TY - GEN
T1 - A supernodal out-of-core sparse Gaussian-elimination method
AU - Toledo, Sivan
AU - Uchitel, Anatoli
N1 - Funding Information:
This research was supported by an IBM Faculty Partnership Award, by grant 848/04 from the Israel Science Foundation (founded by the Israel Academy of Sciences and Humanities), and by grant 2002261 from the United-States-Israel Binational Science Foundation.
PY - 2008
Y1 - 2008
N2 - We present an out-of-core sparse direct solver for unsymmetric linear systems. The solver factors the coefficient matrix A into A∈=∈PLU using Gaussian elimination with partial pivoting. It assumes that A fits within main memory, but it stores the L and U factors on disk (that is, in files). Experimental results indicate that on small to moderately-large matrices (whose factors fit or almost fit in main memory), our code achieves high performance, comparable to that of SuperLU. In some of these cases it is somewhat slower than SuperLU due to overheads associated with the out-of-core behavior of the algorithm (in particular the fact that it always writes the factors to files), but not by a large factor. But in other cases it is faster than SuperLU, probably due to more efficient use of the cache. However, it is able to factor matrices whose factors are much larger than main memory, although at lower computational rates.
AB - We present an out-of-core sparse direct solver for unsymmetric linear systems. The solver factors the coefficient matrix A into A∈=∈PLU using Gaussian elimination with partial pivoting. It assumes that A fits within main memory, but it stores the L and U factors on disk (that is, in files). Experimental results indicate that on small to moderately-large matrices (whose factors fit or almost fit in main memory), our code achieves high performance, comparable to that of SuperLU. In some of these cases it is somewhat slower than SuperLU due to overheads associated with the out-of-core behavior of the algorithm (in particular the fact that it always writes the factors to files), but not by a large factor. But in other cases it is faster than SuperLU, probably due to more efficient use of the cache. However, it is able to factor matrices whose factors are much larger than main memory, although at lower computational rates.
UR - https://www.scopus.com/pages/publications/45449117085
U2 - 10.1007/978-3-540-68111-3_76
DO - 10.1007/978-3-540-68111-3_76
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AN - SCOPUS:45449117085
SN - 3540681051
SN - 9783540681052
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 728
EP - 737
BT - Parallel Processing and Applied Mathematics - 7th International Conference, PPAM 2007, Revised Selected Papers
PB - Springer Verlag
T2 - 7th International Conference on Parallel Processing and Applied Mathematics, PPAM 2007
Y2 - 9 September 2007 through 12 September 2007
ER -