High-Performance Distributed Multi-Model / Multi-Kernel Simulations: A Case-Study in Jungle Computing

TL;DR

This paper explores leveraging heterogeneous Jungle Computing Systems to run complex Multi-Model / Multi-Kernel simulations, demonstrating a prototype implementation that integrates diverse resources for scientific applications.

Contribution

It presents a Jungle-aware version of the astrophysical simulation framework AMUSE, enabling multi-resource utilization for advanced simulations.

Findings

Prototype system successfully integrates multiple distributed resources.

Preliminary experiments show feasibility of multi-resource astrophysical simulations.

Demonstrates potential for improved performance in scientific modeling.

Abstract

High-performance scientific applications require more and more compute power. The concurrent use of multiple distributed compute resources is vital for making scientific progress. The resulting distributed system, a so-called Jungle Computing System, is both highly heterogeneous and hierarchical, potentially consisting of grids, clouds, stand-alone machines, clusters, desktop grids, mobile devices, and supercomputers, possibly with accelerators such as GPUs. One striking example of applications that can benefit greatly of Jungle Computing Systems are Multi-Model / Multi-Kernel simulations. In these simulations, multiple models, possibly implemented using different techniques and programming models, are coupled into a single simulation of a physical system. Examples include the domain of computational astrophysics and climate modeling. In this paper we investigate the use of Jungle Computing Systems for such Multi-Model / Multi-Kernel simulations. We make use of the software developed in the Ibis project, which addresses many of the problems faced when running applications on Jungle Computing Systems. We create a prototype Jungle-aware version of AMUSE, an astrophysical simulation framework. We show preliminary experiments with the resulting system, using clusters, grids, stand-alone machines, and GPUs.