Abstract
Particle advection is a foundational algorithm for analyzing a flow field. The commonly used Parallelization-Over-Data (POD) strategy for particle advection can become slow and inefficient when there are unbalanced workloads, which are particularly prevalent in in situ workflows. In this work, we present an in situ workflow containing workload estimation for block assignment and duplication in a parallelization-over-data algorithm. With tightly coupled workload estimation and load-balanced block assignment strategy, our workflow offers a considerable improvement over the traditional round-robin block assignment strategy. Our experiments demonstrate that particle advection is up to 3X faster and associated workflow saves approximately 30% of execution time after adopting strategies presented in this work.
| Original language | English |
|---|---|
| Journal | Computer Graphics Forum |
| DOIs | |
| State | Accepted/In press - 2025 |
Funding
This work was supported by the National Key Research and Development Program of China (Grant No.2023YFB3002500) and the National Natural Science Foundation of China (No.62202446), June 2024\u2010present. Prior to June 2024, this research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE\u2010AC05\u201000OR22725. This work was supported in part by the U.S. Department of Energy (DOE) RAPIDS SciDAC project under contract number DE\u2010AC05\u201000OR22725.
Keywords
- CCS Concepts
- • Computing methodologies → Massively parallel algorithms