Co-Scheduling Algorithms for High-Throughput Workload Execution

TL;DR

This paper explores co-scheduling algorithms for parallel applications, aiming to optimize execution by concurrently running multiple applications in packs, thereby improving throughput and energy efficiency.

Contribution

It introduces a novel co-scheduling approach with heuristics for partitioning applications into packs, optimizing resource allocation and execution time.

Findings

Co-scheduling reduces workload completion time.

Heuristics perform well across diverse workloads.

Significant improvements in system throughput and energy savings.

Abstract

This paper investigates co-scheduling algorithms for processing a set of parallel applications. Instead of executing each application one by one, using a maximum degree of parallelism for each of them, we aim at scheduling several applications concurrently. We partition the original application set into a series of packs, which are executed one by one. A pack comprises several applications, each of them with an assigned number of processors, with the constraint that the total number of processors assigned within a pack does not exceed the maximum number of available processors. The objective is to determine a partition into packs, and an assignment of processors to applications, that minimize the sum of the execution times of the packs. We thoroughly study the complexity of this optimization problem, and propose several heuristics that exhibit very good performance on a variety of workloads, whose application execution times model profiles of parallel scientific codes. We show that co-scheduling leads to to faster workload completion time and to faster response times on average (hence increasing system throughput and saving energy), for significant benefits over traditional scheduling from both the user and system perspectives.