Thermodynamics and garbage collection
ACM SIGPLAN Notices
Computation, dynamics, and cognition
Computation, dynamics, and cognition
Action, or the fungibility of computation
Feynman and computation
Proceedings of the 6th International Systems and Storage Conference
ASC: automatically scalable computation
Proceedings of the 19th international conference on Architectural support for programming languages and operating systems
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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.