Parallelization by Simulated Tunneling

DSpace/Manakin Repository

Parallelization by Simulated Tunneling

Citable link to this page


Title: Parallelization by Simulated Tunneling
Author: Waterland, Amos; Appavoo, Jonathan; Seltzer, Margo I.

Note: Order does not necessarily reflect citation order of authors.

Citation: Waterland, Amos, Jonathan Appavoo, and Margo Seltzer. 2012. Parallelization by simulated tunneling. HotPar'12: Proceedings of the 4th USENIX conference on Hot Topics in Parallelism, June 7-8, 2012, Berkeley, CA. Berkeley, CA: USENIX Association.
Full Text & Related Files:
Abstract: As highly parallel heterogeneous computers become commonplace, automatic parallelization of software is an increasingly critical unsolved problem. Continued progress on this problem will require large quantities of information about the runtime structure of sequential programs to be stored and reasoned about. Manually formalizing all this information through traditional approaches, which rely on semantic analysis at the language or instruction level, has historically proved challenging. We take a lower level approach, eschewing semantic analysis and instead modeling von Neumann computation as a dynamical system, i.e., a state space and an evolution rule, which gives a natural way to use probabilistic inference to automatically learn powerful representations of this information. This model enables a promising new approach to automatic parallelization, in which probability distributions empirically learned over the state space are used to guide speculative solvers. We describe a prototype virtual machine that uses this model of computation to automatically achieve linear speedups for an important class of deterministic, sequential Intel binary programs through statistical machine learning and a speculative, generalized form of memoization.
Published Version:
Other Sources:
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at
Citable link to this page:
Downloads of this work:

Show full Dublin Core record

This item appears in the following Collection(s)


Search DASH

Advanced Search