Horus: Interference-Aware and Prediction-Based Scheduling in Deep Learning Systems

• Published in: IEEE transactions on parallel and distributed systems Vol. 33; no. 1; pp. 88 - 100
• Main Authors: Yeung, Gingfung, Borowiec, Damian, Yang, Renyu, Friday, Adrian, Harper, Richard, Garraghan, Peter
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accelerators; cloud computing; Computational modeling; Deep learning; Distributed systems; Experimentation; GPU utilization; Graphics processing units; Hardware; Interference; Kernel; Load modeling; Predictive models; Production; Reduction; Resource utilization; workload prediction

To accelerate the training of Deep Learning (DL) models, clusters of machines equipped with hardware accelerators such as GPUs are leveraged to reduce execution time. State-of-the-art resource managers are needed to increase GPU utilization and maximize throughput. While co-locating DL jobs on the s...