000215922 001__ 215922
000215922 005__ 20190416220328.0
000215922 0247_ $$2doi$$a10.1145/2882903.2882916
000215922 037__ $$aCONF
000215922 245__ $$aMicro-architectural Analysis of In-memory OLTP
000215922 269__ $$a2016
000215922 260__ $$c2016
000215922 336__ $$aConference Papers
000215922 500__ $$aPUBLICATION_SHORE_MT
000215922 520__ $$aMicro-architectural behavior of traditional disk-based online transaction processing (OLTP) systems has been investigated extensively over the past couple of decades. Results show that traditional OLTP mostly under-utilize the available micro-architectural resources. In-memory OLTP systems, on the other hand, process all the data in mainmemory, and therefore, can omit the buffer pool. In addition, they usually adopt more lightweight concurrency control mechanisms, cache-conscious data structures, and cleaner codebases since they are usually designed from scratch. Hence, we expect significant differences in micro-architectural behavior when running OLTP on platforms optimized for inmemory processing as opposed to disk-based database systems. In particular, we expect that in-memory systems exploit micro architectural features such as instruction and data caches significantly better than disk-based systems. This paper sheds light on the micro-architectural behavior of in-memory database systems by analyzing and contrasting it to the behavior of disk-based systems when running OLTP workloads. The results show that despite all the design changes, in-memory OLTP exhibits very similar microarchitectural behavior to disk-based OLTP systems: more than half of the execution time goes to memory stalls where L1 instruction misses and the long-latency data misses from the last-level cache are the dominant factors in the overall stall time. Even though aggressive compilation optimizations can almost eliminate instruction misses, the reduction in instruction stalls amplifies the impact of last-level cache data misses. As a result, the number of instructions retired per cycle barely reaches one on machines that are able to retire up to four for both traditional disk-based and new generation in-memory OLTP.
000215922 6531_ $$aOLTP
000215922 6531_ $$aWorkload characterization
000215922 6531_ $$aIn-memory OLTP systems
000215922 6531_ $$aMicro-architectural analysis
000215922 700__ $$0248929$$g212284$$aSirin, Utku
000215922 700__ $$aTozun, Pinar
000215922 700__ $$0244478$$g199720$$aPorobic, Danica
000215922 700__ $$0243527$$g177957$$aAilamaki, Anastasia
000215922 7112_ $$aSIGMOD 2016
000215922 773__ $$tProceedings of the 2016 International Conference on Management of Data$$q387-402
000215922 8564_ $$uhttps://infoscience.epfl.ch/record/215922/files/sirin_sigmod2016.pdf$$zPreprint$$s3849453$$yPreprint
000215922 909C0 $$xU11836$$0252224$$pDIAS
000215922 909CO $$ooai:infoscience.tind.io:215922$$qGLOBAL_SET$$pconf$$pIC
000215922 917Z8 $$x212284
000215922 917Z8 $$x199720
000215922 937__ $$aEPFL-CONF-215922
000215922 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000215922 980__ $$aCONF