# StreamFlow: cross-breeding cloud with HPC

arXiv: https://arxiv.org/abs/2002.01558 · HTML: https://mlsanity.com/paper/2002.01558/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2020.

## Built on by (most cited first, 2 of 2)

- [Bringing AI pipelines onto cloud-HPC: setting a baseline for accuracy of COVID-19 AI diagnosis](https://mlsanity.com/paper/2108.01033/) (2021)
- [Decentralized Time Series Classification with ROCKET Features](https://mlsanity.com/paper/2504.17617/) (2025)

## Abstract (arXiv)

Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments, and that makes it possible the execution onto multiple sites not sharing a common data space. StreamFlow is then exemplified on a novel bioinformatics pipeline for single-cell transcriptomic data analysis workflow.