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Performance Benchmarking the Insight Toolkit

McCormick, Matthew, Kang, Hyun Jae, Barre, Sebastien
University of Wisconsin-Madison
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Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/3557
New: Prefer using the following doi: https://doi.org/10.54294/9feae9
Published in The Insight Journal - 2016 January-December.
Submitted by Matthew Mccormick on 2016-07-01 16:07:27.

This document describes a module for the Insight Toolkit (ITK) to assist in performance benchmarking and a suite of benchmarks for the toolkit. These resources provide metrics to quantify software computational performance. This is a pre-requisite to improve performance either through algorithmic advancements or better utilization of hardware resources. Computational performance is quantified by reducing factors that confound timing measurements and by estimating measurement variance. New classes are presented that increase operating system process priority to minimize the impact of other processes running on the system. System hardware characteristics are extracted and displayed. The influence of hard disk input/output on runtime measurements is removed in the suite of benchmarks. Additionally, the number of threads used by each benchmark can be specified. These benchmarks consist of common analysis pipelines and run on 3D magnetic resonance brain image data so realistic performance is quantified. Benchmarks can be executed with multiple iterations, and timing statistics are recorded in tab separated value (.tsv) files, which are easily stored or processed for further analysis and visualization. This paper is accompanied with the source code, input data, parameters and output data that the authors used for validating the algorithm described in this paper. This adheres to the fundamental principle that scientific publications must facilitate reproducibility of the reported results.