GPU Cluster Scheduling for Network-Sensitive Deep Learning

no code implementations • 29 Jan 2024 • Aakash Sharma, Vivek M. Bhasi, Sonali Singh, George Kesidis, Mahmut T. Kandemir, Chita R. Das

We propose a novel GPU-cluster scheduler for distributed DL (DDL) workloads that enables proximity based consolidation of GPU resources based on the DDL jobs' sensitivities to the anticipated communication-network delays.

Scheduling

Analysis of Distributed Deep Learning in the Cloud

no code implementations • 30 Aug 2022 • Aakash Sharma, Vivek M. Bhasi, Sonali Singh, Rishabh Jain, Jashwant Raj Gunasekaran, Subrata Mitra, Mahmut Taylan Kandemir, George Kesidis, Chita R. Das

We aim to resolve this problem by introducing a comprehensive distributed deep learning (DDL) profiler, which can determine the various execution "stalls" that DDL suffers from while running on a public cloud.

Cocktail: Leveraging Ensemble Learning for Optimized Model Serving in Public Cloud

no code implementations • 9 Jun 2021 • Jashwant Raj Gunasekaran, Cyan Subhra Mishra, Prashanth Thinakaran, Mahmut Taylan Kandemir, Chita R. Das

Towards this, we proposeCocktail, a costeffective ensembling-based model serving framework. Cock-tailcomprises of two key components: (i) a dynamic modelselection framework, which reduces the number of modelsin the ensemble, while satisfying the accuracy and latencyrequirements; (ii) an adaptive resource management (RM)framework that employs a distributed proactive autoscalingpolicy combined with importance sampling, to efficiently allo-cate resources for the models.

Ensemble Learning Management