Staged deployment of interactive multi-application HPC workflows

TL;DR

This paper presents a middleware system that simplifies deploying and managing complex, multi-application scientific workflows on HPC systems, enabling advanced neuroscience research and machine learning applications.

Contribution

It introduces a staged, user-centric deployment model that integrates and extends existing workflow management solutions for HPC, tailored to neuroscience and machine learning use cases.

Findings

Supports coupled neuronal simulators with visualization

Enables closed-loop workflows with machine learning and robotics

Reduces deployment complexity for large-scale HPC workflows

Abstract

Running scientific workflows on a supercomputer can be a daunting task for a scientific domain specialist. Workflow management solutions (WMS) are a standard method for reducing the complexity of application deployment on high performance computing (HPC) infrastructure. We introduce the design for a middleware system that extends and combines the functionality from existing solutions in order to create a high-level, staged user-centric operation/deployment model. This design addresses the requirements of several use cases in the life sciences, with a focus on neuroscience. In this manuscript we focus on two use cases: 1) three coupled neuronal simulators (for three different space/time scales) with in-transit visualization and 2) a closed-loop workflow optimized by machine learning, coupling a robot with a neural network simulation. We provide a detailed overview of the application-integrated monitoring in relationship with the HPC job. We present here a novel usage model for large scale interactive multi-application workflows running on HPC systems which aims at reducing the complexity of deployment and execution, thus enabling new science.