What Every Computer Scientist Needs To Know About Parallelization
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910904081186816 |
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| author | Adefemi, Temitayo |
| author_facet | Adefemi, Temitayo |
| contents | Parallelization has become a cornerstone of modern computing, influencing everything from high performance supercomputers to everyday mobile devices. This paper presents a comprehensive guide on the fundamentals of parallelization that every computer scientist should know, beginning with a historical perspective that traces the evolution from early theoretical models such as PRAM and BSP to today's advanced multicore and heterogeneous architectures. We explore essential theoretical frameworks, practical paradigms, and synchronization mechanisms while discussing implementation strategies using processes, threads, and modern models like the Actor framework. Additionally, we examine how hardware components including CPUs, caches, memory, and accelerators interact with software to impact performance, scalability, and load balancing. This work demystifies parallel programming by integrating historical context, theoretical underpinnings, and practical case studies. It equips readers with the tools to design, optimize, and troubleshoot parallel applications in an increasingly concurrent computing landscape. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_03647 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | What Every Computer Scientist Needs To Know About Parallelization Adefemi, Temitayo Distributed, Parallel, and Cluster Computing Parallelization has become a cornerstone of modern computing, influencing everything from high performance supercomputers to everyday mobile devices. This paper presents a comprehensive guide on the fundamentals of parallelization that every computer scientist should know, beginning with a historical perspective that traces the evolution from early theoretical models such as PRAM and BSP to today's advanced multicore and heterogeneous architectures. We explore essential theoretical frameworks, practical paradigms, and synchronization mechanisms while discussing implementation strategies using processes, threads, and modern models like the Actor framework. Additionally, we examine how hardware components including CPUs, caches, memory, and accelerators interact with software to impact performance, scalability, and load balancing. This work demystifies parallel programming by integrating historical context, theoretical underpinnings, and practical case studies. It equips readers with the tools to design, optimize, and troubleshoot parallel applications in an increasingly concurrent computing landscape. |
| title | What Every Computer Scientist Needs To Know About Parallelization |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2504.03647 |