumed that the matcher/scheduler is in control of a dedicated HC suite of machines. The characteristics of this genetic-algorithm-based approach include: separation of the matching and the scheduling representations, independence of the chromosome structure from the details of the communication subsystem, and consideration of overlap among all computations and communications that obey subtask precedence constraints. It is applicable to the static scheduling of production jobs and can be readily used to collectively schedule a set of tasks that are decomposed into subtasks. Some parameters and the selection scheme of the genetic algorithm were chosen experimentally to achieve the best performance. Extensive simulation tests were conducted. For small-sized problems (e.g., a small number of subtasks and a small number of machines), exhaustive searches were used to verify that this genetic-algorithm-based approach found the optimal solutions. Simulation results for larger-sized problems showed that this genetic-algorithm-based approach outperformed two nonevolutionary heuristics and a random search

Parallel resource co-allocation for the computational grid

Author name used in this publication: K. W. Chau2006-2007 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Mapping of subtasks with multiple versions in a heterogeneous ad hoc grid environment

Includes bibliographical references (pages 7-8).An ad hoc grid is a heterogeneous computing system composed of mobile devices. The problem studied here is to statically assign resources to the subtasks of an application, which has an execution time constraint, when the resources are oversubscribed. Each subtask has a preferred version, and a secondary version that uses fewer resources. The goal is to assign resources so that the application meets its execution time constraint while minimizing the number of secondary versions used. Five resource allocation heuristics to derive near-optimal solutions to this problem are presented and evaluated

GRIDCC - Providing a real-time grid for distributed instrumentation

The GRIDCC project is extending the use of Grid computing to include access to and control of distributed instrumentation. Access to the instruments will be via an interface to a Virtual Instrument Grid Service (VIGS). VIGS is a new concept and its design and implementation, together with middleware that can provide the appropriate Quality of Service (QoS), is a key part of the GRIDCC development plan. An overall architecture for GRIDCC has been defined and some of the application areas, which include distributed power systems, remote control of an accelerator and the remote monitoring of a large particle physics experiment, are briefly discussed.E

Replica Selection in the Globus Data Grid

The Globus Data Grid architecture provides a scalable infrastructure for the management of storage resources and data that are distributed across Grid environments. These services are designed to support a variety of scientific applications, ranging from high-energy physics to computational genomics, that require access to large amounts of data (terabytes or even petabytes) with varied quality of service requirements. By layering on a set of core services, such as data transport, security, and replica cataloging, one can construct various higher-level services. In this paper, we discuss the design and implementation of a high-level replica selection service that uses information regarding replica location and user preferences to guide selection from among storage replica alternatives. We first present a basic replica selection service design, then show how dynamic information collected using Globus information service capabilities concerning storage system properties can help improve and optimize the selection process. We demonstrate the use of Condor's ClassAds resource description and matchmaking mechanism as an efficient tool for representing and matching storage resource capabilities and policies against application requirements.Comment: 8 pages, 6 figure