论文标题
Autonuma内存分层的性能表征
Performance Characterization of AutoNUMA Memory Tiering on Graph Analytics
论文作者
论文摘要
与DRAM相比，非挥发记忆（NVM）可以提供更高的密度和更低的每位成本。它的主要缺点是它比DRAM慢。另一方面，由于其成本和能耗，DRAM具有可伸缩性问题。 NVM可能会与计算机系统中的DRAM共存，最大的挑战是知道在每种内存中分配哪些数据。 Linux内核中的Autonuma是一种最先进的方法。先前的工作仅限于仅根据申请执行时间来衡量Autonuma，而无需了解Autonuma的行为。在这项工作中，我们提供了对Autonuma的更深入的表征，例如，确定在何处分配了一组页面，同时跟踪Autonuma执行的促销和降级决策。我们的分析表明，在运行图形处理应用程序或图形分析时，Autonuma的好处可能是适中的，因为大多数页面在整个执行时间内只有一个访问权限，而其他页面访问却没有时间范围。我们为使用DRAM和NVM之间的对象级映射探索应用程序特征的理由。我们的初步实验表明，与Autonuma相比，对象级内存分层可以更好地捕获应用程序行为，并将图形分析的执行时间减少21％（AVG）和51％（MAX）（最大值），同时显着减少了NVM中内存访问的数量。
Non-Volatile Memory (NVM) can deliver higher density and lower cost per bit when compared with DRAM. Its main drawback is that it is slower than DRAM. On the other hand, DRAM has scalability problems due to its cost and energy consumption. NVM will likely coexist with DRAM in computer systems and the biggest challenge is to know which data to allocate on each type of memory. A state-of-the-art approach is AutoNUMA, in the Linux kernel. Prior work is limited to measuring AutoNUMA solely in terms of the application execution time, without understanding AutoNUMA's behavior. In this work we provide a more in-depth characterization of AutoNUMA, for instance, identifying where exactly a set of pages are allocated, while keeping track of promotion and demotion decisions performed by AutoNUMA. Our analysis shows that AutoNUMA's benefits can be modest when running graph processing applications, or graph analytics, because most pages have only one access over the entire execution time and other pages accesses have no temporal locality. We make a case for exploring application characteristics using object-level mappings between DRAM and NVM. Our preliminary experiments show that an object-level memory tiering can better capture the application behavior and reduce the execution time of graph analytics by 21% (avg) and 51% (max), when compared to AutoNUMA, while significantly reducing the number of memory accesses in NVM.
