Search Results for author:
Found 2 papers, 0 papers with code
GPU Cluster Scheduling for Network-Sensitive Deep Learning
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.
Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks
To solve the performance degradation, we propose to apply $\text{{Global Server Corrections}}$ on the server to refine the locally learned models.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
