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Feature selection and policy optimization for distributed instruction placement using reinforcement learning

9 years 12 months ago
Feature selection and policy optimization for distributed instruction placement using reinforcement learning
Communication overheads are one of the fundamental challenges in a multiprocessor system. As the number of processors on a chip increases, communication overheads and the distribution of computation and data become increasingly important performance factors. Explicit Dataflow Graph Execution (EDGE) processors, in which instructions communicate with one another directly on a distributed substrate, give the compiler control over communication overheads at a fine granularity. Prior work shows that compilers can effectively reduce fine-grained communication overheads in EDGE architectures using a spatial instruction placement algorithm with a heuristic-based cost function. While this algorithm is effective, the cost function must be painstakingly tuned. Heuristics tuned to perform well across a variety of applications leave users with little ability to tune performance-critical applications, yet we find that the best placement heuristics vary significantly with the application. Fir...
Katherine E. Coons, Behnam Robatmili, Matthew E. T
Added 31 May 2010
Updated 31 May 2010
Type Conference
Year 2008
Where IEEEPACT
Authors Katherine E. Coons, Behnam Robatmili, Matthew E. Taylor, Bertrand A. Maher, Doug Burger, Kathryn S. McKinley
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