Abstract
Tightly coupled task-based multiscale models do not scale when implemented using a traditional workflow management system. This is because the fine-grained task parallelism of such applications cannot be exploited efficiently due to scheduling and communication overheads. Existing tools and frameworks allow implementing efficient task-level parallelism, however with high programming effort. On the other hand, Dask and Parsl are Python libraries for low-effort up-scaling of task-parallel applications but still require considerable programming effort and do not equally provide functions for optimal task scheduling. By extending the wfGenes tool with new generators and a static task graph scheduler, we enhance Dask and Parsl to tackle these deficiencies and to generate optimized input for these systems from a simple application description and enable rapid design of scalable task-parallel multiscale applications relying on thorough graph analysis and automatic code generation. The performance of the generated code has been analyzed by using random task graphs with up to 10,000 nodes and executed on thousands of CPU cores. The approach implemented in wfGenes is beneficial for improving the usability and increasing the exploitation of existing tools, and for increasing productivity of multiscale modeling scientists.
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Acknowledgment
The authors gratefully acknowledge support by the GRK 2450. This work was partially performed on the HoreKa supercomputer funded by the Ministry of Science, Research and the Arts Baden-Württemberg and by the Federal Ministry of Education and Research. The authors acknowledge support by the state of Baden-Württemberg through bwHPC.
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Roozmeh, M., Kondov, I. (2022). Automating and Scaling Task-Level Parallelism of Tightly Coupled Models via Code Generation. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13353. Springer, Cham. https://doi.org/10.1007/978-3-031-08760-8_6
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