Browsing by Author "Youssef, Johnny"

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Open Access
The influence of operating system on the energy consumption of software and algorithms
(2022) Youssef, Johnny
Energy consumption of computers rises with the continuous development of more powerful and complex components. To counter the increase in energy consumption resulting from this continuous developments, several solutions were implemented over the years including manufacturing more efficient hardware by shrinking the size of various components like the transistor or optimizing the software used to consume less energy. Many people nowadays use separate computers for work and entertainment with each one being used mainly to perform one or a couple of specific tasks like writing documents, compiling code or video editing. This thesis investigates if the operating system (OS) influences the energy consumption of software and algorithms running on it. To archive this, a series of tests were conducted. These include algorithms written in different programming languages and different software. In addition, the tests were performed across three different operating systems on two different computers. This not only allows the impact of different operating systems on the efficiency of programs or algorithms to be examined, but also whether this impact is the same with different hardware. The result of the conducted tests showed that some algorithms exhibited an increase in efficiency and performance of up to 50 % by simply changing the operating system.
Open Access
Python's dominance in machine learning : unraveling its emergence and exploring the trade-offs of faster alternatives
(2024) Youssef, Johnny
This research investigates the intricate relationship between library optimization and machine learning algorithm performance across Python, Java, C++, and Julia. Through comprehensive benchmarking of widely used libraries, the study reveals that library efficiency often supersedes the inherent characteristics of programming languages in determining execution speed, accuracy, and energy consumption of machine learning models. The findings challenge the conventional wisdom that compiled languages invariably outperform interpreted ones in computational tasks. Notably, Python’s well-optimized libraries, such as Scikit-learn, demonstrate competitive and sometimes superior performance compared to C++ implementations in specific scenarios. This paradigm shift underscores the critical importance of library selection over language choice in optimizing machine learning workflows. The study delves into the nuanced interplay of factors influencing machine learning performance, including execution efficiency, ecosystem richness, and implementation ease. It also examines the impact of Just-In-Time (JIT) compilation in Julia, revealing significant performance enhancements in subsequent runs, which points to its potential in long-running or repetitive tasks. By providing a comprehensive analysis of the performance landscape across different programming languages and libraries, this study offers valuable insights for practitioners and researchers. It enables informed decision-making in selecting optimal tools and languages for specific machine learning applications, considering not only computational efficiency but also broader ecosystem factors and long-term maintainability. Ultimately, this research contributes to a more nuanced understanding of the performance dynamics in machine learning implementations, challenging preconceptions and providing a data-driven foundation for optimizing machine learning workflows across diverse computational environments.