# Introducing SWIRL: An Intermediate Representation Language for Scientific Workflows

arXiv: https://arxiv.org/abs/2407.01838 · HTML: https://mlsanity.com/paper/2407.01838/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2024.

## Built on by (most cited first, 1 of 1)

- [Constructing Executable Analytical Knowledge Representations for Meta-Analysis Synthesis Using an Agentic Harness](https://mlsanity.com/paper/2608.01711/) (2026)

## Abstract (arXiv)

In the ever-evolving landscape of scientific computing, properly supporting the modularity and complexity of modern scientific applications requires new approaches to workflow execution, like seamless interoperability between different workflow systems, distributed-by-design workflow models, and automatic optimisation of data movements. In order to address this need, this article introduces SWIRL, an intermediate representation language for scientific workflows. In contrast with other product-agnostic workflow languages, SWIRL is not designed for human interaction but to serve as a low-level compilation target for distributed workflow execution plans. The main advantages of SWIRL semantics are low-level primitives based on the send/receive programming model and a formal framework ensuring the consistency of the semantics and the specification of translating workflow models represented by Directed Acyclic Graphs (DAGs) into SWIRL workflow descriptions. Additionally, SWIRL offers rewriting rules designed to optimise execution traces, accompanied by corresponding equivalence. An open-source SWIRL compiler toolchain has been developed using the ANTLR Python3 bindings.