Harnessing the Power of Many: Extensible Toolkit for Scalable Ensemble Applications

Many scientific problems require multiple distinct computational tasks to be executed in order to achieve a desired solution. We introduce the Ensemble Toolkit (EnTK) to address the challenges of scale, diversity and reliability they pose. We describe the design and implementation of EnTK, characterize its performance and integrate it with two exemplar use cases: seismic inversion and adaptive analog ensembles. We perform nine experiments, characterizing EnTK overheads, strong and weak scalability, and the performance of the two use case imple-mentations, at scale and on production infrastructures. We show how EnTK meets the following general requirements: (i) imple-menting dedicated abstractions to support the description and execution of ensemble applications; (ii) support for execution on heterogeneous computing infrastructures; (iii) efficient scalability up to O(10⁴) tasks; and (iv) task-level fault tolerance. We discuss novel computational capabilities that EnTK enables and the scientific advantages arising thereof. We propose EnTK as an important addition to the suite of tools in support of production scientific computing.

I. Introduction

Traditionally, advances in high-performance scientific computing have focused on the scale, performance and optimization of an application with a large but single task, and less on applications comprised of multiple tasks. However, many scientific problems are expressed as applications that require multiple distinct computational tasks to be executed in order to achieve a desired solution.