Stefan Noll, J. Teubner, Norman May, Alexander Böhm
{"title":"Analyzing memory accesses with modern processors","authors":"Stefan Noll, J. Teubner, Norman May, Alexander Böhm","doi":"10.1145/3399666.3399896","DOIUrl":null,"url":null,"abstract":"Debugging and tuning database systems is very challenging. Using common profiling tools is often not sufficient because they identify the machine instruction rather than the instance of a data structure that causes a performance problem. This leaves a problem's root cause such as memory hotspots or poor data layouts hidden. The state-of-the-art solution is to augment classical profiling with a memory trace. However, current approaches for collecting memory traces are not usable in practice due to their large runtime overhead. In this work, we leverage a mechanism available in modern processors to collect memory traces via hardware-based sampling. We evaluate our approach using a commercial and an open-source database system running the JCC-H benchmark. In particular, we demonstrate that our approach is practical due to its low runtime overhead and we illustrate how memory traces uncover new insights into the memory access characteristics of database systems.","PeriodicalId":256784,"journal":{"name":"Proceedings of the 16th International Workshop on Data Management on New Hardware","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 16th International Workshop on Data Management on New Hardware","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3399666.3399896","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Debugging and tuning database systems is very challenging. Using common profiling tools is often not sufficient because they identify the machine instruction rather than the instance of a data structure that causes a performance problem. This leaves a problem's root cause such as memory hotspots or poor data layouts hidden. The state-of-the-art solution is to augment classical profiling with a memory trace. However, current approaches for collecting memory traces are not usable in practice due to their large runtime overhead. In this work, we leverage a mechanism available in modern processors to collect memory traces via hardware-based sampling. We evaluate our approach using a commercial and an open-source database system running the JCC-H benchmark. In particular, we demonstrate that our approach is practical due to its low runtime overhead and we illustrate how memory traces uncover new insights into the memory access characteristics of database systems.