Accelerating large graph algorithms on the GPU using CUDA
HiPC'07 Proceedings of the 14th international conference on High performance computing
An effective GPU implementation of breadth-first search
Proceedings of the 47th Design Automation Conference
Accelerating CUDA graph algorithms at maximum warp
Proceedings of the 16th ACM symposium on Principles and practice of parallel programming
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In this paper, we present FlexBFS, a parallelism-aware implementation for breadth-first search on GPU. Our implementation can adjust the computation resources according to the feedback of available parallelism dynamically. We also optimized our program in three ways: (1)a simplified two-level queue management,(2)a combined kernel strategy and (3)a high-degree vertices specialization approach. Our experimental results show that it can achieve 3~20 times speedup against the fastest serial version, and can outperform the TBB based multi-threading CPU version and the previous most effective GPU version on all types of input graphs.