| Autor: |
Seekhao, N., Yu, G., Yuen, S., JaJa, J., Mongeau, L., Li-Jessen, N. Y. K. |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
PDPTA 19 (2019) |
| Popis: |
High-fidelity numerical simulations produce massive amounts of data. Analyzing these numerical data sets as they are being generated provides useful insights into the processes underlying the modeled phenomenon. However, developing real-time in-situ visualization techniques to process large amounts of data can be challenging since the data does not fit on the GPU, thus requiring expensive CPU-GPU data copies. In this work, we present a scheduling scheme that achieve real-time simulation and interactivity through GPU hyper-tasking. Furthermore, the CPU-GPU communications were minimized using an activity-aware technique to reduce redundant copies. Our simulation platform is capable of visualizing 1.7 billion protein data points in situ, with an average frame rate of 42.8 fps. This performance allows users to explore large data sets on remote server with real-time interactivity as they are performing their simulations. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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