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Scientific Workflow Intermediate Representation Language

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# SWIRL: Scientific Workflow Intermediate Representation Language

SWIRL is a low-level intermediate representation language for distributed scientific workflows. Unlike other product-agnostic workflow languages, SWIRL is not intended for human interaction but serves as a low-level compilation target for distributed workflow execution plans. It models the execution plan of a location-aware workflow graph as a distributed system with send/receive communication primitives. The optimised SWIRL representation can then be compiled into one or more self-contained executable bundles, making it adaptable to specific execution environments and embracing heterogeneity. This repository contains the reference SWIRL Compiler toolchain, called `swirlc`, written in Python 3 and relying on the [ANTLR](https://www.antlr.org/) parser generator. if you want to cite SWIRL, please use the reference below:

```bibtex
@inproceedings{24:fm:swirl,
    title     = {Introducing SWIRL: An Intermediate Representation Language for Scientific Workflows},
    author    = {Iacopo Colonnelli and
                 Doriana Medi\'{c} and
                 Alberto Mulone and
                 Viviana Bono and
                 Luca Padovani and
                 Marco Aldinucci},
    editor    = {Andr\'{e} Platzer and
                 Kristin Yvonne Rozier and
                 Matteo Pradella and
                 Matteo Rossi},
    doi       = {10.1007/978-3-031-71162-6_12},
    year      = {2025},
    booktitle = {Formal Methods. FM 2024},
    volume    = {14933},
    pages     = {226–-244},
    publisher = {Springer Nature Switzerland},
    address   = {Cham, Switzerland},
    location  = {Milan, Italy},
    series    = {Lecture Notes in Computer Science}
}
```

## Install

### PyPI

The `swirlc` package is available on [PyPi](https://pypi.org/project/swirlc/), so you can install it using pip.

```bash
pip install swirlc
```

Please note that the SWIRL compiler requires `python >= 3.10`. Then you can use it through the `swirlc` CLI.

### Docker

The SWIRL Docker image is available on [Docker Hub](https://hub.docker.com/r/alphaunito/swirlc). The script below gives an example of `swirlc` CLI invocation in a Docker container

```bash
docker run                          \
        --user $(id -u):$(id -g)    \
        --volume $(pwd):/data       \
        --workdir /data             \
        alphaunito/swirlc:latest    \
    swirlc                          \
        COMMAND                     \
        [OPTION]                     
```

## Use SWIRL

The `swirlc` CLI offers two primary functionalities: `translate` and `compile`. The former translates a distributed workflow written in one of the supported high-level languages into a low-level SWIRL intermediate representation. The latter compiles a SWIRL representation into a target executable bundle.

### Translate

The `swirlc translate` command can be used to translate an existing, high-level workflow representation into a SWIRL low-level intermediate representation, usually stored in a `*.swirl` file, and a `*.yml` file containing a set of metadata. As an example, the following command can be used to generate a SWIRL representation from a [DAX](https://pegasus.isi.edu/documentation/development/schemas.html) workflow generated by the [Pegasus WMS](https://pegasus.isi.edu/):
  
```bash
swirlc translate --language dax [DAX_DIRECTORY]
```

Note that the `DAX_DIRECTORY` must contain four files: `replica.yml`, `sites.yml`, `transformations.yml` and `workflow.yml`.

### Compile

The `swirlc compile` command can be used to create executable traces in different programming languages from a SWIRL representation. The result of the command is an executable bundle ready to run on highly-distributed execution environments (e.g., HPC, Cloud, or Edge). As an example, the following command can be used to generate a Python-based executable bundle from a `SWIRL_FILE` and a `METADATA_FILE`:
  
```bash
swirlc compile [SWIRL_FILE] [METADATA_FILE]
```

Note that all the target locations need to have the Python interpreter installed.

## Case studies

In this [artifact](https://zenodo.org/records/12523000), we describe how users can rely on swirlc to reproduce the 1000 Genomes workflow experiment.

## SWIRL Team

Iacopo Colonnelli <iacopo.colonnelli@unito.it> (Designer and maintainer)  
Doriana Medić <doriana.medic@unito.it> (Designer and maintainer)  
Alberto Mulone <alberto.mulone@unito.it> (Maintainer)

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