42 citations · 54 across the 7 of their papers we have counts for
7 papers
Running Alchemist on Cray XC and CS Series Supercomputers: Dask and PySpark Interfaces, Deployment Options, and Data Transfer Times
Kai Rothauge, Haripriya Ayyalasomayajula, Kristyn J. Maschhoff +2
Alchemist is a system that allows Apache Spark to achieve better performance by interfacing with HPC libraries for large-scale distributed computations. In this paper, we highlight…
Residual Networks as Nonlinear Systems: Stability Analysis using Linearization
Kai Rothauge, Zhewei Yao, Zixi Hu +1
We regard pre-trained residual networks (ResNets) as nonlinear systems and use linearization, a common method used in the qualitative analysis of nonlinear systems, to understand t…
On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent
Noah Golmant, Nikita Vemuri, Zhewei Yao +5
Increasing the mini-batch size for stochastic gradient descent offers significant opportunities to reduce wall-clock training time, but there are a variety of theoretical and syste…
Alchemist: An Apache Spark <=> MPI Interface
Alex Gittens, Kai Rothauge, Shusen Wang +6
The Apache Spark framework for distributed computation is popular in the data analytics community due to its ease of use, but its MapReduce-style programming model can incur signif…
Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist
Alex Gittens, Kai Rothauge, Shusen Wang +6
Apache Spark is a popular system aimed at the analysis of large data sets, but recent studies have shown that certain computations---in particular, many linear algebra computations…
The Discrete Adjoint Method for Exponential Integration
Kai Rothauge, Eldad Haber, Uri Ascher
The implementation of the discrete adjoint method for exponential time differencing (ETD) schemes is considered. This is important for parameter estimation problems that are constr…
Numerical Computation of the Gradient and the Action of the Hessian for Time-Dependent PDE-Constrained Optimization Problems
Kai Rothauge, Eldad Haber, Uri Ascher
We present a systematic derivation of the algorithms required for computing the gradient and the action of the Hessian of an arbitrary misfit function for large-scale parameter est…