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Predictive Cache Coherence: Current Techniques and Future Trends

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Author Affiliations

  • 1S.o.S. in Computer Science & IT, Pt. Ravishankar Shukla University, Raipur, Chhattisgarh, India
  • 2S.o.S. in Computer Science & IT, Pt. Ravishankar Shukla University, Raipur, Chhattisgarh, India
  • 3Govt. J. Yoganandam Chhattisgarh College, Raipur, Chhattisgarh, India

Res. J. Computer & IT Sci., Volume 14, Issue (1), Pages 11-17, June,20 (2026)

Abstract

The rapid evolution of multicore, manycore, and heterogeneous processor architectures has intensified the challenges of maintaining efficient cache coherence. Traditional reactive protocols, such as MESI and MOESI, struggle to scale due to increased latency, interconnect congestion, and energy overhead. Predictive Cache Coherence (PCC) has emerged as a promising paradigm that anticipates memory access patterns and proactively manages cache state transitions, thereby reducing coherence miss penalties and optimizing system performance. This paper provides a comprehensive review of PCC techniques, including history based, heuristic driven, machine learning based, hybrid, and speculative approaches. We analyse their methodologies, performance trade-offs, hardware overhead, and scalability across diverse architectures, highlighting the comparative advantages and limitations of each strategy. Furthermore, we identify key research gaps, including prediction accuracy under dynamic workloads, hardware constraints, standardized evaluation frameworks, security considerations, and challenges in integrating PCC into emerging system architectures such as chiplets and disaggregated memory. Finally, we outline future research directions aimed at developing lightweight, adaptive, and secure predictive coherence mechanisms that can sustain high performance and energy efficiency in next generation computing systems.

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