RADICAL-Pilot and Parsl: Executing Heterogeneous Workflows on HPC Platforms
Not on the map. Why?
Abstract (arXiv)
Workflows applications are becoming increasingly important to support scientific discovery. That is leading to a proliferation of workflow management systems and, thus, to a fragmented software ecosystem. Integration among existing workflow tools can improve development efficiency and, ultimately, increase the sustainability of scientific workflow software. We describe our experience with integrating RADICAL-Pilot (RP) and Parsl as a way to enable users to develop and execute workflow applications with heterogeneous tasks on heterogeneous high-performance computing resources. We describe our approach to the integration of the two systems and detail the development of RPEX, a Parsl executor which uses RP as its workload manager. We develop an RP executor that executes heterogeneous MPI Python functions on CPU cores and GPUs. We measure the weak and strong scaling of RPEX, RP, and Parsl when providing new capabilities to two paradigmatic use cases: Colmena and Ice Wedge Polygons
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 May 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2024: 1; 2025: 1; 2026: 2.
4 papers.
- 2024Employing Artificial Intelligence to Steer Exascale Workflows with Colmenacited by 3
- 2026AAFLOW: Scalable Patterns for Agentic AI Workflowscited by 2
- 2026OpRAG: A Resource-Deterministic Runtime for GPU-Backed Multi-Stage RAG Workflowscited by 1
- 2025Scalable Runtime Architecture for Data-driven, Hybrid HPC and ML Workflow Applicationscited by 0
- 2026OpRAG: A Resource-Deterministic Runtime for GPU-Backed Multi-Stage RAG Workflows8 Aug 2026
- 2026AAFLOW: Scalable Patterns for Agentic AI Workflows4 May 2026
- 2025Scalable Runtime Architecture for Data-driven, Hybrid HPC and ML Workflow Applications17 Mar 2025
- 2024Employing Artificial Intelligence to Steer Exascale Workflows with Colmena26 Aug 2024