Scratchpad Memory Management Random Sampling Algorithm for Multi-core Processor

Authors

  • Kavita Tabbassum Information Technology Center, Sindh Agricultural University Tandojam, 70060, Pakistan
  • Shahnawaz Farhan Khahro Department of Information Technology Center, Sindh Agricultural University Tandojam, 70060, Pakistan
  • Saima Shaikh Department of Information Technology Center, Sindh Agricultural University Tandojam, 70060, Pakistan
  • Farah Naveen Issani Department of Information Technology Center, Sindh Agricultural University Tandojam, 70060, Pakistan.
  • Suhni Abbasi Department of Information Technology Center, Sindh Agricultural University Tandojam, 70060, Pakistan
  • Hina Chandio Department of Information Technology Center, Sindh Agricultural University Tandojam, 70060, Pakistan

DOI:

https://doi.org/10.62019/abbdm.v4i1.112

Abstract

Traditional compiler-based SPM management often fails to accurately predict the memory access characteristics of system scheduling and task switching in a Multi-core Processor environment, thus affecting the effect of SPM management. The use of runtime dynamic detection can make up for this flaw and provide an accurate and efficient dynamic management method. This research focuses on the analysis of the similarities and differences between SPM management in Multi-core Processor environment and single-task environment, and builds a real-time operating system (RTOS) supporting multi-task scheduling according to experimental requirements, which is necessary for the random sampling of SPM allocation algorithm and improvements to meet the needs of adaptive SPM allocation for program runtime in a Multi-core Processor environment. The performance of the random sampling algorithm in Multi-core Processor environment is analyzed which proves the effectiveness of the allocation algorithm for Multi-core Processor environment.

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Published

2024-03-01

How to Cite

Scratchpad Memory Management Random Sampling Algorithm for Multi-core Processor. (2024). The Asian Bulletin of Big Data Management , 4(1), Data Science 4(1), 139-155. https://doi.org/10.62019/abbdm.v4i1.112