Arithmetic Deduction Model for High Performance Computing: A Comparative Exploration of Computational Models Paradigms
Patrick Mukala

TL;DR
This paper introduces the Arithmetic Deduction Model for high performance computing, emphasizing its use of natural arithmetic concepts to overcome limitations of existing parallel models, and compares various computational paradigms.
Contribution
It presents the Arithmetic Deduction Model as a novel approach that reduces dependencies in distributed data processing, addressing limitations of traditional parallel computing models.
Findings
Arithmetic Deduction Model shows potential for improved parallel computation.
Comparison highlights strengths and limitations of existing models like PRAM, BSP, and DataFlow.
The study underscores the need for new computational paradigms in high performance computing.
Abstract
A myriad of applications ranging from engineering and scientific simulations, image and signal processing as well as high-sensitive data retrieval demand high processing power reaching up to teraflops for their efficient execution. While a standard serial computer would require clock-cycles of less than one per second in this instance, parallel computing is a viable alternative. In adopting parallelism, multiple architectural models such as the PRAM, BSP and DataFlow Models have been proposed and implemented with some limitations due to a number of factors. Perhaps one of the predominant causes is the presence of sequential execution at some extent in these models. This status has trigged the need for improved alternatives. Hence, the Arithmetic Deduction Model has been introduced and its peculiarity can be seen through its use of natural arithmetic concepts to perform computation, and…
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Taxonomy
TopicsParallel Computing and Optimization Techniques · Distributed and Parallel Computing Systems · Quantum Computing Algorithms and Architecture
