Which Graph Representation to Select for Static Graph-Algorithms on a CUDA-capable GPU

GPUs seem to be ideal for algorithms that work in parallel. A number of ways to represent graphs in GPU memory are known. But so far there are no guidelines to select the representation that is likely to result in the best performance. This a comprehensive study investigates for CUDA-capable GPUs how different graph representations influence the performance of highly optimized graph processing algorithms that traverse the graphs without modifying them. We evaluate three different graph exchange formats and how efficiently they can be imported into eight graph data structures. We use ten state-of-the-art benchmarks that employ different traversals pattern. We evaluate them on 19 input graphs with different characteristics. The measurements show that there is not a single best data structure; the runtime performance can vary up to a factor of 2 between two representations. The main contribution is a set of rules that helps in picking the best-performing graph representation for a given situation.

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