Name
Abstract | ||
|---|---|---|
Rapid growth in scientific data and a widening gap between computational speed and I/O bandwidth make it increasingly infeasible to store and share all data produced by scientific simulations. Instead, we need methods for reducing data volumes: ideally, methods that can scale data volumes adaptively so as to enable negotiation of performance and fidelity tradeoffs in different situations. Multigri... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IPDPS49936.2021.00095 | 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS) |
Keywords | DocType | ISSN |
Distributed processing,Computational modeling,Graphics processing units,Data visualization,Distributed databases,Throughput,Supercomputers | Conference | 1530-2075 |
ISBN | Citations | PageRank |
978-1-6654-4066-0 | 1 | 0.35 |
References | Authors | |
0 | 11 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jieyang Chen | 1 | 12 | 2.58 |
| Lipeng Wan | 2 | 25 | 3.81 |
| Xin Liang | 3 | 4 | 2.07 |
| Ben Whitney | 4 | 19 | 4.38 |
| Qing Liu | 5 | 31 | 2.95 |
| Dave Pugmire | 6 | 152 | 18.62 |
| Nicholas Thompson | 7 | 1 | 1.37 |
| Matthew Wolf | 8 | 575 | 39.27 |
| Todd Munson | 9 | 236 | 15.43 |
| Ian T. Foster | 10 | 23 | 5.59 |
| Scott Klasky | 11 | 1547 | 99.00 |