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SCHEDULE: NOV 16-21, 2014
When viewing the Technical Program schedule, on the far righthand side is a column labeled "PLANNER." Use this planner to build your own schedule. Once you select an event and want to add it to your personal schedule, just click on the calendar icon of your choice (outlook calendar, ical calendar or google calendar) and that event will be stored there. As you select events in this manner, you will have your own schedule to guide you through the week.
Performance of Sparse Matrix-Multiple Vectors Multiplication on Multicore and GPUs
SESSION: Poster Reception
EVENT TYPE: Posters
TIME: 5:15PM - 7:00PM
AUTHOR(S):Walid Abu-Sufah, Khalid Ahmad
ROOM:New Orleans Theater Lobby
ABSTRACT:
Sparse matrix-vector and multiple-vector multiplications (SpMV and SpMM) are performance bottlenecks in numerous applications. We implemented two SpMM kennels to integrate in our library of auto-tuned kernels for GPUs. Our kernels use registers to exploit data reuse in SpMM. DIA-SpMM targets structured matrices and ELL-SpMM targets matrices with uniform row lengths. Work is continuing on SpMM kernels for unstructured matrices.
Executing on NVIDIA Kepler Tesla K40m, DIA-SpMM is 2.4x faster than NVIDIA CUSP DIA-SpMV. ELL-SpMM is 2.8x faster than CUSP ELL-SpMV.
DIA-SpMM is 5.2x faster than the highly optimized NVIDIA CUSPARSE CSR-SpMV. The maximum speedup is 6.5x. ELL-SpMM is 3.9x faster than CUSPARSE CSR-SpMV. The maximum speedup is 8.3x.
DIA-SpMM is 2x faster than CUSPARSE CSR-SpMM. ELL-SpMM is 1.6x faster.
For structured matrices, DIA-SpMM on the K40m GPU is 7.2x faster than Intel MKL CSR-SpMV on a dual socket 10-core Intel Ivy Bridge E5-2690. The maximum speedup is 12.3x.
Chair/Author Details:
Walid Abu-Sufah - University of Illinois at Urbana-Champaign and University of Jordan
Khalid Ahmad - University of Jordan
Click here to download .ics calendar file
