18944

Accelerating Concurrent Heap on GPUs

Yanhao Chen, Fei Hua, Chaozhang Huang, Jeremy Bierema, Chi Zhang, Eddy Z. Zhang
Rutgers University
arXiv:1906.06504 [cs.DC], (15 Jun 2019)

@misc{chen2019accelerating,

   title={Accelerating Concurrent Heap on GPUs},

   author={Yanhao Chen and Fei Hua and Chaozhang Huang and Jeremy Bierema and Chi Zhang and Eddy Z. Zhang},

   year={2019},

   eprint={1906.06504},

   archivePrefix={arXiv},

   primaryClass={cs.DC}

}

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Priority queue, often implemented as a heap, is an abstract data type that has been used in many well-known applications like Dijkstra’s shortest path algorithm, Prim’s minimum spanning tree, Huffman encoding, and the branch-and-bound algorithm. However, it is challenging to exploit the parallelism of the heap on GPUs since the control divergence and memory irregularity must be taken into account. In this paper, we present a parallel generalized heap model that works effectively on GPUs. We also prove the linearizability of our generalized heap model which enables us to reason about the expected results. We evaluate our concurrent heap thoroughly and show a maximum 19.49X speedup compared to the sequential CPU implementation and 2.11X speedup compared with the existing GPU implementation. We also apply our heap to single source shortest path with up to 1.23X speedup and 0/1 knapsack problem with up to 12.19X speedup.
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