Overlapping methods of all-to-all communication and FFT algorithms for torus-connected massively parallel supercomputers

Abstract

Torus networks are commonly used for massively parallel computers, its performance often becomes the constraint on total application performance. Especially in an asymmetric torus network, network traffic along the longest axis is the performance bottleneck for all-to-all communication, so that it is important to schedule the longest-axis traffic smoothly. In this paper, we propose a new algorithm based on an indirect method for pipelining the all-to-all procedures using shared memory parallel threads, which (1) isolates the longest-axis traffic from other traffic, (2) schedules it smoothly and (3) overlaps all of the other traffic and overhead for the all-to-all communication behind the longest-axis traffic. The proposed method achieves up to 95% of the theoretical peak. We integrated the overlapped all-to-all method with parallel FFT algorithms. And local FFT calculations are also overlapped behind the longest-axis traffic. The FFT performance achieves up to 90% of the theoretical peak for the parallel 1D FFT. © 2010 IEEE.