research-article

iThreads: A Threading Library for Parallel Incremental Computation

Authors:

Pramod Bhatotia,

Björn B. Brandenburg,

Rodrigo RodriguesAuthors Info & Claims

ACM SIGARCH Computer Architecture News, Volume 43, Issue 1

Pages 645 - 659

https://doi.org/10.1145/2786763.2694371

Published: 14 March 2015 Publication History

Abstract

Incremental computation strives for efficient successive runs of applications by re-executing only those parts of the computation that are affected by a given input change instead of recomputing everything from scratch. To realize these benefits automatically, we describe iThreads, a threading library for parallel incremental computation. iThreads supports unmodified shared-memory multithreaded programs: it can be used as a replacement for pthreads by a simple exchange of dynamically linked libraries, without even recompiling the application code. To enable such an interface, we designed algorithms and an implementation to operate at the compiled binary code level by leveraging MMU-assisted memory access tracking and process-based thread isolation. Our evaluation on a multicore platform using applications from the PARSEC and Phoenix benchmarks and two case-studies shows significant performance gains.

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iThreads: A Threading Library for Parallel Incremental Computation

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cover image ACM SIGARCH Computer Architecture News

ACM SIGARCH Computer Architecture News Volume 43, Issue 1

ASPLOS'15

March 2015

676 pages

ISSN:0163-5964

DOI:10.1145/2786763

Editor:
Doug DeGroot
acm dot org

Issue’s Table of Contents

ASPLOS '15: Proceedings of the Twentieth International Conference on Architectural Support for Programming Languages and Operating Systems
March 2015
720 pages
ISBN:9781450328357
DOI:10.1145/2694344
General Chairs:
Ozcan Ozturk
Bilkent University, Turkey
,
Kemal Ebcioglu
Global Supercomputing, USA
,
Program Chair:
Sandhya Dwarkadas
University of Rochester, USA

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Published: 14 March 2015

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https://dl.acm.org/doi/10.1145/3458336.3465294
Missier PCala J(2019)Efficient Re-Computation of Big Data Analytics Processes in the Presence of Changes: Computational Framework, Reference Architecture, and Applications2019 IEEE International Congress on Big Data (BigDataCongress)10.1109/BigDataCongress.2019.00017(24-34)Online publication date: Jul-2019
https://doi.org/10.1109/BigDataCongress.2019.00017
Cała JMissier P(2018)Selective and Recurring Re-computation of Big Data Analytics Tasks: Insights from a Genomics Case StudyBig Data Research10.1016/j.bdr.2018.06.00113(76-94)Online publication date: Sep-2018
https://doi.org/10.1016/j.bdr.2018.06.001
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Anderson DBlelloch GBaweja AAcar UAgrawal KAzar Y(2021)Efficient Parallel Self-Adjusting ComputationProceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures10.1145/3409964.3461799(59-70)Online publication date: 6-Jul-2021
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