PNNL

Abstract

Graph matching and coloring constitute two fundamental classes of combinatorial problems having numerous established as well as emerging applications in computational science and engineering, high-performance computing, and informatics. We provide a snapshot of an on-going work on the design and implementation of new highly-scalable distributed-memory parallel algorithms for two prototypical problems from these classes, edge-weighted matching and distance-1 vertex coloring. Graph algorithms in general have low concurrency and poor data locality, making it challenging to achieve scalability on massively parallel machines. We overcome this challenge by employing a variety of techniques, including approximation, speculation and iteration, optimized communication, and randomization, in concert. We present preliminary results on weak and strong scalability studies conducted on an IBM Blue Gene/P machine employing up to tens of thousands of processors. The results show that the algorithms hold strong potential for computing at petascale.