Learn More
We describe, implement and benchmark EigenCFA, an algorithm for accelerating higher-order control-flow analysis (specifically, 0CFA) with a GPU. Ultimately, our program transformations, reductions and optimizations achieve a factor of 72 speedup over an optimized CPU implementation. We began our investigation with the view that GPUs accelerate(More)
A linear programming formulation of the optimal stopping problem for Markov decision processes is approximated using linear function approximation. Using this formulation, a reinforcement learning scheme based on a primal-dual method and incorporating a sampling device called 'split sampling' is proposed and analyzed. An illustrative example from option(More)
We present a small-step abstract interpretation for the A-Normal Form λ-calculus (ANF). This abstraction has been instrumented to find data-dependence conflicts for expressions and procedures. Our goal is parallelization: when two expressions have no dependence conflicts, it is safe to evaluate them in parallel. The underlying principle for discovering(More)
In order to achieve high performance on modern and future machines, applications need to make effective use of the complex, hierarchical memory system. Writing performance-portable code continues to be challenging since each architecture has unique memory access characteristics. In addition, some optimization decisions can only reasonably be made at(More)
  • 1