32-bit computing

32-bit computing

32-bit computing refers to a type of computer architecture where the integer size or memory address width is four octets, allowing for the processing and storage of data in 32-bit chunks. This architecture is characterized by a 32-bit address bus space, which enables the computer to access up to 4GB of memory. Multibus II is an example of a system that has been extended to support 32-bit computing devices, providing greater processing power and memory capacity than earlier systems that only supported 16-bit data and 24-bit address bus space.From: Quantum Mechanics [2022], Communications Technology Handbook [2013]

A Review on Live Memory Acquisition Approaches for Digital Forensics

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Published in Mukesh Kumar Awasthi, Ravi Tomar, Maanak Gupta, Mathematical Modeling for Intelligent Systems, 2023

Sarishma Dangi, Diksha Bisht

The Intel IA-32 architecture, which supports 32-bit computing, is referred to as x86 architecture. It defines Intel’s 32-bit processors instruction set and programming environment. Byte addressing is used in this architecture, and software running on this processor may have a linear address space of up to 4GB and a physical address space of up to 4GB. Virtual memory, paging, privilege levels, and segmentation are all supported by the IA-32’s operational protected mode.

Computationally efficient GPU based NS solver for two dimensional high-speed inviscid and viscous compressible flows

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Published in Engineering Applications of Computational Fluid Mechanics, 2023

Muhammad Naveed Akhtar, Kamran Rasheed Qureshi, Muhammad Hanif Durad, Anila Usman, Syed Muhammad Mohsin, Band Shahab, Amirhosein Mosavi

In this study, multiple 2D arrays are grouped to be referenced by a single 3D surface pointer and indexed accordingly. This technique is explained in Figures 1 and 2. All 2D arrays needed for the problem are packed into a single 3D array and a surface pointer references this common block. The size of the 3D array is given by X×Y×N, where X and Y are the grid dimensions in a problem and N is the number of 2D arrays packed in the collection. CUDA Surfaces use a 32-bit computing architecture by default. To make it suitable for double-precision computations, 2 consecutive 32-bit memory locations have been grouped to preserve the efficiency of surface pointers while taking advantage/accuracy of 64-bit computations.

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A Review on Live Memory Acquisition Approaches for Digital Forensics

Computationally efficient GPU based NS solver for two dimensional high-speed inviscid and viscous compressible flows