末级缓存重用预测中的可变性处理
Addressing Variability in Reuse Prediction for Last-Level Caches
摘要：
末级缓存（LLC）代表了现代CPU处理器晶体管预算的大部分，对于应用程序性能至关重要，因为LLC可以快速访问数据，而不是更慢的主存储器。然而，具有大工作集大小的应用程序通常在LLC处呈现流式和/或颠簸访问模式。结果，LLC容量的大部分被将不会被再次引用的死块占用，导致LLC容量的低效利用。为了提高高速缓存效率，最先进的高速缓存管理技术采用预测机制，该预测机制从过去的访问模式中学习，目的是准确地识别尽可能多的死块。一旦被识别，死块就被从LLC中驱逐，为潜在的高重用高速缓存块腾出空间。在这篇论文中，我们确定的变化，在重用行为的缓存块的关键限制因素，在最大限度地提高缓存效率的国家的最先进的预测技术。由于LLC无法控制的许多因素，重用预测的可变性是不可避免的。可变性的来源包括控制流变化、推测性执行和来自共享高速缓存的核心的争用等。重用预测中的可变性在可靠地识别块的有用寿命的结束方面挑战现有技术，从而导致较低的预测准确度、覆盖率或两者。为了解决这个问题，本文的目的是设计强大的缓存管理机制和政策，LLC在面对重用预测的变化，以尽量减少缓存未命中，同时保持低成本和硬件实现的复杂性。为此，我们提出了两种高速缓存管理技术，一个域不可知的和一个域专用的，以提高高速缓存的效率，通过解决重用预测的变化。
英文摘要：
Last-Level Cache (LLC) represents the bulk of a modern CPU processor's transistor budget and is essential for application performance as LLC enables fast access to data in contrast to much slower main memory. However, applications with large working set size often exhibit streaming and/or thrashing access patterns at LLC. As a result, a large fraction of the LLC capacity is occupied by dead blocks that will not be referenced again, leading to inefficient utilization of the LLC capacity. To improve cache efficiency, the state-of-the-art cache management techniques employ prediction mechanisms that learn from the past access patterns with an aim to accurately identify as many dead blocks as possible. Once identified, dead blocks are evicted from LLC to make space for potentially high reuse cache blocks. In this thesis, we identify variability in the reuse behavior of cache blocks as the key limiting factor in maximizing cache efficiency for state-of-the-art predictive techniques. Variability in reuse prediction is inevitable due to numerous factors that are outside the control of LLC. The sources of variability include control-flow variation, speculative execution and contention from cores sharing the cache, among others. Variability in reuse prediction challenges existing techniques in reliably identifying the end of a block's useful lifetime, thus causing lower prediction accuracy, coverage, or both. To address this challenge, this thesis aims to design robust cache management mechanisms and policies for LLC in the face of variability in reuse prediction to minimize cache misses, while keeping the cost and complexity of the hardware implementation low. To that end, we propose two cache management techniques, one domain-agnostic and one domain-specialized, to improve cache efficiency by addressing variability in reuse prediction.
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