YoVDO

Conspirator - SmartNIC-Aided Control Plane for Distributed ML Workloads

Offered By: USENIX via YouTube

Tags

Distributed Machine Learning Courses Cost Optimization Courses RDMA Courses SmartNICs Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore a groundbreaking conference talk from USENIX ATC '24 that introduces Conspirator, an innovative control plane design for distributed machine learning workloads. Delve into how this novel approach leverages SmartNICs to address CPU bottlenecks and suboptimal accelerator scheduling simultaneously. Learn about Conspirator's ability to facilitate efficient data transfer without host CPU involvement and its integration of a new scheduling algorithm that adapts to heterogeneous accelerators and changing workload dynamics. Discover the significant improvements Conspirator offers, including a 15% reduction in end-to-end completion time compared to RDMA-based alternatives, 17% better cost-effectiveness, 44% improved power efficiency, and a 33% reduction in GPU hours through optimized scheduling decisions. Gain insights into the evolving role of SmartNICs and their potential to revolutionize distributed ML workload management in this 18-minute presentation by researchers from Northwestern University and Hewlett Packard Labs.

Syllabus

USENIX ATC '24 - Conspirator: SmartNIC-Aided Control Plane for Distributed ML Workloads


Taught by

USENIX

Related Courses

Scalable Data Science
Indian Institute of Technology, Kharagpur via Swayam
Data Science and Engineering with Spark
Berkeley University of California via edX
Data Science on Google Cloud: Machine Learning
Google via Qwiklabs
Modern Distributed Systems
Delft University of Technology via edX
KungFu - Making Training in Distributed Machine Learning Adaptive
USENIX via YouTube