Paul D. Hovland

Bio: Paul D. Hovland is an academic researcher from Argonne National Laboratory. The author has contributed to research in topics: Automatic differentiation & Computer science. The author has an hindex of 23, co-authored 116 publications receiving 2215 citations.

ADIFOR-Generating Derivative Codes from Fortran Programs

Abstract: The numerical methods employed in the solution of many scientific computing problems require the computation of derivatives of a function f $R^N$→$R^m$ Both the accuracy and the computational requirements of the derivative computation are usually of critical importance for the robustness and speed of the numerical solution Automatic Differentiation of FORtran (ADIFOR) is a source transformation tool that accepts Fortran 77 code for the computation of a function and writes portable Fortran 77 code for the computation of the derivatives In contrast to previous approaches, ADIFOR views automatic differentiation as a source transformation problem ADIFOR employs the data analysis capabilities of the ParaScope Parallel Programming Environment, which enable us to handle arbitrary Fortran 77 codes and to exploit the computational context in the computation of derivatives Experimental results show that ADIFOR can handle real-life codes and that ADIFOR-generated codes are competitive with divided-difference approximations of derivatives In addition, studies suggest that the source transformation approach to automatic differentiation may improve the time to compute derivatives by orders of magnitude

Further Research on Feature Selection and Classification Using Genetic Algorithms

Abstract: This paper summarizes work on an approach that combines feature selection and data classiication using Genetic Algorithms. First, it describes our use of Genetic Algorithms combined with a K-nearest neighbor algorithm to optimize classiication by searching for an optimal feature weight-ing, essentially warping the feature space to coalesce individuals within groups and to separate groups from one another. This approach has proven especially useful with large data sets where standard feature selection techniques are computationally expensive. Second, it describes our implementation of the approach in a parallel processing environment, giving nearly linear speed-up in processing time. Third, it will summarize our present results in using the technique to discover the relative importance of features in large biological test sets. Finally, it will indicate areas for future research. 1 The Problem We live in the age of information where data is plentiful , to the extent that we are typically unable to process all of it usefully. Computer science has been challenged to discover approaches that can sort through the mountains of data available and discover the essential features needed to answer a speciic question. These approaches must be able to process large quantities of data, in reasonable time and in the presence of oisy" data i.e., irrelevant or erroneous data. Consider a typical example in biology. Researchers in the Center for Microbial Ecology (CME) have selected soil samples from three environments found in agriculture. The environments were: near the roots of a crop (rhizosphere), away from the innuence of the crop roots (non-rhizosphere), and from a fallow eld (crop residue). The CME researchers wished to investigate whether samples from those three environments could be distinguished. In particular, they wanted to see if diversity decreased in the rhizosphere as a result of the symbiotic relationship between the roots and its near-neighbor microbes, and if so in what ways. Their rst experiments used the Biolog c test as the discriminator. Biolog consists of a plate of 96 wells, with a diierent substrate in each well. These sub-strates (various sugars, amino acids and other nutrients) are assimilated by some microbes and not by others. If the microbial sample processes the substrate in the well, that well changes color which can be recorded photometrically. Thus large numbers of samples can be processed and characterized based on the substrates they can assimilate. The CME researchers applied the Biolog test to 3 sets of 100 samples …

Applied Mathematics Research for Exascale Computing

Abstract: Cover art by George Kitrinos, a derivative of " Circuit board elements background " from freedesign-file.com, used under Creative Commons Attribution 3.0. Equations from a far-field approximation of the Green's function solution to the acoustic analogy equation with thermoacoustic sources. Government nor any agency thereof, nor any of their employees or officers, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of document authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof and shall not be used for advertising or product endorsement purposes. Abstract This report details the findings and recommendations of the DOE ASCR Exascale Mathematics Working Group that was chartered to identify mathematics and algorithms research opportunities that will enable scientific applications to harness the potential of exascale computing. The working group organized a workshop, held August 21-22, 2013 in Washington, D.C., to solicit input from over seventy members of the applied mathematics community. Research gaps, approaches, and directions across the breadth of applied mathematics were discussed, and this report synthesizes these perspectives into an integrated outlook on the applied mathematics research necessary to achieve scientific breakthroughs using exascale systems.

Advances in Automatic Differentiation

Abstract: This collection covers advances in automatic differentiation theory and practice. Computer scientists and mathematicians will learn about recent developments in automatic differentiation theory as well as mechanisms for the construction of robust and powerful automatic differentiation tools. Computational scientists and engineers will benefit from the discussion of various applications, which provide insight into effective strategies for using automatic differentiation for inverse problems and design optimization.

Data-Flow Analysis for MPI Programs

Abstract: Message passing via MPI is widely used in single-program, multiple-data (SPMD) parallel programs. Existing data-flow frameworks do not model the semantics of message-passing SPMD programs, which can result in less precise and even incorrect analysis results. We present a data-flow analysis framework for performing interprocedural analysis of message-passing SPMD programs. The framework is based on the MPI-ICFG representation, which is an interprocedural control-flow graph (ICFG) augmented with communication edges between possible send and receive pairs and partial context sensitivity. We show how to formulate nonseparable data-flow analyses within our framework using reaching constants as a canonical example. We also formulate and provide experimental results for the nonseparable analysis, activity analysis. Activity analysis is a domain-specific analysis used to reduce the computation and storage requirements for automatically differentiated MPI programs. Automatic differentiation is important for application domains such as climate modeling, electronic device simulation, oil reservoir simulation, medical treatment planning and computational economics to name a few. Our experimental results show that using the MPI-ICFG data-flow analysis framework improves the precision of activity analysis and as a result significantly reduces memory requirements for the automatically differentiated versions of a set of parallel benchmarks, including some of the NAS parallel benchmarks

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Automated Solution of Differential Equations by the Finite Element Method: The FEniCS Book

Abstract: This book is a tutorial written by researchers and developers behind the FEniCS Project and explores an advanced, expressive approach to the development of mathematical software. The presentation spans mathematical background, software design and the use of FEniCS in applications. Theoretical aspects are complemented with computer code which is available as free/open source software. The book begins with a special introductory tutorial for beginners. Followingare chapters in Part I addressing fundamental aspects of the approach to automating the creation of finite element solvers. Chapters in Part II address the design and implementation of the FEnicS software. Chapters in Part III present the application of FEniCS to a wide range of applications, including fluid flow, solid mechanics, electromagnetics and geophysics.

Feature selection: evaluation, application, and small sample performance

Abstract: A large number of algorithms have been proposed for feature subset selection. Our experimental results show that the sequential forward floating selection algorithm, proposed by Pudil et al. (1994), dominates the other algorithms tested. We study the problem of choosing an optimal feature set for land use classification based on SAR satellite images using four different texture models. Pooling features derived from different texture models, followed by a feature selection results in a substantial improvement in the classification accuracy. We also illustrate the dangers of using feature selection in small sample size situations.