{"title":"Detailed heap profiling","authors":"Stuart Byma, J. Larus","doi":"10.1145/3210563.3210564","DOIUrl":null,"url":null,"abstract":"Modern software systems heavily use the memory heap. As systems grow more complex and compute with increasing amounts of data, it can be difficult for developers to understand how their programs actually use the bytes that they allocate on the heap and whether improvements are possible. To answer this question of heap usage efficiency, we have built a new, detailed heap profiler called Memoro. Memoro uses a combination of static instrumentation, subroutine interception, and runtime data collection to build a clear picture of exactly when and where a program performs heap allocation, and crucially how it actually uses that memory. Memoro also introduces a new visualization application that can distill collected data into scores and visual cues that allow developers to quickly pinpoint and eliminate inefficient heap usage in their software. Our evaluation and experience with several applications demonstrates that Memoro can reduce heap usage and produce runtime improvements of 10%.","PeriodicalId":420262,"journal":{"name":"Proceedings of the 2018 ACM SIGPLAN International Symposium on Memory Management","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2018 ACM SIGPLAN International Symposium on Memory Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3210563.3210564","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

Modern software systems heavily use the memory heap. As systems grow more complex and compute with increasing amounts of data, it can be difficult for developers to understand how their programs actually use the bytes that they allocate on the heap and whether improvements are possible. To answer this question of heap usage efficiency, we have built a new, detailed heap profiler called Memoro. Memoro uses a combination of static instrumentation, subroutine interception, and runtime data collection to build a clear picture of exactly when and where a program performs heap allocation, and crucially how it actually uses that memory. Memoro also introduces a new visualization application that can distill collected data into scores and visual cues that allow developers to quickly pinpoint and eliminate inefficient heap usage in their software. Our evaluation and experience with several applications demonstrates that Memoro can reduce heap usage and produce runtime improvements of 10%.
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详细的堆分析
现代软件系统大量使用内存堆。随着系统变得越来越复杂,并且需要越来越多的数据进行计算,开发人员可能很难理解他们的程序实际上是如何使用它们在堆上分配的字节的,以及是否有可能进行改进。为了回答堆使用效率的问题,我们构建了一个新的、详细的堆分析器,名为Memoro。Memoro使用静态插装、子例程拦截和运行时数据收集的组合来构建一个清晰的图像,准确地了解程序在何时何地执行堆分配,以及它实际如何使用该内存。Memoro还引入了一个新的可视化应用程序,它可以将收集到的数据提取为分数和可视化提示,从而允许开发人员快速查明并消除软件中低效的堆使用情况。我们对几个应用程序的评估和经验表明,Memoro可以减少堆使用,并使运行时性能提高10%。
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FRC: a high-performance concurrent parallel deferred reference counter for C++ mPart: miss-ratio curve guided partitioning in key-value stores Detailed heap profiling OMR: out-of-core MapReduce for large data sets Hardware-software co-optimization of memory management in dynamic languages
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