Name
Abstract | ||
|---|---|---|
It is increasingly difficult for complex scientific programs to attain a significant fraction of peak performance on systems that are based on microprocessors with substantial instruction-level parallelism and deep memory hierarchies. Despite this trend, performance analysis and tuning tools are still not used regularly by algorithm and application designers. To a large extent, existing performance tools fail to meet many user needs and are cumbersome to use. To address these issues, we developed HPCVIEW—a toolkit for combining multiple sets of program profile data, correlating the data with source code, and generating a database that can be analyzed anywhere with a commodity Web browser. We argue that HPCVIEW addresses many of the issues that have limited the usability and the utility of most existing tools. We originally built HPCVIEW to facilitate our own work on data layout and optimizing compilers. Now, in addition to daily use within our group, HPCVIEW is being used by several code development teams in DoD and DoE laboratories as well as at NCSA. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1023/A:1015789220266 | The Journal of Supercomputing |
Keywords | Field | DocType |
performance evaluation,software performance,binary analysis,software tools | Source code,Computer science,Top-down and bottom-up design,Usability,Parallel computing,Compiler,Software performance testing,Hierarchy,Code development,Program profile,Distributed computing | Journal |
Volume | Issue | ISSN |
23 | 1 | 1573-0484 |
Citations | PageRank | References |
53 | 5.36 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| John Mellor-Crummey | 1 | 53 | 5.36 |
| Robert J. Fowler | 2 | 1088 | 213.11 |
| Gabriel Marin | 3 | 698 | 35.97 |
| Nathan R. Tallent | 4 | 335 | 25.06 |