This paper evaluates how concurrent users influence on processors and caches behavior of DBMSs on todayȁ9;s hardware platform. AS TPC-C is currently the only active OLTP benchmark supported by the TPC. the main properties of TPC-C are that the database size and the number of users scale linearly with the throughput. Because main memory capability is limited and I/O is not a bottleneck, TPC-C benchmark is unsuitable for MMDBMSs. According to those properties, this paper proposes the ETPC-C benchmark for MMDBMSs. Then using hardware counters, we evaluate ETPC-C workload on MMDBMS. We find that the miss stall time is mostly spent on on-CPU-chip caches, that is, the first and second level cache misses are dominant. Furthermore, we find instruction cache (I-cache) stall time of on-CPU-chip is a major component to the memory stall time. The smaller the emulated users, the more proportion the I-cache stall time of on-CPU-chip contributes to the memory stall time. However, if employing index, the system under test (SUT) has more total I-cache stall time than the SUT without index at the same number of emulated users and data population.
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机译:本文评估了并发用户如何影响当今的硬件平台上的DBMS的处理器和缓存行为。 AS TPC-C是当前TPC支持的唯一活动OLTP基准。 TPC-C的主要特性是数据库大小和用户数量随吞吐量线性增长。由于主内存功能有限且I / O并非瓶颈,因此TPC-C基准测试不适用于MMDBMS。根据这些特性,本文提出了MMDBMS的ETPC-C基准。然后使用硬件计数器,我们评估MMDBMS上的ETPC-C工作负载。我们发现未命中停顿时间主要花费在CPU芯片高速缓存上,即第一级和第二级高速缓存未命中占主导地位。此外,我们发现CPU芯片上的指令缓存(I-cache)停顿时间是内存停顿时间的主要组成部分。仿真用户越小,CPU上芯片的I缓存停顿时间占内存停顿时间的比例就越大。但是,如果使用索引,则在相同数量的模拟用户和数据填充下,被测系统(SUT)的总I缓存停顿时间要比没有索引的SUT更长。
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