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SCHEDULE: NOV 16-21, 2014
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Parallelization of Reordering Algorithms for Bandwidth and Wavefront Reduction
SESSION: Sparse Solvers
EVENT TYPE: Papers
TIME: 1:30PM - 2:00PM
SESSION CHAIR: Anne C. Elster
AUTHOR(S):Konstantinos I. Karantasis, Andrew Lenharth, Donald Nguyen, Maria Garzaran, Keshav Pingali
ROOM:391-92
ABSTRACT:
Many sparse matrix computations can be speeded up by first suitably reordering the sparse matrix. Reorderings, originally developed to assist direct methods, have recently become popular for improving cache locality of parallel iterative solvers. When sparse matrix-vector product (SpMV), the key kernel in iterative solvers, runs, reorderings targeting bandwidth and wavefront reduction can improve locality of reference to the compressed sparse storage vectors.
In this paper, we present the first parallel implementations of two widely used reordering algorithms: Reverse Cuthill-McKee (RCM) and Sloan. On 16 cores on Stampede supercomputer, our results show average improvement of 5.56X compared to the state-of-the-art sequential RCM implementation in the HSL library. Sloan is significantly more constrained than RCM; our parallel implementation achieves 2.88X average improvement compared to HSL-Sloan. Considering end-to-end times, using our reordering, SpMV obtains performance improvement of 1.5X compared to natural ordering, and 2X compared to HSL RCM for 100 SpMV iterations.
Chair/Author Details:
Anne C. Elster (Chair) - Norwegian University of Science & Technology / University of Texas at Austin
Konstantinos I. Karantasis - University of Illinois at Urbana-Champaign
Andrew Lenharth - University of Texas at Austin
Donald Nguyen - University of Texas at Austin
Maria Garzaran - University of Illinois at Urbana-Champaign
Keshav Pingali - University of Texas at Austin
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