Name
Abstract | ||
|---|---|---|
This work compares the two major paradigms for doing in situ visualization: in-line, where the simulation and visualization share the same resources, and in-transit, where simulation and visualization are given dedicated resources. Our runs vary many parameters, including simulation cycle time, visualization frequency, and dedicated resources, to study how tradeoffs change over configuration. In particular, we consider simulations as large as 1,024 nodes (16,384 cores) and dedicated visualization resources with as many as 512 nodes (8,192 cores). We draw conclusions about when each paradigm is superior, such as in-line being superior when the simulation cycle time is very fast. Surprisingly, we also find that in-transit can minimize the total resources consumed for some configurations, since it can cause the visualization routines to require fewer overall resources when they run at lower concurrency. For example, one of our scenarios finds that allocating 25% more resources for visualization allows the simulation to run 61% faster than its in-line comparator. Finally, we explore various models for quantifying the cost for each paradigm, and consider transition points when one paradigm is superior to the other. Our contributions inform design decisions for simulation scientists when performing in situ visualization. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-20656-7_6 | HIGH PERFORMANCE COMPUTING, ISC HIGH PERFORMANCE 2019 |
DocType | Volume | ISSN |
Conference | 11501 | 0302-9743 |
Citations | PageRank | References |
4 | 0.40 | 0 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| James Kress | 1 | 5 | 1.76 |
| Matthew Larsen | 2 | 33 | 4.64 |
| Jong Youl Choi | 3 | 309 | 26.54 |
| Mark Kim | 4 | 24 | 4.80 |
| Matthew Wolf | 5 | 575 | 39.27 |
| Norbert Podhorszki | 6 | 1046 | 83.84 |
| Scott Klasky | 7 | 1547 | 99.00 |
| Hank Childs | 8 | 264 | 33.50 |
| Dave Pugmire | 9 | 152 | 18.62 |