Optimization of fast fourier transforms on the blue gene/L supercomputer

Abstract

We analyze the bottlenecks in the parallel FFT algorithm and describe optimizations carried out for the algorithm on the Blue Gene/L Supercomputer. We identified three avenues for improving the performance of the algorithm - single-node FFT performance, Alltoall collective performance and overlap of computation and communication. Performance at all these levels has been optimized using the double-hummer intrinsics of the Blue Gene/L CPU, careful ordering and synchronization of messages in Alltoall communications and suitable interleaving of message exchanges with computations. Using these optimizations, we obtained 20% performance improvement over the baseline version on the 64 racks Blue Gene/L system. We give a brief overview of the Alltoall optimizations, describe our computation-communication overlap strategy and present results for strong scaling and weak scaling of parallel FFT on Blue Gene/L. We also discuss the fundamental limits to scaling of the parallel transpose algorithm for computing FFT. © 2008 Springer Berlin Heidelberg.