no code implementations • 31 Jul 2023 • Aditya Devarakonda, Grey Ballard
In this work, we design, analyze, and optimize sequential and shared-memory parallel algorithms for partitioned local depths (PaLD).
no code implementations • 16 Nov 2020 • Aditya Devarakonda, James Demmel
Stochastic gradient descent (SGD) is one of the most widely used optimization methods for solving various machine learning problems.
no code implementations • 17 Dec 2017 • Aditya Devarakonda, Kimon Fountoulakis, James Demmel, Michael W. Mahoney
Parallel computing has played an important role in speeding up convex optimization methods for big data analytics and large-scale machine learning (ML).
1 code implementation • 6 Dec 2017 • Aditya Devarakonda, Maxim Naumov, Michael Garland
Training deep neural networks with Stochastic Gradient Descent, or its variants, requires careful choice of both learning rate and batch size.
no code implementations • 24 Oct 2017 • Saeed Soori, Aditya Devarakonda, James Demmel, Mert Gurbuzbalaban, Maryam Mehri Dehnavi
We formulate the algorithm for two different optimization methods on the Lasso problem and show that the latency cost is reduced by a factor of k while bandwidth and floating-point operation costs remain the same.
1 code implementation • 5 Jul 2016 • Alex Gittens, Aditya Devarakonda, Evan Racah, Michael Ringenburg, Lisa Gerhardt, Jey Kottalam, Jialin Liu, Kristyn Maschhoff, Shane Canon, Jatin Chhugani, Pramod Sharma, Jiyan Yang, James Demmel, Jim Harrell, Venkat Krishnamurthy, Michael W. Mahoney, Prabhat
We explore the trade-offs of performing linear algebra using Apache Spark, compared to traditional C and MPI implementations on HPC platforms.
Distributed, Parallel, and Cluster Computing G.1.3; C.2.4