Here's a very impressive speedup on a program analysis problem by using a GPU.

EigenCFA: Accelerating Flow Analysis with GPUs - Tarun Prabhu, Shreyas Ramalingam, Matthew Might, Mary Hall

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 high-arithmetic, data-parallel computations with a poor tolerance for branching. Taking that perspective to its limit, we reduced Shivers’s abstract-interpretive 0CFA to an algorithm synthesized from linear-algebra operations. Central to this reduction were “abstract” Church encodings, and encodings of the syntax tree and abstract domains as vectors and matrices.

A straightforward (dense-matrix) implementation of EigenCFA performed slower than a fast CPU implementation. Ultimately, sparse-matrix data structures and operations turned out to be the critical accelerants. Because control-flow graphs are sparse in practice (up to 96% empty), our control-flow matrices are also sparse, giving the sparse matrix operations an overwhelming space and speed advantage.

## Recent comments

3 days 13 hours ago

1 week 4 days ago

1 week 6 days ago

1 week 6 days ago

1 week 6 days ago

1 week 6 days ago

1 week 6 days ago

2 weeks 25 min ago

2 weeks 4 hours ago

2 weeks 6 hours ago