Privacy-preserving Job Scheduler for GPU Sharing
- Resource Type
- Conference
- Authors
- Ray, Aritra; Lafata, Kyle; Zhang, Zhaobo; Xiong, Ying; Chakrabarty, Krishnendu
- Source
- 2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) CCGRIDW Cluster, Cloud and Internet Computing Workshops (CCGridW), 2023 IEEE/ACM 23rd International Symposium on. :337-339 May, 2023
- Subject
- Computing and Processing
Training
Schedules
Privacy
Data analysis
Processor scheduling
Knowledge based systems
Graphics processing units
- Language
Machine learning (ML) training jobs are resource intensive. High infrastructure costs of computing clusters encourage multi-tenancy in GPU resources. This invites a scheduling problem in assigning multiple ML training jobs on a single GPU while minimizing task interference. Our paper introduces a clustering-based privacy-preserving job scheduler that minimizes task interference without accessing sensitive user data. We perform ML workload characterization, made available publicly [1], and do exploratory data analysis to cluster ML workloads. Consequently, we build a knowledge base of inter and intra-cluster task interference to minimize task interference.