Accelerating BFS via Data Structure-Aware Prefetching on GPU

Guo, Hui; Huang, Libo; Lü, Yashuai; Ma, Jianqiao; Cheng, Qi; Ma, Shaoping; Wang, Zhiying

doi:10.1109/access.2018.2876201

Cited by 4 publications

(2 citation statements)

References 35 publications

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“…The time complexity of graph partitioning is O(|V | + |E|). Graph partitioning can be parallelized by applying the existing methods of running the BFS algorithm in parallel [20] [21] [22] [23]. When applying an existing parallel BFS algorithm, we can assign level labels to the nodes in the same way as in sequential BFS algorithm.…”

Section: Graph's Namementioning

confidence: 99%

Efficient Parallel Processing of All-Pairs Shortest Paths on Multicore and GPU Systems

Alghamdi,

He,

Ren

et al. 2024

IEEE Trans. Consumer Electron.

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Section: Graph's Namementioning

confidence: 99%

Efficient Parallel Processing of All-Pairs Shortest Paths on Multicore and GPU Systems

Alghamdi,

He,

Ren

et al. 2024

IEEE Trans. Consumer Electron.

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“…Thus, since its calculation time is limited by the memory bandwidth, it seems to be suitable for accelerators with high-speed memory such as GPUs. As for BFS-APSP, several studies [4,6] show that GPUs can improve BFS performance to some extent. However, BFS-APSP must be less suitable for GPU than ADJ-APSP because BFS needs an irregular memory access pattern.…”

Section: Development Issues 51 Parallelization Using Gpumentioning

confidence: 99%

Parallelization of All-Pairs-Shortest-Path Algorithms in Unweighted Graph

Nakao

Murai

Sato

2020

Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region

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The design of the network topology of a large-scale parallel computer system can be represented as an order/degree problem in graph theory. To solve the order/degree problem, it is necessary to obtain an all-pairs-shortest-path (APSP) of the graph. Thus, this paper evaluates two parallel algorithms that quickly find the APSP in unweighted graphs and compares their performance. The first APSP algorithm is based on the breadth-first search (BFS-APSP) and the second is based on the adjacency matrix (ADJ-APSP). First, we develop serial algorithms and threaded algorithms using OpenMP, and show that ADJ-APSP is up to 32.34 times faster than BFS-APSP. Next, we develop hybrid-parallel algorithms using OpenMP and MPI, and show that BFS-APSP is faster than ADJ-APSP under certain conditions because the maximum number of processes in BFS-APSP is greater than in ADJ-APSP. In addition, we parallelize ADJ-APSP using a single GPU (NVIDIA Tesla V100) and achieve a speed increase of up to 16.53-fold compared to that of a single CPU. Finally, we evaluate the performance of the algorithms using 128 GPUs and achieve a computation time 101.10 times faster than that using a single GPU. Moreover, it is shown that the calculation time of both algorithms can be greatly reduced when the input graphs are symmetric. CCS CONCEPTS • Theory of computation → Massively parallel algorithms.

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NGraph: Parallel Graph Processing in Hybrid Memory Systems

Liu

Liao

et al. 2019

IEEE Access

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Accelerating BFS via Data Structure-Aware Prefetching on GPU

Cited by 4 publications

References 35 publications

Efficient Parallel Processing of All-Pairs Shortest Paths on Multicore and GPU Systems

Efficient Parallel Processing of All-Pairs Shortest Paths on Multicore and GPU Systems

Parallelization of All-Pairs-Shortest-Path Algorithms in Unweighted Graph

NGraph: Parallel Graph Processing in Hybrid Memory Systems

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