Agile TLB Prefetching and Prediction Replacement Policy

TL;DR

This paper introduces an integrated approach combining Agile TLB prefetching, dynamic replacement policies, and control flow analysis to significantly improve TLB performance in virtual memory systems.

Contribution

It proposes a novel Agile TLB Prefetcher and a control flow-based replacement policy, CHiRP, tailored for L2 TLBs, to enhance address translation efficiency.

Findings

CHiRP outperforms traditional policies in TLB hit rate.

Integration of prefetching and predictive replacement reduces page walk latency.

Dynamic identification of essential PTEs improves overall system performance.

Abstract

Virtual-to-physical address translation is a critical performance bottleneck in paging-based virtual memory systems. The Translation Lookaside Buffer (TLB) accelerates address translation by caching frequently accessed mappings, but TLB misses lead to costly page walks. Hardware and software techniques address this challenge. Hardware approaches enhance TLB reach through system-level support, while software optimizations include TLB prefetching, replacement policies, superpages, and page size adjustments. Prefetching Page Table Entries (PTEs) for future accesses reduces bottlenecks but may incur overhead from incorrect predictions. Integrating an Agile TLB Prefetcher (ATP) with SBFP optimizes performance by leveraging page table locality and dynamically identifying essential free PTEs during page walks. Predictive replacement policies further improve TLB performance. Traditional LRU replacement is limited to near-instant references, while advanced policies like SRRIP, GHRP, SHiP, SDBP, and CHiRP enhance performance by targeting specific inefficiencies. CHiRP, tailored for L2 TLBs, surpasses other policies by leveraging control flow history to detect dead blocks, utilizing L2 TLB entries for learning instead of sampling. These integrated techniques collectively address key challenges in virtual memory management.