Traversing large graphs on GPUs with unified memory

Prasun Gera, Hyojong Kim, Piyush Sao, Hyesoon Kim, David Bader

Research output: Contribution to journalConference articlepeer-review

35 Scopus citations


Due to the limited capacity of GPU memory, the majority of prior work on graph applications on GPUs has been restricted to graphs of modest sizes that fit in memory. Recent hardware and software advances make it possible to address much larger host memory transparently as a part of a feature known as unified virtual memory. While accessing host memory over an interconnect is understandably slower, the problem space has not been sufficiently explored in the context of a challenging workload with low computational intensity and an irregular data access pattern such as graph traversal. We analyse the performance of breadth first search (BFS) for several large graphs in the context of unified memory and identify the key factors that contribute to slowdowns. Next, we propose a lightweight offline graph reordering algorithm, HALO (Harmonic Locality Ordering), that can be used as a pre-processing step for static graphs. HALO yields speedups of 1.5x-1.9x over baseline in subsequent traversals. Our method specifically aims to cover large directed real world graphs in addition to undirected graphs whereas prior methods only account for the latter. Additionally, we demonstrate ties between the locality ordering problem and graph compression and show that prior methods from graph compression such as recursive graph bisection can be suitably adapted to this problem.

Original languageEnglish (US)
Pages (from-to)1119-1133
Number of pages15
JournalProceedings of the VLDB Endowment
Issue number7
StatePublished - 2020
Event46th International Conference on Very Large Data Bases, VLDB 2020 - Virtual, Japan
Duration: Aug 31 2020Sep 4 2020

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • General Computer Science


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