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POSTER: Automatic Differentiation of Parallel Loops with Formal Methods 27

POSTER: Automatic Differentiation of Parallel Loops with For...

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27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP)

作者： Huckelheim, Jan Hascoet, Laurent Argonne Natl Lab Lemont IL 60439 USA Inria Sophia Antipolis Valbonne France

ISBN: (纸本)9781450392044

The accompanying poster to this short paper presents a combination of reverse mode AD and formal methods to enable efficient differentiation of (or backpropagation through) shared-memory parallel code. Compared to the state of the art, our approach can more often avoid the need for atomic updates or private data copies during the parallel derivative computation, even in the presence of unstructured or data-dependent data access patterns. This is achieved by gathering information about the memory access patterns from the input program, which is assumed to be correctly parallelized. This information is then used to build a model of assertions in a theorem prover, which can be used to check the safety of shared memory accesses during the parallel derivative computation.

关键词： Automatic Differentiation OpenMP Theorem Proving Formal Methods data flow reversal

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Automatic Differentiation of Parallel Loops with Formal Methods 51

Automatic Differentiation of Parallel Loops with Formal Meth...

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51st International Conference on Parallel Processing (ICPP)

作者： Huckelheim, Jan Hascoet, Laurent Argonne Natl Lab Argonne IL 60439 USA Inria Sophia Antipolis Valbonne France

ISBN: (纸本)9781450397339

This paper presents a novel combination of reverse mode automatic differentiation and formal methods, to enable efficient differentiation of (or backpropagation through) shared-memory parallel loops. Compared to the state of the art, our approach can reduce the need for atomic updates or private data copies during the parallel derivative computation, even in the presence of unstructured or data-dependent data access patterns. This is achieved by gathering information about the memory access patterns from the input program, which is assumed to be correctly parallelized. This information is then used to build a model of assertions in a theorem prover, which can be used to check the safety of shared memory accesses during the parallel derivative loops. We demonstrate this approach on scientific computing benchmarks including a lattice-Boltzmann method (LBM) solver from the Parboil benchmark suite and a Green's function Monte Carlo (GFMC) kernel from the CORAL benchmark suite.

关键词： Automatic Differentiation OpenMP Theorem Proving Formal Methods data flow reversal

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Automatic Differentiation of Parallel Loops with Formal Methods 22

Automatic Differentiation of Parallel Loops with Formal Meth...

引用

Proceedings of the 51st International Conference on Parallel Processing

作者： Jan Hückelheim Laurent Hascoët Argonne National Laboratory United States of America INRIA France

ISBN: (纸本)9781450397339

This paper presents a novel combination of reverse mode automatic differentiation and formal methods, to enable efficient differentiation of (or backpropagation through) shared-memory parallel loops. Compared to the state of the art, our approach can reduce the need for atomic updates or private data copies during the parallel derivative computation, even in the presence of unstructured or data-dependent data access patterns. This is achieved by gathering information about the memory access patterns from the input program, which is assumed to be correctly parallelized. This information is then used to build a model of assertions in a theorem prover, which can be used to check the safety of shared memory accesses during the parallel derivative loops. We demonstrate this approach on scientific computing benchmarks including a lattice-Boltzmann method (LBM) solver from the Parboil benchmark suite and a Green’s function Monte Carlo (GFMC) kernel from the CORAL benchmark suite.

关键词： Theorem Proving OpenMP Automatic Differentiation Formal Methods data flow reversal

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