95 citations · 190 across the 8 of their papers we have counts for
8 papers
PACEMAKER: Avoiding HeART attacks in storage clusters with disk-adaptive redundancy
Saurabh Kadekodi, Francisco Maturana, Suhas Jayaram Subramanya +3
Data redundancy provides resilience in large-scale storage clusters, but imposes significant cost overhead. Substantial space-savings can be realized by tuning redundancy schemes t…
Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning
Aurick Qiao, Sang Keun Choe, Suhas Jayaram Subramanya +5
Pollux improves scheduling performance in deep learning (DL) clusters by adaptively co-optimizing inter-dependent factors both at the per-job level and at the cluster-wide level. M…
Vilamb: Low Overhead Asynchronous Redundancy for Direct Access NVM
Rajat Kateja, Andy Pavlo, Gregory R. Ganger
Vilamb provides efficient asynchronous systemredundancy for direct access (DAX) non-volatile memory (NVM) storage. Production storage deployments often use system-redundancy in for…
Accelerating Deep Learning by Focusing on the Biggest Losers
Angela H. Jiang, Daniel L. -K. Wong, Giulio Zhou +8
This paper introduces Selective-Backprop, a technique that accelerates the training of deep neural networks (DNNs) by prioritizing examples with high loss at each iteration. Select…
Tvarak: Software-managed hardware offload for DAX NVM storage redundancy
Rajat Kateja, Nathan Beckmann, Gregory R. Ganger
Tvarak efficiently implements system-level redundancy for direct-access (DAX) NVM storage. Production storage systems complement device-level ECC (which covers media errors) with s…
MLSys: The New Frontier of Machine Learning Systems
Alexander Ratner, Dan Alistarh, Gustavo Alonso +66
Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains…