Timeline for What's the state of the art in parallel ODE methods?
Current License: CC BY-SA 3.0
9 events
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| Nov 29, 2020 at 18:35 | comment | added | Millemila | NoseKnowsAll I have a question: if one uses domain decomposition (eg METIS) an MPI to parallelize a PDE, you end up with a (usually) linear system of ODE, and if the ODE scheme is time-explicit why one would be interested in parallelize it? All the cores are already used to divide the domain. Maybe for small domains, for which communication is important? If the ODE scheme is implicit, then you end up with linear systems to solve in a parallel fashion by using N processors where N is the number of the Metis domains. So why should I parallelize the ODE? | |
| Oct 30, 2017 at 18:34 | comment | added | NoseKnowsAll | This is a great overview. However it should be explicitly mentioned that ODEs are often solved after a spatial discretization of PDEs. Therefore, parallelism across the method can yield great scalability to thousands of cores if your spatial domain is big enough. This is because the vast majority of compute times goes into the computation of, for instance, the RK stage RHS evaluations. | |
| S Dec 28, 2013 at 20:22 | history | suggested | ЯegDwight | CC BY-SA 3.0 |
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| Dec 28, 2013 at 19:46 | review | Suggested edits | |||
| S Dec 28, 2013 at 20:22 | |||||
| Jun 30, 2013 at 20:53 | history | bounty ended | Florian Brucker | ||
| Jun 30, 2013 at 20:53 | vote | accept | Florian Brucker | ||
| Jun 25, 2013 at 5:25 | comment | added | eccstartup | I am currently learning parareal. And I think it is of much help to me. | |
| Jun 25, 2013 at 4:14 | history | edited | Matthew Emmett | CC BY-SA 3.0 |
added references
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| Jun 25, 2013 at 2:47 | history | answered | Matthew Emmett | CC BY-SA 3.0 |