News

• LineFS wins the Best Paper Award at SOSP’21.
• Congratulations Atal! Rethinking gradient sparsification as total error minimization accepted as spotlight paper (top 3%) at NeurIPS’21.
• How expressive of an offload does RDMA support? We find that the answer is a whole lot! RedN accepted at NSDI’22.
• OmniReduce accepted at SIGCOMM’21. Congratulations Jiawei and Elton! Also, we will run a tutorial on Network-Accelerated Distributed Deep Learning at SIGCOMM. Stay tuned.
• GRACE accepted at ICDCS’21.
• SIDCo, an efficient gradient compression technique for distributed deep learning accepted at MLSys’21.
• SwitchML accepted at NSDI’21.
• Congratulations Arnaud and Ahmed! Two papers accepted at INFOCOM’21.
• Congratulations Waleed! Assise is accepted at OSDI’20.
• Bilal gets papers in at VLDB’20 and SoCC’20! In the VLDB paper, we systematically investigate the problem of cloud configuration using black-box optimization methods and uncover how different methods behave with more than 20 workloads. At SoCC, we propose a new method, Vanir, that reins in configuring data analytics clusters formed by multiple distributed systems whose joint optimization is required.
• We survey popular gradient compression techniques for distributed deep learning and perform a comprehensive comparative evaluation. Read our technical report.
• Is there a discrepancy between the theory and practice of gradient compression for distributed deep learning? We argue so in our AAAI’20 paper.
• Our paper on improving how to reason about and explain the behavior of reinforcement learning agents in networking applications accepted at NetAI’19.
• A paper on the DAIET project published at HotNets’17.