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.
Faster Vertex Cover Algorithms on GPUs with Component-Aware Parallel Branching
Department of Computer Science at the American University of Beirut（贝鲁特美国大学计算机科学系）；David R. Cheriton School of Computer Science at the University of Waterloo（滑铁卢大学大卫R.切里顿计算机科学学院）
Decomposing Runtime, Kernel, and Quantization Speedups via a Matched FP16 Intermediate: A Hardware-Conditioned Case Study on Four NVIDIA RTX A5000 GPUs
