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    Performance Evaluation of Computation and Communication Kernels of the Fast Multipole Method on Intel Manycore Architecture

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    Type
    Conference Paper
    Authors
    AbdulJabbar, Mustafa Abdulmajeed
    Al Farhan, Mohammed cc
    Yokota, Rio cc
    Keyes, David E. cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Extreme Computing Research Center
    Date
    2017-08-01
    Online Publication Date
    2017-08-01
    Print Publication Date
    2017
    Permanent link to this record
    http://hdl.handle.net/10754/625714
    
    Metadata
    Show full item record
    Abstract
    Manycore optimizations are essential for achieving performance worthy of anticipated exascale systems. Utilization of manycore chips is inevitable to attain the desired floating point performance of these energy-austere systems. In this work, we revisit ExaFMM, the open source Fast Multiple Method (FMM) library, in light of highly tuned shared-memory parallelization and detailed performance analysis on the new highly parallel Intel manycore architecture, Knights Landing (KNL). We assess scalability and performance gain using task-based parallelism of the FMM tree traversal. We also provide an in-depth analysis of the most computationally intensive part of the traversal kernel (i.e., the particle-to-particle (P2P) kernel), by comparing its performance across KNL and Broadwell architectures. We quantify different configurations that exploit the on-chip 512-bit vector units within different task-based threading paradigms. MPI communication-reducing and NUMA-aware approaches for the FMM’s global tree data exchange are examined with different cluster modes of KNL. By applying several algorithm- and architecture-aware optimizations for FMM, we show that the N-Body kernel on 256 threads of KNL achieves on average 2.8× speedup compared to the non-vectorized version, whereas on 56 threads of Broadwell, it achieves on average 2.9× speedup. In addition, the tree traversal kernel on KNL scales monotonically up to 256 threads with task-based programming models. The MPI-based communication-reducing algorithms show expected improvements of the data locality across the KNL on-chip network.
    Citation
    Abduljabbar M, Al Farhan M, Yokota R, Keyes D (2017) Performance Evaluation of Computation and Communication Kernels of the Fast Multipole Method on Intel Manycore Architecture. Euro-Par 2017: Parallel Processing: 553–564. Available: http://dx.doi.org/10.1007/978-3-319-64203-1_40.
    Publisher
    Springer Nature
    Journal
    Euro-Par 2017: Parallel Processing
    Conference/Event name
    23rd International Conference on Parallel and Distributed Computing, Euro-Par 2017
    DOI
    10.1007/978-3-319-64203-1_40
    Additional Links
    https://link.springer.com/chapter/10.1007%2F978-3-319-64203-1_40
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-319-64203-1_40
    Scopus Count
    Collections
    Conference Papers; Applied Mathematics and Computational Science Program; Extreme Computing Research Center; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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