SCHEDULE: NOV 10-16, 2012
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Managing Data-Movement for Effective Shared-Memory Parallelization of Out-of-Core Sparse Solvers
SESSION: Linear Algebra Algorithms
EVENT TYPE: Papers
TIME: 4:30PM - 5:00PM
SESSION CHAIR: X. Sherry Li
AUTHOR(S):Haim Avron, Anshul Gupta
ROOM:355-D
ABSTRACT:
Direct methods for solving sparse linear systems are robust and typically
exhibit good performance, but often require large amounts of memory
due to fill-in. Many industrial applications use out-of-core techniques
to mitigate this problem. However, parallelizing sparse out-of-core
solvers poses some unique challenges because accessing secondary storage
introduces serialization and I/O overhead. We analyze the data-movement
costs and memory versus parallelism trade-offs in a shared-memory
parallel out-of-core linear solver for sparse symmetric systems. We
propose an algorithm that uses a novel memory management scheme and
adaptive task parallelism to reduce the data-movement
costs. We present experiments to show that our solver is faster
than existing out-of-core sparse solvers on a single core, and is
more scalable than the only other known shared-memory parallel out-of-core solver. This work is also directly applicable at the node level in
a distributed-memory parallel scenario.
Chair/Author Details:
X. Sherry Li (Chair) - Lawrence Berkeley National Laboratory
Haim Avron - IBM T.J. Watson Research Center
Anshul Gupta - IBM T.J. Watson Research Center
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Managing Data-Movement for Effective Shared-Memory Parallelization of Out-of-Core Sparse Solvers
SESSION: Linear Algebra Algorithms
EVENT TYPE:
TIME: 4:30PM - 5:00PM
SESSION CHAIR: X. Sherry Li
AUTHOR(S):Haim Avron, Anshul Gupta
ROOM:355-D
ABSTRACT:
Direct methods for solving sparse linear systems are robust and typically
exhibit good performance, but often require large amounts of memory
due to fill-in. Many industrial applications use out-of-core techniques
to mitigate this problem. However, parallelizing sparse out-of-core
solvers poses some unique challenges because accessing secondary storage
introduces serialization and I/O overhead. We analyze the data-movement
costs and memory versus parallelism trade-offs in a shared-memory
parallel out-of-core linear solver for sparse symmetric systems. We
propose an algorithm that uses a novel memory management scheme and
adaptive task parallelism to reduce the data-movement
costs. We present experiments to show that our solver is faster
than existing out-of-core sparse solvers on a single core, and is
more scalable than the only other known shared-memory parallel out-of-core solver. This work is also directly applicable at the node level in
a distributed-memory parallel scenario.
Chair/Author Details:
X. Sherry Li (Chair) - Lawrence Berkeley National Laboratory
Haim Avron - IBM T.J. Watson Research Center
Anshul Gupta - IBM T.J. Watson Research Center
Click here to download .ics calendar file