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Parameter Sensitivity Analysis of the SparTen High Performance Sparse Tensor Decomposition Software (Extended Analysis)

Myers, Jeremy M.; Dunlavy, Daniel D.; Teranishi, Keita T.; Hollman, David S.

Tensor decomposition models play an increasingly important role in modern data science applications. One problem of particular interest is fitting a low-rank Canonical Polyadic (CP) tensor decomposition model when the tensor has sparse structure and the tensor elements are nonnegative count data. SparTen is a high-performance C++ library which computes a low-rank decomposition using different solvers: a first-order quasi-Newton or a second-order damped Newton method, along with the appropriate choice of runtime parameters. Since default parameters in SparTen are tuned to experimental results in prior published work on a single real-world dataset conducted using MATLAB implementations of these methods, it remains unclear if the parameter defaults in SparTen are appropriate for general tensor data. Furthermore, it is unknown how sensitive algorithm convergence is to changes in the input parameter values. This report addresses these unresolved issues with large-scale experimentation on three benchmark tensor data sets. Experiments were conducted on several different CPU architectures and replicated with many initial states to establish generalized profiles of algorithm convergence behavior.

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TYPE Other Report YEAR 2020
DOIOSTI

Parameter Sensitivity Analysis of the SparTen High Performance Sparse Tensor Decomposition Software

2020 IEEE High Performance Extreme Computing Conference, HPEC 2020

Myers, Jeremy M.; Dunlavy, Daniel D.; Teranishi, Keita T.; Hollman, David S.

Tensor decomposition models play an increasingly important role in modern data science applications. One problem of particular interest is fitting a low-rank Canonical Polyadic (CP) tensor decomposition model when the tensor has sparse structure and the tensor elements are nonnegative count data. SparTen is a high-performance C++ library which computes a low-rank decomposition using different solvers: a first-order quasi-Newton or a second-order damped Newton method, along with the appropriate choice of runtime parameters. Since default parameters in SparTen are tuned to experimental results in prior published work on a single real-world dataset conducted using MATLAB implementations of these methods, it remains unclear if the parameter defaults in SparTen are appropriate for general tensor data. Furthermore, it is unknown how sensitive algorithm convergence is to changes in the input parameter values. This report addresses these unresolved issues with large-scale experimentation on three benchmark tensor data sets. Experiments were conducted on several different CPU architectures and replicated with many initial states to establish generalized profiles of algorithm convergence behavior.

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TYPE Conference Poster YEAR 2020
OSTIScopus

Making openMP ready for c++ executors

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Scogland, Thomas R.W.; Sunderland, Daniel S.; Olivier, Stephen L.; Hollman, David S.; Evans, Noah; De Supinski, Bronis R.

For at least the last 20 years, many have tried to create a general resource management system to support interoperability across various concurrent libraries. The previous strategies all suffered from additional toolchain requirements, and/or a usage of a shared programing model that assumed it owned/controlled access to all resources available to the program. None of these techniques have achieved wide spread adoption. The ubiquity of OpenMP coupled with C++ developing a standard way to describe many different concurrent paradigms (C++23 executors) would allow OpenMP to assume the role of a general resource manager without requiring user code written directly in OpenMP. With a few added features such as the ability to use otherwise idle threads to execute tasks and to specify a task “width”, many interesting concurrent frameworks could be developed in native OpenMP and achieve high performance. Further, one could create concrete C++ OpenMP executors that enable support for general C++ executor based codes, which would allow Fortran, C, and C++ codes to use the same underlying concurrent framework when expressed as native OpenMP or using language specific features. Effectively, OpenMP would become the de facto solution for a problem that has long plagued the HPC community.

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TYPE Conference Poster YEAR 2019
OSTIScopus
Results 1–25 of 51
Results 1–25 of 51