282 citations · 1k across the 43 of their papers we have counts for
4 papers · 2 filters
Avoiding Synchronization in First-Order Methods for Sparse Convex Optimization
Aditya Devarakonda, Kimon Fountoulakis, James Demmel +1
Parallel computing has played an important role in speeding up convex optimization methods for big data analytics and large-scale machine learning (ML). However, the scalability of…
Avoiding Communication in Proximal Methods for Convex Optimization Problems
Saeed Soori, Aditya Devarakonda, James Demmel +2
The fast iterative soft thresholding algorithm (FISTA) is used to solve convex regularized optimization problems in machine learning. Distributed implementations of the algorithm h…
Scaling Deep Learning on GPU and Knights Landing clusters
Yang You, Aydin Buluc, James Demmel
The speed of deep neural networks training has become a big bottleneck of deep learning research and development. For example, training GoogleNet by ImageNet dataset on one Nvidia…
Communication Lower Bounds of Bilinear Algorithms for Symmetric Tensor Contractions
Edgar Solomonik, James Demmel, Torsten Hoefler
We introduce a new theoretical framework for deriving lower bounds on data movement in bilinear algorithms. Bilinear algorithms are a general representation of fast algorithms for…