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    The Hybrid Task Graph Scheduler

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    Blattner_umbc_0434D_11563.pdf (2.140Mb)
    Permanent Link
    http://hdl.handle.net/11603/15470
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    • UMBC Theses and Dissertations
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    Author/Creator
    Unknown author
    Date
    2016-01-01
    Type of Work
    Text
    dissertation
    Department
    Computer Science and Electrical Engineering
    Program
    Computer Science
    Rights
    This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
    Distribution Rights granted to UMBC by the author.
    Subjects
    application program interface
    execution pipelines
    high performance computing
    multiple accelerators
    programming model
    task graph scheduling
    Abstract
    Scalability of applications is a key requirement to gaining performance in hybrid and cluster computing. Implementing code to utilize multiple accelerators and CPUs is difficult, particularly when dealing with dependencies, memory management, data locality, and processor occupancy. The Hybrid Task Graph Scheduler (HTGS) is designed to increase programmer productivity to develop applications for single nodes with multiple CPUs and accelerators. Current task graph schedulers provide APIs, directives, and compilers to schedule work on nodes; however, many fail to expose the locality of data and often use a single address space to represent memory resulting in inefficient data transfer patterns for accelerators. HTGS merges dataflow and traditional task graph schedulers into a novel model to assist developers in exposing the parallelism and data locality of their algorithm. With the HTGS model, an algorithm is represented at a high level of abstraction and modularizes the computationally intensive components as a series of concurrent tasks. Using this approach, the model explicitly defines memory for each address space and provides interfaces to express the locality of data between tasks. The result achieves the full performance of the node comparable to the best of breed implementations of algorithms such as matrix multiplication and LU decomposition. The performance gains are demonstrated with a modest effort using the HTGS C++ API, which improves programmer productivity with obtaining that performance.


    Albin O. Kuhn Library & Gallery
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    www.umbc.edu/scholarworks

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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3544


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.