Year: 2021
Type: conference-paper
Abstract: Self-adjusting computation is an approach for automatically producing dynamic algorithms from static ones. It works by tracking control and data dependencies, and propagating changes through the dependencies when making an update. Extensively studied in the sequential setting, some results on parallel self-adjusting computation exist, but are only applicable to limited classes of computations, or are ad-hoc systems with no theoretical analysis of their performance.
Institution: Carnegie Mellon University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: National Science Foundation
Awards: Collaborative Research: PPoSS: Planning: Unifying Software and Hardware to Achieve Performant and Scalable Zero-cost Parallelism in the Heterogeneous Future, AF: Small: Shared-Memory Parallel Algorithms: Theory and Practice, Collaborative Research: SHF: Medium: Responsive Parallelism for Interactive Applications: Theory and Practice, SHF: Medium: Algorithmic lambda-Calculus for the Design, Analysis, and Implementation of Parallel Algorithms, SPX: Parallel Models and Algorithms for Emerging Memory Systems
OpenAlex ID: W3160001291