Name
Abstract | ||
|---|---|---|
One key to improving high performance computing (HPC) productivity is to find better ways to measure it. We define productivity in terms of mission goals, i.e. greater productivity means that more science is accomplished with less cost and effort. Traditional software productivity metrics and computing benchmarks have proven inadequate for assessing or predicting such end-to-end productivity. In this paper we introduce a new approach to measuring productivity in HPC applications that addresses both development time and execution time. Our goal is to develop a public repository of effective productivity benchmarks that anyone in the HPC community can apply to assess or predict productivity. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1177/1094342004048539 | IJHPCA |
Keywords | Field | DocType |
productivity | Industrial engineering,Supercomputer,Computer science,Execution time,Software productivity,Productivity model,Management science,Distributed computing | Journal |
Volume | Issue | ISSN |
18 | 4 | 1094-3420 |
Citations | PageRank | References |
14 | 1.04 | 12 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stuart R. Faulk | 1 | 143 | 27.68 |
| John Gustafson | 2 | 52 | 9.54 |
| Philip M. Johnson | 3 | 591 | 75.86 |
| Adam Porter | 4 | 2159 | 196.52 |
| Walter F. Tichy | 5 | 2546 | 438.90 |
| Lawrence Votta | 6 | 195 | 19.06 |