Performance Evaluation of Raspberry Pi 4B Microcomputer: Case Studies on MPICH Cluster, VMware ESXi ARM Fling, and Windows 11 ARM OS

Abstract

The performance of the Raspberry Pi 4B computer was evaluated for three cases. First, a Raspberry Pi heterogeneous MPICH cluster with weight-based load balancing and fuzzy estimation of node computational performance was designed. Fuzzification, formation of fuzzy rules, fuzzy inference, and defuzzification were employed to determine the performance weights. In the cluster with two Raspberry Pi 4B boards with 2 GB RAM and Raspberry Pi 64-bit OS and one Raspberry Pi 3B board with 1 GB RAM and Raspberry Pi 32-bit OS, the recommended performance weights are (5, 5, 1), respectively. The developed Python program for the prime numbers finding algorithm employs the proposed weight-based load balancing, which is approximately five times faster than the basic algorithm with equal loading for the maximum integer of 300000. Second, the MPICH cluster with two nodes in two virtual machines located on two different Raspberry Pi 4B boards with Ubuntu Server for ARM on the hypervisor VMware ESXi ARM Fling shows the mean signed deviation −34.01 s regarding the Raspberry Pi 64-bit OS for the maximum integer of 300000. Third, the performance of the Raspberry Pi 4B 8 GB computer with Windows 11 ARM OS was compared with the laptop Lenovo G510 with Intel Core i7-4700MQ and Windows 10 64-bit OS using the combinatorial optimization algorithm implemented in the 32-bit Windows app. The Raspberry Pi 4B 8 GB consumed approximately six times more power. Thus, the Raspberry Pi 4B single-board computer is recommended for executing low-performance applications and/or short-term processing of high-performance tasks.