An application of complexity measures in addressing modes for CISC- and RISC-architectures

El-Aawar, Haissam

doi:10.1109/icit.2008.4608682

Cited by 3 publications

(1 citation statement)

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“…Big O notation used for measuring code performance in terms of growth function of an algorithm's frequency count of its basic operation [12][13] [14]. The proposed method for computation can be extended to also express time complexities in terms of big O notation.…”

Section: Analyzing Time Complexitiesmentioning

confidence: 99%

Static Profiling of Assembly Code Performance and Optimization Effectiveness using Instructions Performed and Program Latency

2019

IJRTE

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Software program optimization for improved execution speed can be achieved through modifying the program. Programs are usually written in high level languages then translated into low level assembly language. More coverage of optimization and performance analysis can be performed on low level than high level language. Optimization improvement is measured in the difference in program execution performance. Several methods are available for measuring program performance are classified into static approaches and dynamic approaches. This paper presents an alternative method of more accurately measuring code performance statically than commonly used code analysis metrics. New metrics proposed are designed to expose effectiveness of optimization performed on code, specifically unroll optimizations. An optimization method, loop unroll is used to demonstrate the effectiveness of the increased accuracy of the proposed metric. The results of the study show that measuring Instructions Performed and Instruction Latency is a more accurate static metric than Instruction Count and subsequently those based on it.

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Section: Analyzing Time Complexitiesmentioning

confidence: 99%

Static Profiling of Assembly Code Performance and Optimization Effectiveness using Instructions Performed and Program Latency

2019

IJRTE

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Optically reconfigurable gate array platform for mono-instruction set computer architecture

Shimba

Watanabe

2017

2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC)

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Changing Trends in Computer Architecture : A Comprehensive Analysis of ARM and x86 Processors

Gupta

Sharma

2021

IJSRCSEIT

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In the modern world, we use microprocessors which are either based on ARM or x86 architecture which are the most common processor architectures. ARM originally stood for ‘Acorn RISC Machines’ but over the years changed to ‘Advanced RISC Machines’. It was started as just an experiment but showed promising results and now it is omnipresent in our modern devices. Unlike x86 which is designed for high performance, ARM focuses on low power consumption with considerable performance. Because of the advancements in the ARM technology, they are becoming more powerful than their x86 counterparts. In this analysis we will collate the two architectures briefly and conclude which microprocessor will dominate the microprocessor industry. The processor which will perform better in different tests will be more suitable for the reader to use in their application. The shift in the industry towards ARM processors can change how we write softwares which in turn will affect the whole software development environment.

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An application of complexity measures in addressing modes for CISC- and RISC-architectures

Cited by 3 publications

References 1 publication

Static Profiling of Assembly Code Performance and Optimization Effectiveness using Instructions Performed and Program Latency

Static Profiling of Assembly Code Performance and Optimization Effectiveness using Instructions Performed and Program Latency

Optically reconfigurable gate array platform for mono-instruction set computer architecture

Changing Trends in Computer Architecture : A Comprehensive Analysis of ARM and x86 Processors

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