Pangloss: a novel Markov chain prefetcher
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We present Pangloss, an efficient high-performance data prefetcher that approximates a Markov chain on delta transitions. With a limited information scope and space/logic complexity, it is able to reconstruct a variety of both simple and complex access patterns. This is achieved by a highly-efficient representation of the Markov chain to provide accurate values for transition probabilities. In addition, we have added a mechanism to reconstruct delta transitions originally obfuscated by the out-of-order execution or page transitions, such as when streaming data from multiple sources. Our single-level (L2) prefetcher achieves a geometric speedup of 1.7 selected state-of-the-art baselines (KPCP and BOP). When combined with an equivalent for the L1 cache (L1 L2), the speedups rise to 6.8 40.4 considerable performance improvement as well.
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