A Hybrid Graph Coloring Algorithm for GPUs

TL;DR

This paper introduces a hybrid graph coloring algorithm for GPUs that maintains a worklist throughout all iterations, achieving significant speedups over traditional approaches by combining topology-driven and data-driven methods.

Contribution

A novel hybrid graph coloring algorithm that maintains a worklist throughout all iterations, improving performance on GPUs compared to existing methods.

Findings

Achieved a mean speedup of 2.13x over data-driven IPGC.

Effective hybridization reduces computation time on large graphs.

Implemented in IrGL, demonstrating practical GPU acceleration.

Abstract

Graph algorithms mainly belong to two categories, topology-driven and data-driven. Data-driven approach maintains a worklist of active nodes, the nodes on which work has to be done. Topology-driven approach sweeps over the entire graph to find active nodes. Hybridization is an optimization technique where in each iteration, the computation is done in a topology-driven or data-driven manner based on worklist size. In hybrid implementations, there is a need to switch between topology-driven and data-driven approaches. Typically, a worklist is maintained just in the data-driven part of the algorithm and discarded in the topology-driven part. We propose a variant of hybridization, wherein a worklist is maintained throughout all iterations of the algorithm and still show it to be faster than both, topology-driven and data-driven approaches. We consider a graph coloring algorithm called IPGC (Iterative Parallel Graph Coloring) and implement a hybrid version for the same in a graph domain specific language called IrGL. We observe a mean speedup of 2.13x over a data-driven implementation of IPGC on a suite of 10 large graphs on a NVIDIA GPU.