Novel List Scheduling Strategies for Data Parallelism Task Graphs

Novel List Scheduling Strategies for Data Parallelism Task Graphs

This paper studies task scheduling algorithms which schedule a set of tasks on multiple cores so that the total scheduling length is minimized. Most of the algorithms developed in the past assume that a task is executed on a single core. Unlike the previous algorithms, the algorithms studied in this paper allow a task to be executed on multiple cores. This paper proposes six algorithms. All of ...

List Scheduling for Iterative Data-flow Graphs

List Scheduling for Iterative Data-Flow Graphs Martin S. Koster and Sabih H. Gerez University of Twente, Department of Electrical Engineering P.O. Box 217, 7500 AE Enschede, The Netherlands e-mail: [email protected] or [email protected] Abstract This paper presents a scheduling method for the high-level synthesis of DSP algorithms described by iterative data ow graphs with an iterati...

Scheduling Strategies for Mixed Data and Task Parallelism on Heterogeneous Clusters

We consider the execution of a complex application on a heterogeneous “grid” computing platform. The complex application consists of a suite of identical, independent problems to be solved. In turn, each problem consists of a set of tasks. There are dependences (precedence constraints) between these tasks and these dependences are organized as a tree. A typical example is the repeated execution...

Scheduling Strategies for Mixed Data and Task Parallelism on Heterogeneous Processor Grids

List scheduling of general task graphs under LogP

List scheduling is the most frequently used scheduling technique. In this context worst case analysis as well as many experimental studies were performed for various computational models. However, many new models have been proposed during the last decade with the aim to provide a realistic but still simple and general model of parallel computation. LogP is one of the most popular models so far ...