63 citations · 114 across the 4 of their papers we have counts for
7 papers
L-DQN: An Asynchronous Limited-Memory Distributed Quasi-Newton Method
Bugra Can, Saeed Soori, Maryam Mehri Dehnavi +1
This work proposes a distributed algorithm for solving empirical risk minimization problems, called L-DQN, under the master/worker communication model. L-DQN is a distributed limit…
ASYNC: A Cloud Engine with Asynchrony and History for Distributed Machine Learning
Saeed Soori, Bugra Can, Mert Gurbuzbalaba +1
ASYNC is a framework that supports the implementation of asynchrony and history for optimization methods on distributed computing platforms. The popularity of asynchronous optimiza…
DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate
Saeed Soori, Konstantin Mischenko, Aryan Mokhtari +2
In this paper, we consider distributed algorithms for solving the empirical risk minimization problem under the master/worker communication model. We develop a distributed asynchro…
MatRox: Modular approach for improving data locality in Hierarchical (Mat)rix App(Rox)imation
Bangtian Liu, Kazem Cheshmi, Saeed Soori +2
Hierarchical matrix approximations have gained significant traction in the machine learning and scientific community as they exploit available low-rank structures in kernel methods…
Sparse Matrix Code Dependence Analysis Simplification at Compile Time
Mahdi Soltan Mohammadi, Kazem Cheshmi, Ganesh Gopalakrishnan +5
Analyzing array-based computations to determine data dependences is useful for many applications including automatic parallelization, race detection, computation and communication…
A Unified Optimization Approach for Sparse Tensor Operations on GPUs
Bangtian Liu, Chengyao Wen, Anand D. Sarwate +1
Sparse tensors appear in many large-scale applications with multidimensional and sparse data. While multidimensional sparse data often need to be processed on manycore processors,…