PDL ABSTRACT

Improving Cache Performance by Exploiting Read-Write Disparity

Proceedings of the 20th International Symposium on High-Performance Computer Architecture (HPCA), Orlando, FL, February 2014.

Samira Khan†*, Alaa Alameldeen*, Chris Wilkerson*, Onur Mutlu†, Daniel Jimenez^

†Carnegie Mellon University
*Intel Labs
^Texas A&M University

http:\\www..pdl.cmu.edu

Cache read misses stall the processor if there are no independent instructions to execute. In contrast, most cache write misses are off the critical path of execution, since writes can be buffered in the cache or the store buffer. With few exceptions, cache lines that serve loads are more critical for performance than cache lines that serve only stores. Unfortunately, traditional cache management mechanisms do not take into account this disparity between read-write criticality. The key contribution of this paper is the new idea of distinguishing between lines that are reused by reads versus those that are reused only by writes to focus cache management policies on the more critical read lines. We propose a Read-Write Partitioning (RWP) policy that minimizes read misses by dynamically partitioning the cache into clean and dirty partitions, where partitions grow in size if they are more likely to receive future read requests. We show that exploiting the differences in read-write criticality provides better performance over prior cache management mechanisms. For a single-core system, RWP provides 5% average speedup across the entire SPEC CPU2006 suite, and 14% average speedup for cachesensitive benchmarks, over the baseline LRU replacement policy. We also show that RWP can perform within 3% of a new yet complex instruction-address-based technique, Read Reference Predictor (RRP), that bypasses cache lines which are unlikely to receive any read requests, while requiring only 5:4% of RRP's state overhead. On a 4-core system, our RWP mechanism improves system throughput by 6% over the baseline and outperforms three other state-of-the-art mechanisms we evaluate.

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