A Granularity Characterization of Task Scheduling Effectiveness

TL;DR

This paper introduces a framework linking task dependency structures to scheduling overhead growth, helping predict and improve the strong scaling performance of task-based parallel systems.

Contribution

It presents a novel granularity characterization method based on dependency topology, enabling better prediction and decision-making for scheduling strategies in parallel applications.

Findings

Dependency structure governs overhead scaling.

The characterization explains scaling breakdowns.

Overhead models predict strong-scaling limits.

Abstract

Task-based runtime systems provide flexible load balancing and portability for parallel scientific applications, but their strong scaling is highly sensitive to task granularity. As parallelism increases, scheduling overhead may transition from negligible to dominant, leading to rapid drops in performance for some algorithms, while remaining negligible for others. Although such effects are widely observed empirically, there is a general lack of understanding how algorithmic structure impacts whether dynamic scheduling is always beneficial. In this work, we introduce a granularity characterization framework that directly links scheduling overhead growth to task-graph dependency topology. We show that dependency structure, rather than problem size alone, governs how overhead scales with parallelism. Based on this observation, we characterize execution behavior using a simple granularity measure that indicates when scheduling overhead can be amortized by parallel computation and when scheduling overhead dominates performance. Through experimental evaluation on representative parallel workloads with diverse dependency patterns, we demonstrate that the proposed characterization explains both gradual and abrupt strong-scaling breakdowns observed in practice. We further show that overhead models derived from dependency topology accurately predict strong-scaling limits and enable a practical runtime decision rule for selecting dynamic or static execution without requiring exhaustive strong-scaling studies or extensive offline tuning.