# RADICAL-Pilot and Parsl: Executing Heterogeneous Workflows on HPC Platforms

arXiv: https://arxiv.org/abs/2105.13185 · HTML: https://mlsanity.com/paper/2105.13185/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2021.

## Built on by (most cited first, 4 of 4)

- [Employing Artificial Intelligence to Steer Exascale Workflows with Colmena](https://mlsanity.com/paper/2408.14434/) (2024)
- [AAFLOW: Scalable Patterns for Agentic AI Workflows](https://mlsanity.com/paper/2605.02162/) (2026)
- [OpRAG: A Resource-Deterministic Runtime for GPU-Backed Multi-Stage RAG Workflows](https://mlsanity.com/paper/2608.08340/) (2026)
- [Scalable Runtime Architecture for Data-driven, Hybrid HPC and ML Workflow Applications](https://mlsanity.com/paper/2503.13343/) (2025)

## 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