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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.
Fast Sparse Matrix-Vector Multiplication on GPUs for Graph Applications
SESSION: Numerical Kernels
EVENT TYPE: Papers
TIME: 11:00AM - 11:30AM
SESSION CHAIR: Kirk E. Jordan
AUTHOR(S):Arash Ashari, Naser Sedaghati, John Eisenlohr, Srinivasan Parthasarathy, P. Sadayappan
ROOM:391-92
ABSTRACT:
Sparse matrix-vector multiplication (SpMV) is a widely used
computational kernel. In this paper, we present ACSR, an adaptive SpMV algorithm that uses
the standard CSR format but reduces thread divergence by combining
rows into groups (bins) which have a similar number of non-zero
elements. Further, for rows in bins that span a wide range of non
zero counts, dynamic parallelism is leveraged. A significant benefit
of ACSR over other proposed SpMV approaches is that it works directly with the
standard CSR format, and thus avoids significant pre-processing
overheads. A CUDA implementation of ACSR is shown to outperform SpMV
implementations in the NVIDIA CUSP and cuSPARSE libraries on a set of
sparse matrices representing power-law graphs. We also demonstrate the
use of ACSR for the analysis of dynamic graphs, where the improvement
over extant approaches is even higher.
Chair/Author Details:
Kirk E. Jordan (Chair) - IBM Corporation
Arash Ashari - Ohio State University
Naser Sedaghati - Ohio State University
John Eisenlohr - Ohio State University
Srinivasan Parthasarathy - Ohio State University
P. Sadayappan - Ohio State University
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