ROME: Maximizing GPU Efficiency for All-Pairs Shortest Path via Taming Fine-Grained Irregularities
Weile Luo, Yuhan Chen, Xiangrui Yu, Qiang Wang, Ruibo Fan, Hongyuan Liu, Xiaowen Chu
Abstract
All-Pairs Shortest Path (APSP), a fundamental problem in graph analytics, can be solved efficiently by reducing the computational workload through vertex reordering. However, it fails on GPUs due to fine-grained granularity, shape, and dependency irregularities, which cause severe hardware underutilization. We introduce ROME, a system that tames these irregularities by spatially restructuring computation into regularized workloads and temporally overlapping them with an asynchronous pipeline. ROME achieves 14.7-244.5× speedup over the state-of-the-art multicore CPU solution and 11.2-338.0× speedup over the state-of-the-art GPU solution. Notably, our results achieve mostly above 20% and up to 34.7% of peak min-plus OPs across all tested graphs.
Ask about this paper
Ask your agent about it.
Lune has read the top-tier papers around this one, so every answer names the papers it rests on.
Your agent calls
Lunesearch_papers
Free to start. No credit card required.
Terminal
Install the CLIlune papers get f30c98c7-a070-4ce8-9b02-0ef9a038d9f3Related papers
- A fast work-efficient SSSP algorithm for GPUsKai Wang, Don Fussell, Calvin LinPPoPP 2021 · 21 citations
- SparseWeaver: Converting Sparse Operations as Dense Operations on GPUs for Graph WorkloadsShinnung Jeong, Liam Paul Cooper, Ju Min Lee, Heelim Choi et al.HPCA 2025 · 2 citations
- Scalable All-pairs Shortest Paths for Huge Graphs on Multi-GPU ClustersPiyush Sao, Hao Lu, Ramakrishnan Kannan, Vijay Thakkar et al.HPDC 2021 · 6 citations
- TempGraph: An Efficient Chain-driven Temporal Graph Computing Framework on the GPUJin Zhao, Qian Wang, Ligang He, Yu Zhang et al.ASPLOS 2025
- PairGraph: An Efficient Search-space-aware Accelerator for High-performance Concurrent Pairwise QueriesYutao Fu, Zhongtian Long, Yu Zhang, Zirui He et al.DAC 2025 · 1 citation
