Out-of-Order Superscalar Execution: Resolving Data Hazards in MIPS Architecture

ut-of-order superscalar execution is a critical advancement in modern processor architectures, enhancing instruction-level parallelism (ILP) and mitigating performance bottlenecks. This paper explores the implementation of super- scalar execution in MIPS architecture, focusing on resolving data hazards such as Read-After-Write (RAW), Write-After- Read (WAR), and Write-After-Write (WAW). Techniques like Tomasulo’s algorithm, register renaming, reservation stations, and branch prediction are employed to optimize execution flow. A Python-based simulation demonstrates improved throughput, reduced stalls, and efficient CPU resource utilization. The results highlight the impact of dynamic scheduling and speculative execution in maximizing computational efficiency, contributing to the evolution of high-performance processor designs

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Publication Details

Journal
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Published
2025-03-04
DOI
https://doi.org/10.55041/ijsrem42066
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

Out-of-Order Superscalar Execution: Resolving Data Hazards in MIPS Architecture

Sreenivasa Murthy B P
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Parallel Computing and Optimization Techniques
article

Out-of-Order Superscalar Execution: Resolving Data Hazards in MIPS Architecture

Sreenivasa Murthy B P
article en

Abstract

ut-of-order superscalar execution is a critical advancement in modern processor architectures, enhancing instruction-level parallelism (ILP) and mitigating performance bottlenecks. This paper explores the implementation of super- scalar execution in MIPS architecture, focusing on resolving data hazards such as Read-After-Write (RAW), Write-After- Read (WAR), and Write-After-Write (WAW). Techniques like Tomasulo’s algorithm, register renaming, reservation stations, and branch prediction are employed to optimize execution flow. A Python-based simulation demonstrates improved throughput, reduced stalls, and efficient CPU resource utilization. The results highlight the impact of dynamic scheduling and speculative execution in maximizing computational efficiency, contributing to the evolution of high-performance processor designs

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTVol. 09(03)
Openalex Percentile: Top 97%
Parallel Computing and Optimization Techniques
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