Image Processing on Raspberry Pi Cluster

Abstract

The development direction of the high-performance computing has been primarily oriented toward improvements in thenumber of computing units, and their better organization and interconnection. The central processing units in modern mainframes arein some cases inadequate for data parallelization, because a large amount of data requires a large number of processing units. Thisproblem can be partially overcome by introducing embedded devices with enough processing power and with smaller power dissipation,but the new problems emerge, specifically the problem of their interconnection in a distributed environment. One of such devices is aRaspberry Pi board, which has four processing cores in the latest revision, and we combined four of such devices in a cluster. Thesedevices are interconnected via Ethernet router and by using MPI interface, they can be programmed to work as a single unit. Wesupplied a set of images that were processed on the cluster and measured its performance.

Distributed Multi-Ant Colony System Algorithm using Raspberry Pi Cluster for Travelling Salesman Problem

“…RPi cluster with open-source libraries such as Hadoop and OpenCV could play a pivotal role in image processing e.g. feature points extraction system using Speeded Up Robust Features (SURF) algorithm [27], face recognition [28], image stacking [29], edge detection [30], [31], image analysis [32], ray tracing [33], image conversion [34], image recognition [35], Fourier transform [36] and image classification [37]. Moreover, a RPi cluster can extend its capability in image processing by adding a GPU [38].…”

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“…Parallel computing integrates independent computing resources into a coherent system that can achieve superior performance to conventional computing by executing numerous tasks in parallel [3]. Parallel computing is achieved with a message-passing interface (MPI) [4].…”

“…Similarly, numerous scholars have investigated computing clusters assembled from Raspberry Pi and analyzed the performance of these clusters for various algorithms, core count, node count, costs, and power consumption [10][11][12][13][14][15]. Moreover, researchers have used Raspberry Pi in education and demonstrated that an interactive learning environment incorporating the platform increased student engagement in atmospheric or oceanic studies.…”

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“…In the newest update, A single Raspberry Pi board contains four processing cores. [3] Each unit processes simultaneously via Messaging Passing Interface (MPI) between the processors in the form of messages on a cluster system. It consists of 8 nodes of Raspberry Pi and measures the instruction set's performance in decimal per second or Floating-point Operation Per Second (FLOPS) with High-Performance Linpack (HPL) program.…”