Accelerating large graph algorithms on the GPU using CUDA

TLDR: This work presents a few fundamental algorithms - including breadth first search, single source shortest path, and all-pairs shortest path - using CUDA on large graphs using the G80 line of Nvidia GPUs.

Abstract: Large graphs involving millions of vertices are common in many practical applications and are challenging to process. Practical-time implementations using high-end computers are reported but are accessible only to a few. Graphics Processing Units (GPUs) of today have high computation power and low price. They have a restrictive programming model and are tricky to use. The G80 line of Nvidia GPUs can be treated as a SIMD processor array using the CUDA programming model. We present a few fundamental algorithms - including breadth first search, single source shortest path, and all-pairs shortest path - using CUDA on large graphs. We can compute the single source shortest path on a 10 million vertex graph in 1.5 seconds using the Nvidia 8800GTX GPU costing $600. In some cases optimal sequential algorithm is not the fastest on the GPU architecture. GPUs have great potential as high-performance co-processors.