BCSR on GPU: A Way Forward Extreme-scale Graph Processing on Accelerator-enabled Frontier Supercomputer

Handling large graphs in a distributed environment requires effective partitioning across processors and efficient management of local partitions. In 2D partitioning, local graphs often become too sparse, making memory-efficient data structures crucial. Using the Compressed Sparse Row (CSR) format w...

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Bibliographic Details
Published in:SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis pp. 280 - 289
Main Authors: Sattar, Naw Safrin, Lu, Hao, Wang, Feiyi
Format: Conference Proceeding
Language:English
Published: IEEE 17.11.2024
Subjects:
2D Partitioning
AMD GPU
Benchmark testing
Breadth First Search (BFS)
Compressed Sparse Row (CSR) Graph
Conferences
Data structures
Degradation
Graphics processing units
High performance computing
HIP
Large-scale Graph
Partitioning algorithms
Performance analysis
Runtime
Supercomputers
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Summary:Handling large graphs in a distributed environment requires effective partitioning across processors and efficient management of local partitions. In 2D partitioning, local graphs often become too sparse, making memory-efficient data structures crucial. Using the Compressed Sparse Row (CSR) format wastes space, especially for > 83% of vertices with empty edges for the sparse graphs. This study explores bit-CSR (BCSR), a modified CSR representation, on GPUs to reduce memory usage in graph computations. We achieved 16.67% memory savings on a sparse rmat dataset with 268 million vertices and 357 million edges, without performance degradation, supported by both theoretical and experimental storage savings of 33%. However, we observed a 1.7× slowdown in degree lookup times due to bitwise operations on AMD CPUs. This analysis highlights the potential of BCSR on GPUs for improving Graph500 benchmark performance on GPU-accelerated systems, such as the Frontier supercomputer.
DOI:10.1109/SCW63240.2024.00044