Abstract
Remote sensing data has known an explosive growth in the past decade. This has led to the need for efficient dimensionality reduction techniques, mathematical procedures that transform the high-dimensional data into a meaningful, reduced representation. Projection Pursuit (PP) based algorithms were shown to be efficient solutions for performing dimensionality reduction on large datasets by searching low-dimensional projections of the data where meaningful structures are exposed. However, PP faces computational difficulties in dealing with very large datasets—which are common in hyperspectral imaging, thus raising the challenge for implementing such algorithms using the latest High Performance Computing approaches. In this paper, a PP-based geometrical approximated Principal Component Analysis algorithm (gaPCA) for hyperspectral image analysis is implemented and assessed on multi-core Central Processing Units (CPUs), Graphics Processing Units (GPUs) and multi-core CPUs using Single Instruction, Multiple Data (SIMD) AVX2 (Advanced Vector eXtensions) intrinsics, which provide significant improvements in performance and energy usage over the single-core implementation. Thus, this paper presents a cross-platform and cross-language perspective, having several implementations of the gaPCA algorithm in Matlab, Python, C++ and GPU implementations based on NVIDIA Compute Unified Device Architecture (CUDA). The evaluation of the proposed solutions is performed with respect to the execution time and energy consumption. The experimental evaluation has shown not only the advantage of using CUDA programming in implementing the gaPCA algorithm on a GPU in terms of performance and energy consumption, but also significant benefits in implementing it on the multi-core CPU using AVX2 intrinsics.
Highlights
Today, enormous amounts of data are being generated on a daily basis from social networks, sensors and web sites [1]
We introduce four implementations of the geometrical approximated Principal Component Analysis algorithm (gaPCA) algorithm: three targeting multi-core Central Processing Units (CPUs) developed in Matlab, Python and C++ and a Graphics Processing Units (GPUs)-accelerated Compute Unified Device Architecture (CUDA) implementation;
This paper addresses the computational difficulties of a Projection Pursuit method named geometrical approximated Principal Component Analysis (PCA), which is based on the idea of finding the projection defined by the maximum Euclidean distance between the points
Summary
Enormous amounts of data are being generated on a daily basis from social networks, sensors and web sites [1]. The availability of new space missions providing large amounts of data on a daily basis has raised important challenges for better processing techniques and the development of computationally efficient techniques for transforming the massive amount of remote sensing data into scientific understanding. Because much of the data are highly redundant, it can be efficiently brought down to a much smaller number of variables without a significant loss of information. This can be achieved using the so-called Dimensionality Reduction (DR) techniques [4]. One of the most popular PI is the variance of the projected data, defined by the largest Principal Components (PCs) of the PCA
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