BYO: A Unified Framework for Benchmarking Large-Scale Graph Containers
Brian Wheatman, Xiaojun Dong, Zheqi Shen, Laxman Dhulipala, Jakub, {\L}\k{a}cki, Prashant Pandey, and Helen Xu

TL;DR
This paper introduces BYO, a standardized benchmarking framework for large-scale graph containers, providing an apples-to-apples comparison of their performance across various algorithms and graphs, revealing minimal differences among containers.
Contribution
The paper presents BYO, the first unified benchmarking framework for graph containers, enabling fair performance comparisons and revealing insights into container efficiency and API design.
Findings
Average algorithm runtime varies little across containers.
Simple container APIs cause minimal slowdown.
Dynamic containers achieve high batch-insert throughput.
Abstract
A fundamental building block in any graph algorithm is a graph container - a data structure used to represent the graph. Ideally, a graph container enables efficient access to the underlying graph, has low space usage, and supports updating the graph efficiently. In this paper, we conduct an extensive empirical evaluation of graph containers designed to support running algorithms on large graphs. To our knowledge, this is the first apples-to-apples comparison of graph containers rather than overall systems, which include confounding factors such as differences in algorithm implementations and infrastructure. We measure the running time of 10 highly-optimized algorithms across over 20 different containers and 10 graphs. Somewhat surprisingly, we find that the average algorithm running time does not differ much across containers, especially those that support dynamic updates.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDistributed and Parallel Computing Systems · Cloud Computing and Resource Management · Interconnection Networks and Systems
