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3 edition of Graph reduction on shared-memory multiprocessors found in the catalog.

Graph reduction on shared-memory multiprocessors

K. G. Langendoen

Graph reduction on shared-memory multiprocessors

by K. G. Langendoen

  • 367 Want to read
  • 7 Currently reading

Published by Centrum voor Wiskunde en Informatica in Amsterdam, The Netherlands .
Written in English

    Subjects:
  • Multiprocessors.,
  • Memory management (Computer science),
  • Graph grammars.

  • Edition Notes

    Includes bibliographical references (p. [183]-196) and index.

    StatementK.G. Langendoen.
    SeriesCWI tract -- 117.
    The Physical Object
    Paginationv, 199 p. :
    Number of Pages199
    ID Numbers
    Open LibraryOL18096576M
    ISBN 109061964709

    Find helpful customer reviews and review ratings for Lambda-calculus, Combinators and Functional Programming (Cambridge Tracts in Theoretical Computer Science) at Read honest and unbiased product reviews from our users. in memory hierarchy design for shared memory multiprocessors. 1. Figure 1: The Intel Core Duo processor layout. The report is largely based on the material from Hennessy and Patter-son [HP03], Culler, Singh, and Gupta [CSG99], and Adve and Gharachor-loo [AG95]. Other sources are referenced throughout the report.

    Shared memory is very fast, on-chip memory in the SM that threads can use for data interchange within a thread block. Since it is a per-SM resource, shared memory usage can affect occupancy, the number of warps that the SM can keep resident. SMs load and store shared memory with special instructions: G2R/R2G on SM 1.x, and LDS/STS on SM 2.x and. Our graph incorporates the order on operations given by the program text, enabling us to do without locks even when database conflict graphs would suggest that locks are necessary. Our work has implications for the design of multiprocessors; it offers new compiler optimization techniques for parallel languages that support shared variables.

    (DMMs) [82], shared-memory multiprocessors (SMMs) [82], clusters of symmetric multiprocessors (SMPs) [], and networks of workstations (NOWs) [82]. Therefore, their more detailed architectural characteristics must be taken into account. For example, inter-task communication in the form of message-passing or shared-memory access inevitably. Shared memory multiprocessors 2. Non Uniform Memory Access (NUMA): these systems have a shared logical address space, but physical memory is distributed among CPUs, so that access time to data depends on data position, in local or in a remote memory (thus the NUMA denomination) • These systems are also called Distributed Shared Memory (DSM).


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Graph reduction on shared-memory multiprocessors by K. G. Langendoen Download PDF EPUB FB2

This book is the first to provide a coherent review of current research in shared memory multiprocessing in the United States and Japan.

Shared memory multiprocessors are becoming the dominant architecture for small-scale parallel computation. This book is the first to provide a coherent review of current research in shared memory multiprocessing in the United States and Japan. Graph reduction on shared-memory multiprocessors.

Amsterdam, The Netherlands: Centrum voor Wiskunde en Informatica, © (OCoLC) Document Type: Book. FRATS is a strategy for parallel execution of functional languages on shared memory multiprocessors.

It provides fork-join parallelism through the explicit us-age of an annotation to (recursively) spark a set of parallel tasks.

These tasks are executed by ordinary sequential graph reducers which share the program by: 5. A shared-memory multiprocessor is an architecture consisting of a modest number of processors, all of which have direct (hardware) access to all the main memory in the system (Fig.

).This permits any of the system processors to access data that any of the other processors has created or will use. The key to this form of multiprocessor architecture is the interconnection network that. Abstract. Machine for Parallel Graph Reduction [George89] is another shared memory implementation based on the sequential G-machine.

A prototype implementation has been constructed for the BBN Butterfly multiprocessor, which consists of a number (15) of processing elements (MC + 4Mbyte memory) interconnected through a delta : Koen Langendoen.

CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Machine for Parallel Graph Reduction [George89] is another shared memory implementation based on the sequential G-machine.

A prototype implementation has been constructed for the BBN Butterfly multiprocessor, which consists of a number (15) of processing elements (MC + 4Mbyte memory) interconnected. We present a task duplication based scheduling algorithm for shared memory multiprocessors (SMPs), called S2MP (scheduling for SMP), to address the problem of task scheduling.

This algorithm employs heuristics to select duplication of tasks so that schedule length is reduced/minimized. Analytic evaluation of shared-memory systems with ilp processors by Daniel J.

Sorin, Vijay S. Pai, Sarita V. Adve, Mary K. Vernon, David A. Wood - In ISCA ’ Proceedings of the 25th annual international symposium on Computer architecture, shared memory multiprocessors. Section 4 formalizes the definitions of local and global slack and presents an algorithm for computing them.

The offline approach of our analysis requires significant storage space for keeping dynamic information during program executions. To ease this problem, we develop a graph reduction technique in Section 5. The. Part of the Lecture Notes in Computer Science book series (LNCS, volume ) Parallel graph reduction for shared-memory architectures.

PhD thesis, Department of Computing, Imperial College, London, editors, Workshop on Scalable Shared Memory Multiprocessors, Seattle, May, pages –, Boston, Kluwer Academic Publishers. We examine the computational complexity of scheduling problems associated with a certain abstract model of a multiprocessing system.

The essential elements of the model are a finite number of ident. Graph reduction on shared-memory multiprocessors () Pagina-navigatie: Main; Save publication. Save as MODS; Export to Mendeley; Save as EndNote. Graph Reduction Graph reduction [13] is the evaluation method most often used to execute functional programs.

It can be thought of as the graphical equivalent of reduction in the lambda calculus, and supports higher-order func- tions and lazy evaluation in a very natural manner. Home Conferences SC Proceedings SC '19 Understanding priority-based scheduling of graph algorithms on a shared-memory platform research-article Understanding priority-based scheduling of graph algorithms on a shared-memory platform.

Parallel graph reduction is a model for parallel program execution in which shared memory is used under a strict access regime with single assignment and blocking reads. system performance analysis.

The book also applies the synchronization graph model to develop hardware and software optimizations that can significantly reduce the interprocessor communication overhead of a given schedule. This edition updates the background material on existing embedded multiprocessors, including single-chip multiprocessors.

Shared memory multiprocessors are becoming the dominant architecture for small-scale parallel computation. This book is the first to provide a coherent review of current research in shared memory multiprocessing in the United States and Japan.

Shared-memory bus-based systems are described and analyzed in Section 4. Sec-tion 5 concludes the paper. This paper is an extension of previous work on performance analysis of shared-memory bus-based multiprocessors using timed Petri nets [10].

The new contributions include a refined model of pipelined processors which captures. Performance analysis of storage management in combinator graph reduction.

reduction machine. Report on the programming language Haskell – a non-strict purely functional language, version Scheduling performance under the influence of optimisations for shared memory graph reduction. To offset the effect of read miss penalties on processor utilization in shared-memory multiprocessors, several software- and hardware-based data prefetching schemes have been proposed.

The book provides a general introduction to the DSM field as well as a broad survey of the basic DSM concepts, mechanisms, design issues, and systems.

Distributed Shared Memory: Concepts and Systems concentrates on basic DSM algorithms, their enhancements, and their performance evaluation. In addition, it details implementations that employ DSM.The worst-case time complexity of algorithms for multiprocessor computers with binary comparisons as the basic operations is investigated.

It is shown that for the problems of finding the [email protected]{osti_, title = {Hypercube multiprocessors }, author = {Heath, M T}, abstractNote = {This book presents papers given at a conference on hypercube multiprocessors. Topics include the following: programming environments, language and data structures; operating systems; performance measurement; communication and architectural issues; and scientific applications.}, doi.