An efficient parallel algorithm for O( N2) direct summation method and its variations on distributed-memory parallel machines
We present a novel, highly efficient algorithm to parallelize O( N 2) direct summation method for N-body problems with individual timesteps on distributed-memory parallel machines such as Beowulf clusters. Previously known algorithms, in which all processors have complete copies of the N-body system...
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| Vydané v: | New astronomy Ročník 7; číslo 7; s. 373 - 384 |
|---|---|
| Hlavný autor: | |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier B.V
01.10.2002
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| ISSN: | 1384-1076, 1384-1092 |
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| Abstract | We present a novel, highly efficient algorithm to parallelize
O(
N
2) direct summation method for
N-body problems with individual timesteps on distributed-memory parallel machines such as Beowulf clusters. Previously known algorithms, in which all processors have complete copies of the
N-body system, has the serious problem that the communication–computation ratio increases as we increase the number of processors, since the communication cost is independent of the number of processors. In the new algorithm,
p processors are organized as a
p
×
p
two-dimensional array. Each processor has
N/
p
particles, but the data are distributed in such a way that complete system is presented if we look at any row or column consisting of
p
processors. In this algorithm, the communication cost scales as
N/
p
, while the calculation cost scales as
N
2/
p. Thus, we can use a much larger number of processors without losing efficiency compared to what was practical with previously known algorithms. |
|---|---|
| AbstractList | We present a novel, highly efficient algorithm to parallelize
O(
N
2) direct summation method for
N-body problems with individual timesteps on distributed-memory parallel machines such as Beowulf clusters. Previously known algorithms, in which all processors have complete copies of the
N-body system, has the serious problem that the communication–computation ratio increases as we increase the number of processors, since the communication cost is independent of the number of processors. In the new algorithm,
p processors are organized as a
p
×
p
two-dimensional array. Each processor has
N/
p
particles, but the data are distributed in such a way that complete system is presented if we look at any row or column consisting of
p
processors. In this algorithm, the communication cost scales as
N/
p
, while the calculation cost scales as
N
2/
p. Thus, we can use a much larger number of processors without losing efficiency compared to what was practical with previously known algorithms. |
| Author | Makino, Junichiro |
| Author_xml | – sequence: 1 givenname: Junichiro surname: Makino fullname: Makino, Junichiro email: makino@astron.s.u-tokyo.ac.jp organization: Department of Astronomy, School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan |
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| Cites_doi | 10.1038/345033a0 10.1109/71.663861 10.1016/0021-9991(73)90160-5 10.1086/191306 10.1093/mnras/126.3.223 10.1086/316455 |
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| Title | An efficient parallel algorithm for O( N2) direct summation method and its variations on distributed-memory parallel machines |
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