ASC: Automatically scalable computation

Amos Waterland, Elaine Angelino, Ryan P. Adams, Jonathan Appavoo, Margo Seltzer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

We present an architecture designed to transparently and automatically scale the performance of sequential programs as a function of the hardware resources available. The architecture is predicated on a model of computation that views program execution as a walk through the enormous state space composed of the memory and registers of a singlethreaded processor. Each instruction execution in this model moves the system from its current point in state space to a deterministic subsequent point. We can parallelize such execution by predictively partitioning the complete path and speculatively executing each partition in parallel. Accurately partitioning the path is a challenging prediction problem. We have implemented our system using a functional simulator that emulates the x86 instruction set, including a collection of state predictors and a mechanism for speculatively executing threads that explore potential states along the execution path. While the overhead of our simulation makes it impractical to measure speedup relative to native x86 execution, experiments on three benchmarks show scalability of up to a factor of 256 on a 1024 core machine when executing unmodified sequential programs. Copyright is held by the owner/author(s).

Original languageEnglish (US)
Title of host publicationASPLOS 2014 - 19th International Conference on Architectural Support for Programming Languages and Operating Systems
Pages575-589
Number of pages15
DOIs
StatePublished - 2014
Event19th International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2014 - Salt Lake City, UT, United States
Duration: Mar 1 2014Mar 5 2014

Publication series

NameInternational Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS

Other

Other19th International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2014
CountryUnited States
CitySalt Lake City, UT
Period3/1/143/5/14

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Hardware and Architecture

Keywords

  • Automatic parallelization
  • Machine learning

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