Scaling Graph 500 SSSP to 140 Trillion Edges with over 40 Million Cores
DescriptionThe SSSP kernel was introduced into the Graph 500 benchmark in 2017. However, there has been no result from a full-scale world-top supercomputer. The primary reason is the poor work-inefficiency of existing algorithms at large scales.
We propose an SSSP implementation for machines, including an SSSP algorithm to achieve work-efficiency, along with an adaptive dense/sparse-mode selection approach to achieve communication-efficiency. Our implementation reaches 7638 GTEPS, with 103158 processors (over 40 million cores), and achieves 3.7 times in performance and 512 times in graph size compared with the current top one on the Graph 500 SSSP list. Based on our experience of running extreme-scale SSSP, we uncover the root cause of its poor scalability: the weight distribution allows edges with weights close to zero, making the SSSP tree deeper on larger graphs. We further explore a scalability-friendly weight distribution by setting a non-zero lower bound to the edge weights.
We propose an SSSP implementation for machines, including an SSSP algorithm to achieve work-efficiency, along with an adaptive dense/sparse-mode selection approach to achieve communication-efficiency. Our implementation reaches 7638 GTEPS, with 103158 processors (over 40 million cores), and achieves 3.7 times in performance and 512 times in graph size compared with the current top one on the Graph 500 SSSP list. Based on our experience of running extreme-scale SSSP, we uncover the root cause of its poor scalability: the weight distribution allows edges with weights close to zero, making the SSSP tree deeper on larger graphs. We further explore a scalability-friendly weight distribution by setting a non-zero lower bound to the edge weights.
Event Type
Paper
TimeWednesday, 16 November 202210:30am - 11am CST
LocationC146
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