# Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources

arXiv: https://arxiv.org/abs/2303.08803 · HTML: https://mlsanity.com/paper/2303.08803/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 63 references that are in the map.
- First posted on arXiv 15 Mar 2023.

## Builds on (most cited first, 12 of 12)

- [Ray: A Distributed Framework for Emerging AI Applications](https://mlsanity.com/paper/1712.05889/) (2017)
- [Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models](https://mlsanity.com/paper/1811.12823/) (2018)
- [Parsl: Pervasive Parallel Programming in Python](https://mlsanity.com/paper/1905.02158/) (2019)
- [funcX: A Federated Function Serving Fabric for Science](https://mlsanity.com/paper/2005.04215/) (2020)
- [Message-passing neural networks for high-throughput polymer screening](https://mlsanity.com/paper/1807.10363/) (2018)
- [DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding](https://mlsanity.com/paper/1909.07817/) (2019)
- [Enabling Dynamic and Intelligent Workflows for HPC, Data Analytics, and AI Convergence](https://mlsanity.com/paper/2204.09287/) (2022)
- [RADICAL-Cybertools: Middleware Building Blocks for Scalable Science](https://mlsanity.com/paper/1904.03085/) (2019)
- [Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release](https://mlsanity.com/paper/2006.02431/) (2020)
- [HydroNet: Benchmark Tasks for Preserving Intermolecular Interactions and Structural Motifs in Predictive and Generative Models for Molecular Data](https://mlsanity.com/paper/2012.00131/) (2020)
- [A Serverless Tool for Platform Agnostic Computational Experiment Management](https://mlsanity.com/paper/1809.07693/) (2018)
- [AI-coupled HPC Workflows](https://mlsanity.com/paper/2208.11745/) (2022)

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

- [Employing Artificial Intelligence to Steer Exascale Workflows with Colmena](https://mlsanity.com/paper/2408.14434/) (2024)
- [High-Performance Serverless Computing: A Systematic Literature Review on Serverless for HPC, AI, and Big Data](https://mlsanity.com/paper/2601.09334/) (2026)

## Abstract (arXiv)

Applications that fuse machine learning and simulation can benefit from the use of multiple computing resources, with, for example, simulation codes running on highly parallel supercomputers and AI training and inference tasks on specialized accelerators. Here, we present our experiences deploying two AI-guided simulation workflows across such heterogeneous systems. A unique aspect of our approach is our use of cloud-hosted management services to manage challenging aspects of cross-resource authentication and authorization, function-as-a-service (FaaS) function invocation, and data transfer. We show that these methods can achieve performance parity with systems that rely on direct connection between resources. We achieve parity by integrating the FaaS system and data transfer capabilities with a system that passes data by reference among managers and workers, and a user-configurable steering algorithm to hide data transfer latencies. We anticipate that this ease of use can enable routine use of heterogeneous resources in computational science.