超越单一队列：多级多队列作为GPU上SSSP问题的有效设计
Beyond a Single Queue: Multi-Level-Multi-Queue as an Effective Design for SSSP problems on GPUs
AI总结：
本文提出多级多队列设计，通过优化GPU上的SSSP问题，实现显著的速度提升。
摘要：
作为图处理中最基本的问题之一，单源最短路径（SSSP）问题在众多应用场景中发挥着关键作用。然而，现有的基于GPU的解决方案仍然效率低下，因为它们通常依赖于单一、固定的队列设计，这会导致严重的同步开销、高内存延迟以及对多样化输入的适应性差。为了解决这些效率问题，我们提出了多级多队列（MLMQ），一种新的数据结构，该结构将多个队列分布在GPU的多级并行性和内存层次结构上。为了实现MLMQ，我们引入了一种类似缓存的协作机制，以实现高效的队列间协调，并开发了一种基于统一读写原语的模块化队列设计。在此框架中，我们通过设计一组GPU友好的队列、在多个层次上组合它们，并进一步提供一个输入自适应的MLMQ配置方案来扩展优化空间。我们的MLMQ设计在最先进的实现上实现了平均1.87倍到17.13倍的速度提升。我们的代码已开源在https://github.com/Leo9660/MLMQ.git。
英文摘要：
As one of the most fundamental problems in graph processing, the Single-Source Shortest Path (SSSP) problem plays a critical role in numerous application scenarios. However, existing GPU-based solutions remain inefficient, as they typically rely on a single, fixed queue design that incurs severe synchronization overhead, high memory latency, and poor adaptivity to diverse inputs. To address these inefficiencies, we propose MultiLevelMultiQueue (MLMQ), a novel data structure that distributes multiple queues across the GPU's multi-level parallelism and memory hierarchy. To realize MLMQ, we introduce a cache-like collaboration mechanism for efficient inter-queue coordination, and develop a modular queue design based on unified Read and Write primitives. Within this framework, we expand the optimization space by designing a set of GPU-friendly queues, composing them across multiple levels, and further providing an input-adaptive MLMQ configuration scheme. Our MLMQ design achieves average speedups of 1.87x to 17.13x over state-of-the-art implementations. Our code is open-sourced at https://github.com/Leo9660/MLMQ.git.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
