PS-TLB: Leveraging page classification information for fast, scalable and efficient translation for future CMPs

  • Authors:
  • Yong Li;Rami Melhem;Alex K. Jones

  • Affiliations:
  • University of Pittsburgh, Pittsburgh, PA;University of Pittsburgh, Pittsburgh, PA;University of Pittsburgh, Pittsburgh, PA

  • Venue:
  • ACM Transactions on Architecture and Code Optimization (TACO) - Special Issue on High-Performance Embedded Architectures and Compilers
  • Year:
  • 2013

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Abstract

Traversing the page table during virtual to physical address translation causes pipeline stalls when misses occur in the translation-lookaside buffer (TLB). State-of-the-art translation proposals typically optimize a single aspect of translation performance (e.g., translation sharing, context switch performance, etc.) with potential trade-offs of additional hardware complexity, increased translation latency, or reduced scalability. In this article, we propose the partial sharing TLB (PS-TLB), a fast and scalable solution that reduces off-chip translation misses without sacrificing the timing-critical requirement of on-chip translation. We introduce the partial sharing buffer (PSB) which leverages application page sharing characteristics using minimal additional hardware resources. Compared to the leading TLB proposal that leverages sharing, PS-TLB provides a more than 45% improvement in translation latency with a 9% application speedup while using fewer storage resources. In addition, the page classification and PS-TLB architecture provide further optimizations including an over 30% reduction of interprocessor interrupts for coherence, and reduced context switch misses with fewer resources compared with existing methods.