A hierarchical neural model of data prefetching
A hierarchical neural model of data prefetching
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DOI：
10.1145/3445814.3446752
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
Zhan Shi;Akanksha Jain;Kevin Swersky;Milad Hashemi;Parthasarathy Ranganathan;Calvin Lin
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文献类型：
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作者：
Zhan Shi;Akanksha Jain;Kevin Swersky;Milad Hashemi;Parthasarathy Ranganathan;Calvin Lin
本文提出了一种新型的神经元网络，用于预摘情况，与以前的神经元模型相比，我们的模型还可以学习地址相关性，这对于预摘情况而言很重要我们的解决方案是其层次结构，将地址分为页面和偏移，并引入了学习页面之间重要关系的机制和偏移。 ％分别用于理想化的多米诺骨牌和ISB预订者。利益，Voyager显着提高了准确性和覆盖范围，慢速训练和预测使神经元模型实际上在硬件中使用，但是Voyager的间接费用大大降低 - 在每个维度上，而不是先前的神经元模型。减少了15-20倍，而储存开销则减少了110-200倍。预摘要。
This paper presents Voyager, a novel neural network for data prefetching. Unlike previous neural models for prefetching, which are limited to learning delta correlations, our model can also learn address correlations, which are important for prefetching irregular sequences of memory accesses. The key to our solution is its hierarchical structure that separates addresses into pages and offsets and that introduces a mechanism for learning important relations among pages and offsets. Voyager provides significant prediction benefits over current data prefetchers. For a set of irregular programs from the SPEC 2006 and GAP benchmark suites, Voyager sees an average IPC improvement of 41.6% over a system with no prefetcher, compared with 21.7% and 28.2%, respectively, for idealized Domino and ISB prefetchers. We also find that for two commercial workloads for which current data prefetchers see very little benefit, Voyager dramatically improves both accuracy and coverage. At present, slow training and prediction preclude neural models from being practically used in hardware, but Voyager’s overheads are significantly lower—in every dimension—than those of previous neural models. For example, computation cost is reduced by 15- 20×, and storage overhead is reduced by 110-200×. Thus, Voyager represents a significant step towards a practical neural prefetcher.