May 3, 2019
Jolley Hall, Room 309
Title: Scheduling Multiple Parallel Jobs Online
Abstract: The prevalence of parallel processing has only increased in recent years. Today, most computing machines available on the market shifted from using single processors to possessing a multicore architecture. Naturally, there has been considerable work in developing parallel programming languages and frameworks which programmers can use to leverage the computing power of these machines. These languages allow users to create programs with internal parallelism. The next, and crucial, step is to ensure that the computing system can efficiently execute these parallel jobs.
Executing a single parallel job efficiently is a very well-studied problem in parallel computing and there is extensive work on scheduling multiple sequential jobs to minimize important objectives. However, there is little work on scheduling multiple jobs that have internal parallelism.
This dissertation will focus on designing theoretically efficient and practically good scheduling algorithms for parallelizable jobs in the identical machines setting. In this talk we study the problem of optimizing objectives such as average flow time and maximum flow time. The goal of the dissertation is to examine the problem of scheduling multiple parallel jobs and to take the first steps in creating a body of knowledge comparable to the extensive amount of existing work on scheduling sequential jobs.
Advisor: I-Ting Angelina Lee