Heterogeneous combinatorial candidate generation

Abstract

Elementary Flux Modes (EFMs) can be used to characterize functional cellular networks and have gained importance in systems biology. Enumeration of EFMs is a compute-intensive problem due to the combinatorial explosion in candidate generation. While there exist parallel implementations for shared-memory SMP and distributed memory architectures, tools supporting heterogeneous platforms have not yet been developed. Here we propose and evaluate a heterogeneous implementation of combinatorial candidate generation that employs GPUs as accelerators. It uses a 3-stage pipeline based method to manage arithmetic intensity. Our implementation results in a 6x speedup over the serial implementation, and a 1.8x speedup over a multithreaded implementation for CPU-only SMP architectures. © 2013 Springer-Verlag.