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.

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. Principal Component Analysis (PCA) is a well-known dimensionality reduction technique used in the field of hyperspectral satellite images. However, PCA suffers from high computational costs and increased complexity, an issue that led to elaborating PCA adaptations capable of running on multi-core computing architectures. This paper proposes a parallel implementation of the geometrical PCA approximation (gaPCA) algorithm. Three parallel implementations are studied: two on multi-core CPUs and a NVIDIA Graphics Processing Units (GPU) CUDA accelerated implementation. Our results show significant speedups of the parallel implementations when applied on hyperspectral image datasets. Our results show that on the Intel Core i5 CPU, Python multi-core implementation is up to 2.01\times faster than its Matlab equivalent. Our GPU PyCUDA implementation is considerably faster than both our Python multi-core CPU implementations: up to 1.76\times faster than Intel Core i5-6200U and up to 5.72\times faster than the NVIDIA Jetson Nano quad-core ARM A53 CPU. We performed data analysis on the output data for the three methods and the maxim relative error was less than 0.001%.