2 citations · 4 across the 15 of their papers we have counts for
4 papers · 1 filter
Tesserae: Scalable Placement Policies for Deep Learning Workloads
Song Bian, Saurabh Agarwal, Md. Tareq Mahmood +1
Training deep learning (DL) models has become a dominant workload in data-centers and improving resource utilization is a key goal of DL cluster schedulers. In order to do this, sc…
SYMPHONY: Improving Memory Management for LLM Inference Workloads
Saurabh Agarwal, Anyong Mao, Aditya Akella +1
Large Language Models (LLMs) are increasingly being deployed in applications such as chatbots, code editors, and conversational agents. A key feature of LLMs is their ability to en…
PAL: A Variability-Aware Policy for Scheduling ML Workloads in GPU Clusters
Rutwik Jain, Brandon Tran, Keting Chen +2
Large-scale computing systems are increasingly using accelerators such as GPUs to enable peta- and exa-scale levels of compute to meet the needs of Machine Learning (ML) and scient…
Blox: A Modular Toolkit for Deep Learning Schedulers
Saurabh Agarwal, Amar Phanishayee, Shivaram Venkataraman
Deep Learning (DL) workloads have rapidly increased in popularity in enterprise clusters and several new cluster schedulers have been proposed in recent years to support these work…