The low capacity of main memory has become a critical issue in the performance of systems. Several memory schemes, utilizing multiple classes of memory devices, are used to mitigate the problem; hiding the small capacity by placing data in proper memory devices based on the hotness of the data. Memory tracers can provide such hotness information, but existing tracing tools incur extremely high overhead and the overhead increases as the problem size of a workload grows. In this paper, we propose Daptrace built for tracing memory access with bounded and light overhead. The two main techniques, region-based sampling and adaptive region construction, are utilized to maintain a low overhead regardless of the program size. For evaluation, we trace a wide range of 20 workloads and compared with baseline. The results show that Daptrace has a very small amount of runtime overhead and storage space overhead (1.95% and 5.38 MB on average) while maintaining the tracing quality regardless of the working set size of a workload. Also, a case study on out-of-core memory management exhibits a high potential of Daptrace for optimal data management. From the evaluation results, we can conclude that Daptrace shows great performance on identifying hot memory objects.

主存储器的低容量已成为系统性能中的关键问题。利用多种类型的存储设备的几种存储方案可以缓解该问题。通过根据数据的热度将数据放置在适当的存储设备中来隐藏小容量存储。内存跟踪器可以提供此类热度信息，但是现有的跟踪工具会产生极高的开销，并且开销随着工作负载的问题大小的增长而增加。在本文中，我们提出了Daptrace，该Daptrace是为跟踪内存访问而建立的，开销有限且开销很小。无论程序大小如何，都使用两种主要技术（基于区域的采样和自适应区域构造）来维持较低的开销。为了进行评估，我们跟踪了20种工作负载，并与基线进行了比较。结果表明，Daptrace的运行时开销和存储空间开销非常小（平均为1.95％和5.38 MB），而无论工作集的工作集大小如何，都可以保持跟踪质量。此外，有关内核外内存管理的案例研究显示了Daptrace具有实现最佳数据管理的巨大潜力。根据评估结果，我们可以得出结论，Daptrace在识别热内存对象方面显示出了出色的性能。