ARGO addresses the poor scalability of existing GNN libraries on multi-core processors by optimizing memory bandwidth utilization. The auto-tuner efficiently searches for near-optimal configurations, leading to significant speedups in GNN training performance.
The content discusses the challenges faced by current GNN libraries in utilizing multi-core processors effectively due to memory-intensive workloads. ARGO's approach of parallelizing processes and optimizing resource allocation is highlighted as a solution to enhance platform resource utilization.
Furthermore, the online auto-tuner developed by the author dynamically adjusts configurations during training, ensuring optimal performance without altering the semantics of GNN algorithms. Experimental results demonstrate substantial speedups achieved by ARGO compared to traditional GNN libraries.
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arxiv.org
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by Yi-Chien Lin... ב- arxiv.org 02-29-2024
https://arxiv.org/pdf/2402.03671.pdfשאלות מעמיקות