Combining HTM with RCU to speed Up graph coloring on multicore platforms

Abstract

Graph algorithms are hard to parallelize, as they exhibit varying degrees of parallelism and perform irregular memory accesses. Graph coloring is a well studied problem, that colors the vertices of a graph, such that no adjacent vertices have the same color. This is a necessity for a large number of applications that require a coloring with few colors in near-linear time. In this work, we propose a simple and fast parallel graph coloring algorithm, well suited for shared memory architectures. Our algorithm employs Hardware Transactional Memory (HTM) to detect coloring inconsistencies between adjacent vertices, and exploits Read-Copy-Update (RCU) to enable high performance and ensure correctness. We evaluate our algorithm on an Intel Haswell server using large-scale synthetic and real-world graphs, chosen to vary in terms of density and structure. With 14 threads, we achieved a geometric-mean speedup of 4.35 and a maximum speedup of 11.44.