HPCClusterScape: Increasing Transparency and Efficiency of Shared High-Performance Computing Clusters for Large-scale AI Models

Heungseok Park, Aeree Cho, Hyojun Jeon, Hayoung Lee, Youngil Yang, Sungjae Lee, Heungsub Lee, Jaegul Choo

Room: 106

2023-10-23T03:00:00ZGMT-0600Change your timezone on the schedule page
Exemplar figure, described by caption below
HPCClusterScape offers real-time resource monitoring for HPC clusters. The (A) Cluster Overview displays GPU resources, allowing users to observe both the overall cluster and individual resources. Users can set (B) Violation Rules to track system metric statistics and detect anomalies over time. Clicking on specific workloads leads to the (C) Diagnostics View, enabling detailed node, GPU, and metric analysis for large-scale distributed training.
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The emergence of large-scale AI models, like GPT-4, has significantly impacted academia and industry, driving the demand for high-performance computing (HPC) to accelerate workloads. To address this, we present HPCClusterScape, a visualization system that enhances the efficiency and transparency of shared HPC clusters for large-scale AI models. HPCClusterScape provides a comprehensive overview of system-level (e.g., partitions, hosts, and workload status) and application-level (e.g., identification of experiments and researchers) information, allowing HPC operators and machine learning researchers to monitor resource utilization and identify issues through customizable violation rules. The system includes diagnostic tools to investigate workload imbalances and synchronization bottlenecks in large-scale distributed deep learning experiments. Deployed in industrial-scale HPC clusters, HPCClusterScape incorporates user feedback and meets specific requirements. This paper outlines the challenges and prerequisites for efficient HPC operation, introduces the interactive visualization system, and highlights its contributions in addressing pain points and optimizing resource utilization in shared HPC clusters.