63 citations · 81 across the 4 of their papers we have counts for
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stat.ML2019
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations
Debraj Basu, Deepesh Data, Can Karakus +1
Communication bottleneck has been identified as a significant issue in distributed optimization of large-scale learning models. Recently, several approaches to mitigate this proble…
stat.ML2018
Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning
Can Karakus, Yifan Sun, Suhas Diggavi +1
Performance of distributed optimization and learning systems is bottlenecked by "straggler" nodes and slow communication links, which significantly delay computation. We propose a…
stat.ML2017★ 63 cited
Straggler Mitigation in Distributed Optimization Through Data Encoding
Can Karakus, Yifan Sun, Suhas Diggavi +1
Slow running or straggler tasks can significantly reduce computation speed in distributed computation. Recently, coding-theory-inspired approaches have been applied to mitigate the…