Malleable-Lab: A Tool for Evaluating Adaptive Online Schedulers on Malleable Jobs

—The emergence of multi-core computers has led to explosive development of parallel applications and hence the need of efficient schedulers for parallel jobs. Adaptive online schedulers have recently been proposed to exploit the multiple processor resource and shown good promise in theory. To verify the effectiveness of these parallel schedulers, it will be reassuring to test them extensively with various parallel workloads. Unfortunately it is still unknown how the job mixes will eventually evolve for multi-core computers; moreover, it is also non-obvious how the parallelism of a typical job will look like. To evaluate the dynamic behaviors of an adaptive scheduler under various scenarios, an ideal workload model for schedulers should thus allow the user to vary parallelism profiles of individual jobs as well as the job arrival patterns. In this paper, we present a tool called Malleable-Lab, which models malleable parallel jobs by extending the traditional moldable job models. Ins...

• Type: Conference
• Year: 2010
• Where: PDP
• Authors: Yangjie Cao, Hongyang Sun, Wen-Jing Hsu, Depei Qian
• Keywords: Adaptive Online Schedulers, Distributed And Parallel Computing, Online Schedulers, Parallel Jobs, PDP 2010