Abstract
This study explores the performance of Apple Silicon processors in real-world research tasks, with a specific focus on optimization and Machine Learning applications. Diverging from conventional benchmarks, various algorithms across fundamental datasets have been assessed using diverse hardware configurations, including Apple’s M1 and M2 processors, NVIDIA RTX 3090 GPU and a mid-range laptop. The M2 demonstrates competitiveness in tasks such as BreastCancer, liver and yeast classification, establishing it as a suitable platform for practical applications. Conversely, the dedicated GPU outperformed M1 and M2 on the eyestate1 dataset, underscoring its superiority in handling more complex tasks, albeit at the expense of substantial power consumption. With the technology advances, Apple Silicon emerges as a compelling choice for real-world applications, warranting further exploration and research in chip development. This study underscores the critical role of device specifications in evaluating Machine Learning algorithms.
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Struniawski, K., Konopka, A., Kozera, R. (2024). Exploring Apple Silicon’s Potential from Simulation and Optimization Perspective. In: Franco, L., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2024. ICCS 2024. Lecture Notes in Computer Science, vol 14836. Springer, Cham. https://doi.org/10.1007/978-3-031-63775-9_3
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