961 citations · 1.4k across the 33 of their papers we have counts for
5 papers · 1 filter
Shockwave: Fair and Efficient Cluster Scheduling for Dynamic Adaptation in Machine Learning
Pengfei Zheng, Rui Pan, Tarannum Khan +2
Dynamic adaptation has become an essential technique in accelerating distributed machine learning (ML) training. Recent studies have shown that dynamically adjusting model structur…
Not All GPUs Are Created Equal: Characterizing Variability in Large-Scale, Accelerator-Rich Systems
Prasoon Sinha, Akhil Guliani, Rutwik Jain +3
Scientists are increasingly exploring and utilizing the massive parallelism of general-purpose accelerators such as GPUs for scientific breakthroughs. As a result, datacenters, hyp…
LlamaTune: Sample-Efficient DBMS Configuration Tuning
Konstantinos Kanellis, Cong Ding, Brian Kroth +3
Tuning a database system to achieve optimal performance on a given workload is a long-standing problem in the database community. A number of recent works have leveraged ML-based a…
BagPipe: Accelerating Deep Recommendation Model Training
Saurabh Agarwal, Chengpo Yan, Ziyi Zhang +1
Deep learning based recommendation models (DLRM) are widely used in several business critical applications. Training such recommendation models efficiently is challenging because t…
MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks
Roger Waleffe, Jason Mohoney, Theodoros Rekatsinas +1
We study training of Graph Neural Networks (GNNs) for large-scale graphs. We revisit the premise of using distributed training for billion-scale graphs and show that for graphs tha…