- By:
- Yang, Haoshen ; Lu, Hao ; Sattar, Naw Safrin; Liu, Hang; Wang, Feiyi
- Page Number:
- 650-658
- Book Title:
- SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis
- Publication Date:
- March 12, 2026
- Publisher Location:
- IEEE, New Jersey, United States of America
- Conference Name:
- 14th Workshop on Irregular Applications: Architectures and Algorithms
- Conference Location:
- Atlanta, Georgia, United States of America
- Conference Sponsor:
- IEEE, ACM
- View DOI Listing:
- https://doi.org/10.1109/SCW63240.2024.00090
Abstract
Graphics Processing Units (GPUs) offer significant potential for accelerating various computational tasks, including Breadth-First Search (BFS). Numerous efforts have been made to deploy BFS on GPUs effectively. To address the dynamic nature of BFS, XBFS, the state-of-the-art work, employs an adaptive strategy that leverages different optimized frontier queue generation designs, accommodating the varying characteristics of levels in BFS. While XBFS demonstrates excellent performance on NVIDIA Quadro P6000 GPUs, it faces challenges when deployed on AMD GPUs. In this work, we present our efforts to implement XBFS’s adaptive approach on Frontier, the most powerful supercomputer system, by porting XBFS to AMD MI250X GPUs. Through targeted optimizations tailored to the unique features of AMD GPUs, our implementation achieves an average performance of 43 Giga-Traversed Edges Per Second (GTEPS) per Graphics Compute Dies (GCD). Based on these results, we observe potential for surpassing the performance of the official Frontier results from the Graph500 benchmark released in June 2024.
