|Elastic deep learning through resilient collective operations
|Year of Publication
|Li, J., G. Bosilca, A. Bouteiller, and B. Nicolae
|SC-W 2023: Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis
A robust solution that incorporates fault tolerance and elastic scaling capabilities for distributed deep learning. Taking advantage of MPI resilient capabilities, aka. User-Level Failure Mitigation (ULFM), this novel approach promotes efficient and lightweight failure management and encourages smooth scaling in volatile computational settings. The proposed ULFM MPI-centered mechanism outperforms the only officially supported elastic learning framework, Elastic Horovod (using Gloo and NCCL), by a significant factor. These results reinforce the capability of MPI extension to deal with resiliency, and promote ULFM as an effective technique for fault management, minimizing downtime, and thereby enhancing the overall performance of distributed applications, in particular elastic training in high-performance computing (HPC) environments and machine learning applications.
Elastic deep learning through resilient collective operations
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