Towards Zero Spawn Overhead: Work Stealing Without Deques

In a randomized work-stealing scheduler, parallel speedup depends on the spawn overhead, which workers pay to allow tasks to execute in parallel, and the steal overhead, which thieves pay to start executing new work. The importance of minimizing the spawn overhead in a randomized work-stealing scheduler is first formalized by Frigo et al., coined as the work-first principle [15], which states that one should minimize spawn overhead even at the expense of a larger steal overhead. Since then, many strategies have been proposed to reduce the spawn overhead, which is dominated by maintaining a per-worker double-ended queue, or deque, to keep track of available parallel work.

Authors

Institutions

Publication Details

Published
2025-07-16
DOI
https://doi.org/10.1145/3694906.3743349
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Towards Zero Spawn Overhead: Work Stealing Without Deques

Tao B. Schardl, Aaron Handleman, Kyle Singer, I-Ting Angelina Lee
Parallel Computing and Optimization Techniques
article

Towards Zero Spawn Overhead: Work Stealing Without Deques

Tao B. Schardl, Aaron Handleman, Kyle Singer, I-Ting Angelina Lee
article en

Abstract

In a randomized work-stealing scheduler, parallel speedup depends on the spawn overhead, which workers pay to allow tasks to execute in parallel, and the steal overhead, which thieves pay to start executing new work. The importance of minimizing the spawn overhead in a randomized work-stealing scheduler is first formalized by Frigo et al., coined as the work-first principle [15], which states that one should minimize spawn overhead even at the expense of a larger steal overhead. Since then, many strategies have been proposed to reduce the spawn overhead, which is dominated by maintaining a per-worker double-ended queue, or deque, to keep track of available parallel work.

Washington University in St. Louis (US), Massachusetts Institute of Technology (US)
National Science Foundation, U.S. Department of Energy, National Nuclear Security Administration
Openalex Percentile: Top 82%
Parallel Computing and Optimization Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.