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    High Productivity Programming of Dense Linear Algebra on Heterogeneous NUMA Architectures

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    Rabab_Thesis.pdf
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    Description:
    Rabab Alomairy Thesis
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    Type
    Thesis
    Authors
    Alomairy, Rabab M. cc
    Advisors
    Keyes, David E. cc
    Committee members
    Ltaief, Hatem cc
    Moshkov, Mikhail cc
    Shihada, Basem cc
    Program
    Computer Science
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2013-07
    Embargo End Date
    2014-07-30
    Permanent link to this record
    http://hdl.handle.net/10754/297194
    
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    Access Restrictions
    At the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis became available to the public after the expiration of the embargo on 2014-07-30.
    Abstract
    High-end multicore systems with GPU-based accelerators are now ubiquitous in the hardware landscape. Besides dealing with the nontrivial heterogeneous environ- ment, end users should often take into consideration the underlying memory architec- ture to decrease the overhead of data motion, especially when running on non-uniform memory access (NUMA) platforms. We propose the OmpSs parallel programming model approach using its Nanos++ dynamic runtime system to solve the two challeng- ing problems aforementioned, through 1) an innovative NUMA node-aware scheduling policy to reduce data movement between NUMA nodes and 2) a nested parallelism feature to concurrently exploit the resources available from the GPU devices as well as the CPU host, without compromising the overall performance. Our approach fea- tures separation of concerns by abstracting the complexity of the hardware from the end users so that high productivity can be achieved. The Cholesky factorization is used as a benchmark representative of dense numerical linear algebra algorithms. Superior performance is also demonstrated on the symmetric matrix inversion based on Cholesky factorization, commonly used in co-variance computations in statistics. Performance on a NUMA system with Kepler-based GPUs exceeds that of existing implementations, while the OmpSs-enabled code remains very similar to its original sequential version.
    Citation
    Alomairy, R. M. (2013). High Productivity Programming of Dense Linear Algebra on Heterogeneous NUMA Architectures. KAUST Research Repository. https://doi.org/10.25781/KAUST-J256D
    DOI
    10.25781/KAUST-J256D
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-J256D
    Scopus Count
    Collections
    Theses; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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