ASC: automatically scalable computation

  • Authors:

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

  • Affiliations:

  • Harvard University, Cambridge, MA, USA;Harvard University, Cambridge, MA, USA;Harvard University, Cambridge, MA, USA;Boston University, Boston, MA, USA;Harvard University, Cambridge, MA, USA

  • Venue:
  • Proceedings of the 19th international conference on Architectural support for programming languages and operating systems
  • Year:
  • 2014

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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 single-threaded 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.