# Accelerating Concurrent Heap on GPUs

## TL;DR

This paper introduces a new parallel heap model optimized for GPUs, demonstrating significant speedups over traditional CPU and existing GPU implementations in various algorithms.

## Contribution

A novel parallel generalized heap model for GPUs with proven linearizability, enabling efficient concurrent priority queue operations.

## Key findings

- Maximum 19.49X speedup over CPU implementation
- 2.11X speedup over existing GPU heap
- Up to 12.19X speedup in knapsack problem

## Abstract

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.