Robert Preissl, Theodore M. Wong, et al.
SC 2012
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. © 2012 IEEE.
Robert Preissl, Theodore M. Wong, et al.
SC 2012
Erich P. Stuntebeck, John S. Davis II, et al.
HotMobile 2008
Pradip Bose
VTS 1998
Raymond Wu, Jie Lu
ITA Conference 2007