Analysis of a List Scheduling Algorithm for Task Graphs on Two Types of Resources

Abstract

We consider the problem of scheduling task graphs on two types of unrelated resources, which arises in the context of task-based runtime systems on modern platforms containing CPUs and GPUs. In this paper, we focus on an algorithm named HeteroPrio, which was originally introduced as an efficient heuristic for a particular application. HeteroPrio is an adaptation of the well known list scheduling algorithm, in which the tasks are picked by the resources in the order of their acceleration factor. This algorithm is augmented with a spoliation mechanism: a task assigned by the list algorithm can later on be reassigned to a different resource if it allows to finish this task earlier. We propose here the first theoretical analysis of the HeteroPrio algorithm in the presence of dependencies. More specifically, if the platform contains m and n processors of each type, we show that the worst-case approximation ratio of HeteroPrio is between 1 + max(m n , n m) and 2 + max(m n , n m). Our proof structure allows to precisely identify the necessary conditions on the spoliation strategy to obtain such a guarantee. We also present an in-depth experimental analysis, comparing several such spoliation strategies, and comparing HeteroPrio with other algorithms from the literature. Although the worst case analysis shows the possibility of pathological behavior, HeteroPrio is able to produce, in very reasonable time, schedules of significantly better quality.

Multi-Resource List Scheduling of Moldable Parallel Jobs under Precedence Constraints

“…Beaumont et al [3] and Eyraud-Dubois and Kumar [13] considered scheduling sequential jobs on two alternative types of resources (CPU and GPU) to minimize the makespan. In their model, each job can be chosen to execute on either resource type with different processing rates.…”