PTAT: An efficient and precise tool for collecting detailed TLB miss traces

Jiutian Zhang, Yuhang Liu, Xiaojing Zhu, Yuan Ruan, Mingyu Chen
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Abstract

It is well known that the TLB performance impacts the memory system performance, which is critical for overall system performance. Similar to multi-level caches, multilevel TLBs have become an important leverage for boosting data access performance. Applications have increasingly large working sets. Servers targeting such applications have thus been built with ever larger main memory capacities, but there has been no commensurate growth in TLB sizes. Designing high performance and energy efficient memory hierarchies require insight into the behavior of current designs: when do they work well, and when do they fall short of expectations. Profiling the TLB misses is the prerequisite for memory system optimization. Both designing efficient TLB architecture and TLB-friendly applications require analysis of TLB miss behavior. Although researchers have extensively studied TLB behavior, current approaches have some issues in either efficiency or precision.
PTAT:一个有效和精确的工具,用于收集详细的TLB遗漏痕迹
众所周知,TLB性能会影响内存系统的性能,而内存系统的性能对整个系统的性能至关重要。与多级缓存类似,多级tlb已成为提高数据访问性能的重要手段。应用程序的工作集越来越大。因此,针对这些应用程序的服务器被构建为具有更大的主内存容量,但TLB大小没有相应的增长。设计高性能和节能的内存层次结构需要洞察当前设计的行为:它们什么时候工作得很好,什么时候没有达到预期。分析TLB缺失是内存系统优化的先决条件。无论是设计高效的TLB架构,还是设计对TLB友好的应用,都需要对TLB缺失行为进行分析。虽然研究人员对TLB行为进行了广泛的研究,但目前的方法在效率和精度上都存在一些问题。
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