LLC Dead Block Prediction Considered Not Useful
LLC Dead Block Prediction Considered Not Useful
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发表时间：
2016-06
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通讯作者：
P. Faldu;Boris Grot
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作者：
P. Faldu;Boris Grot
死块预测器(DBP)通过识别已在缓存中耗尽其有用寿命的块，并按优先顺序逐出这些块，而不管这些块经历了多少重复使用，从而提高缓存效率。在这样做时，DBP超越了仅针对那些没有缓存重用的块的插入策略。但是，与插入策略相比，DBP增加了多少价值？这项工作研究了DBP在有限责任公司的机会，并得出结论：与最先进的插入策略相比，DBP的附加值很低。我们的主要结果是，有限责任公司的驱逐主要是由没有重复使用的块主导的。即使使用最优替换策略，通过对未来的完美了解来最大限度地提高缓存重用，平均78%的LLC逐出也没有遇到任何命中。对于剩余的经历LLC重复使用的驱逐，最先进的DBP方案只预测了一小部分，并且这些预测的准确性很低。我们的一阶限制研究表明，DBP超过最佳插入政策的可能覆盖上限仅占所有有限责任公司驱逐的6.9%。基于这些发现，我们认为DBP的更高复杂性是不合理的。
Dead block predictors (DBPs) improve cache efficiency by identifying blocks that have exhausted their useful lifetime in the cache and prioritizing them for eviction regardless of how much reuse these blocks have experienced. In doing so, DBPs go beyond insertion policies that target only those blocks that have no cache reuse. But how much value do DBPs add over insertion policies? This work examines the opportunity for DBP at the LLC and concludes that its value-add is low over a state-of-the-art insertion policy. Our key result is that LLC evictions are dominated by blocks having no reuse. Even with the optimal replacement policy that maximizes cache reuse through perfect knowledge of the future, an average of 78% of LLC evictions have not experienced any hits. For the remaining evictions that experience LLC reuse, only a fraction is predicted by a state-of-the-art DBP scheme, and the accuracy of these predictions is low. Our first-order limit study shows that a likely coverage ceiling for DBP over the best performing insertion policy is just 6.9% of all LLC evictions. Based on these findings, we argue that the higher complexity of DBP is not justified.