Extensions to the SENSEI In situ Framework for Heterogeneous Architectures

TL;DR

This paper extends the SENSEI in situ framework to better support heterogeneous architectures like GPUs and accelerators, addressing challenges in data management and execution to improve performance in complex supercomputing systems.

Contribution

It introduces new data and execution model extensions to SENSEI, enabling more effective in situ coupling on heterogeneous architectures.

Findings

Enhanced data and execution models for heterogeneous systems

Analysis of in situ placement and execution configurations

Performance improvements demonstrated on GPU-accelerated systems

Abstract

The proliferation of GPUs and accelerators in recent supercomputing systems, so called heterogeneous architectures, has led to increased complexity in execution environments and programming models as well as to deeper memory hierarchies on these systems. In this work, we discuss challenges that arise in in situ code coupling on these heterogeneous architectures. In particular, we present data and execution model extensions to the SENSEI in situ framework that are targeted at the effective use of systems with heterogeneous architectures. We then use these new data and execution model extensions to investigate several in situ placement and execution configurations and to analyze the impact these choices have on overall performance.