Name
Abstract | ||
|---|---|---|
The theory of bulk-synchronous parallel computing has produced a large number of attractive algorithms, which are provably optimal in some sense, but typically require that the aggregate random access memory (RAM) of the processors be sufficient to hold the entire data set of the parallel problem instance. In this work we investigate the performance of parallel algorithms for extremely large problem instances relative to the available RAM. We describe a system, Parallel External Memory System (PEMS), which allows existing parallel programs designed for a large number of processors without disks to be adapted easily to smaller, realistic numbers of processors, each with its own disk system. Our experiments with PEMS show that this approach is practical and promising and the run times scale predictable with the number of processors and with the problem size. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-77220-0_10 | HiPC |
Keywords | Field | DocType |
realistic number,parallel algorithm,available ram,problem size,pems show,large number,bulk-synchronous parallel computing,large problem instance,parallel program,parallel external memory system,parallel problem instance,bulk synchronous parallel,external memory | Analysis of parallel algorithms,Uniform memory access,Parallel random-access machine,Computer science,Massively parallel,Embarrassingly parallel,Parallel computing,Distributed memory,Bulk synchronous parallel,CUDA Pinned memory,Distributed computing | Conference |
Volume | ISSN | ISBN |
4873 | 0302-9743 | 3-540-77219-7 |
Citations | PageRank | References |
1 | 0.36 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad R. Nikseresht | 1 | 1 | 0.70 |
| David A. Hutchinson | 2 | 124 | 8.73 |
| Anil Maheshwari | 3 | 869 | 104.47 |