# RADICAL-Cybertools: Middleware Building Blocks for Scalable Science

arXiv: https://arxiv.org/abs/1904.03085 · HTML: https://mlsanity.com/paper/1904.03085/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2019.

## Built on by (most cited first, 6 of 6)

- [Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance Computing](https://mlsanity.com/paper/2110.02827/) (2021)
- [Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulations](https://mlsanity.com/paper/2104.04797/) (2021)
- [Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers](https://mlsanity.com/paper/2103.02843/) (2021)
- [High Throughput Training of Deep Surrogates from Large Ensemble Runs](https://mlsanity.com/paper/2309.16743/) (2023)
- [Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources](https://mlsanity.com/paper/2303.08803/) (2023)
- [Adaptive Protein Design Protocols and Middleware](https://mlsanity.com/paper/2510.06396/) (2025)

## Abstract (arXiv)

RADICAL-Cybertools (RCT) are a set of software systems that serve as middleware to develop efficient and effective tools for scientific computing. Specifically, RCT enable executing many-task applications at extreme scale and on a variety of computing infrastructures. RCT are building blocks, designed to work as stand-alone systems, integrated among themselves or integrated with third-party systems. RCT enables innovative science in multiple domains, including but not limited to biophysics, climate science and particle physics, consuming hundreds of millions of core hours. This paper provides an overview of RCT systems, their impact, and the architectural principles and software engineering underlying RCT