3 citations · 4 across the 4 of their papers we have counts for
6 papers
Mixed-Precision Communication-Avoiding SGD for Generalized Linear Models on GPUs
Aditya Devarakonda, Irene Simó Muñoz, Giulia Guidi
Distributed stochastic gradient descent (SGD) is limited by communication rather than computation, since each iteration requires an AllReduce across processes. Communication-avoidi…
High-Performance Star-M SVD for Big Data Compression
Md Taufique Hussain, Grey Ballard, Aditya Devarakonda +3
In the era of big data, effectively compressing large datasets while performing complex mathematical operations is crucial. Tensor-based decomposition methods have shown superior c…
Avoiding Communication in Logistic Regression
Aditya Devarakonda, James Demmel
Stochastic gradient descent (SGD) is one of the most widely used optimization methods for solving various machine learning problems. SGD solves an optimization problem by iterative…
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…
Avoiding communication in primal and dual block coordinate descent methods
Aditya Devarakonda, Kimon Fountoulakis, James Demmel +1
Primal and dual block coordinate descent methods are iterative methods for solving regularized and unregularized optimization problems. Distributed-memory parallel implementations…