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| Hauptseite > Publikationsdatenbank > A Comparison of QMR, CGS and TFQMR on a Distributed Memory Machine > print |
| Hauptseite > Publikationsdatenbank > A Comparison of QMR, CGS and TFQMR on a Distributed Memory Machine > print |
| 001 | 190257 | ||
| 005 | 20210129215539.0 | ||
| 037 | _ | _ | |a FZJ-2015-03170 |
| 088 | 1 | _ | |a KFA-ZAM-IB-9412 |
| 088 | _ | _ | |a KFA-ZAM-IB-9412 |2 JUEL |
| 100 | 1 | _ | |a Bücker, Martin |0 P:(DE-HGF)0 |b 0 |
| 245 | _ | _ | |a A Comparison of QMR, CGS and TFQMR on a Distributed Memory Machine |
| 260 | _ | _ | |a Jülich |c 1994 |b Zentralinstitut für Angewandte Mathematik |
| 300 | _ | _ | |a 14 p. |
| 336 | 7 | _ | |a Report |b report |m report |0 PUB:(DE-HGF)29 |s 1431524990_1323 |2 PUB:(DE-HGF) |
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| 336 | 7 | _ | |a report |2 DRIVER |
| 520 | _ | _ | |a For the solution of systems of linear equations with general non-Hermitian nonsingular coefficient matrices, an implementation of three different algorithms on a parallel machine with distributed memory is proposed. Each of the three algorithms, QMR, CGS and TFQMR, contains two matrix-vector products that dominate the execution time. While the matrix-vector products of CGS and TFQMR are dependent this is not valid for QMR. The two matrix-vector products of QMR can be computed simultaneously. This paper shows how the performance of a parallel implementation is increased by exploiting this property. Timing results of all three algorithms on an Intel PARAGON XP/S 10 system are presented. |
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| 700 | 1 | _ | |a Basermann, Achim |0 P:(DE-HGF)0 |b 1 |
| 773 | _ | _ | |y 1994 |
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