187 citations · 1k across the 59 of their papers we have counts for
5 papers · 1 filter
Integrated Model, Batch and Domain Parallelism in Training Neural Networks
Amir Gholami, Ariful Azad, Peter Jin +2
We propose a new integrated method of exploiting model, batch and domain parallelism for the training of deep neural networks (DNNs) on large distributed-memory computers using min…
Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation
Penporn Koanantakool, Alnur Ali, Ariful Azad +5
Across a variety of scientific disciplines, sparse inverse covariance estimation is a popular tool for capturing the underlying dependency relationships in multivariate data. Unfor…
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…
Extreme-Scale De Novo Genome Assembly
Evangelos Georganas, Steven Hofmeyr, Rob Egan +4
De novo whole genome assembly reconstructs genomic sequence from short, overlapping, and potentially erroneous DNA segments and is one of the most important computations in modern…
Distributed-Memory Breadth-First Search on Massive Graphs
Aydin Buluc, Scott Beamer, Kamesh Madduri +2
This chapter studies the problem of traversing large graphs using the breadth-first search order on distributed-memory supercomputers. We consider both the traditional level-synchr…