AntTracks TrendViz:随时间配置堆内存可视化

Markus Weninger, Lukas Makor, Elias Gander, H. Mössenböck
{"title":"AntTracks TrendViz:随时间配置堆内存可视化","authors":"Markus Weninger, Lukas Makor, Elias Gander, H. Mössenböck","doi":"10.1145/3302541.3313100","DOIUrl":null,"url":null,"abstract":"The complexity of modern applications makes it hard to fix memory leaks and other heap-related problems without tool support. Yet, most state-of-the-art tools share problems that still need to be tackled: (1) They group heap objects only based on their types, ignoring other properties such as allocation sites or data structure compositions. (2) Analyses strongly focus on a single point in time and do not show heap evolution over time. (3) Results are displayed in tables, even though more advanced visualization techniques may ease and improve the analysis. In this paper, we present a novel visualization approach that addresses these shortcomings. Heap objects can be arbitrarily classified, enabling users to group objects based on their needs. Instead of inspecting the size of those object groups at a single point in time, our approach tracks the growth of each object group over time. This growth is then visualized using time-series charts, making it easy to identify suspicious object groups. A drill-down feature enables users to investigate these object groups in more detail. Our approach has been integrated into AntTracks, a trace-based memory monitoring tool, to demonstrate its feasibility.","PeriodicalId":231712,"journal":{"name":"Companion of the 2019 ACM/SPEC International Conference on Performance Engineering","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"AntTracks TrendViz: Configurable Heap Memory Visualization Over Time\",\"authors\":\"Markus Weninger, Lukas Makor, Elias Gander, H. Mössenböck\",\"doi\":\"10.1145/3302541.3313100\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The complexity of modern applications makes it hard to fix memory leaks and other heap-related problems without tool support. Yet, most state-of-the-art tools share problems that still need to be tackled: (1) They group heap objects only based on their types, ignoring other properties such as allocation sites or data structure compositions. (2) Analyses strongly focus on a single point in time and do not show heap evolution over time. (3) Results are displayed in tables, even though more advanced visualization techniques may ease and improve the analysis. In this paper, we present a novel visualization approach that addresses these shortcomings. Heap objects can be arbitrarily classified, enabling users to group objects based on their needs. Instead of inspecting the size of those object groups at a single point in time, our approach tracks the growth of each object group over time. This growth is then visualized using time-series charts, making it easy to identify suspicious object groups. A drill-down feature enables users to investigate these object groups in more detail. Our approach has been integrated into AntTracks, a trace-based memory monitoring tool, to demonstrate its feasibility.\",\"PeriodicalId\":231712,\"journal\":{\"name\":\"Companion of the 2019 ACM/SPEC International Conference on Performance Engineering\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-03-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Companion of the 2019 ACM/SPEC International Conference on Performance Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3302541.3313100\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Companion of the 2019 ACM/SPEC International Conference on Performance Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3302541.3313100","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

摘要

现代应用程序的复杂性使得在没有工具支持的情况下很难修复内存泄漏和其他与堆相关的问题。然而,大多数最先进的工具都有一些仍然需要解决的问题:(1)它们只根据堆对象的类型对堆对象进行分组,而忽略了其他属性,如分配站点或数据结构组合。(2)分析强烈关注单个时间点,而不显示堆随时间的演变。(3)结果显示在表格中,即使更先进的可视化技术可以简化和改进分析。在本文中,我们提出了一种新的可视化方法来解决这些缺点。可以对堆对象进行任意分类,使用户能够根据自己的需要对对象进行分组。我们的方法不是在单个时间点检查这些对象组的大小,而是跟踪每个对象组随时间的增长。然后使用时间序列图表将这种增长可视化,从而很容易识别可疑的对象组。向下钻取功能使用户能够更详细地研究这些对象组。我们的方法已经集成到AntTracks,一个基于跟踪的内存监控工具,以证明其可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AntTracks TrendViz: Configurable Heap Memory Visualization Over Time
The complexity of modern applications makes it hard to fix memory leaks and other heap-related problems without tool support. Yet, most state-of-the-art tools share problems that still need to be tackled: (1) They group heap objects only based on their types, ignoring other properties such as allocation sites or data structure compositions. (2) Analyses strongly focus on a single point in time and do not show heap evolution over time. (3) Results are displayed in tables, even though more advanced visualization techniques may ease and improve the analysis. In this paper, we present a novel visualization approach that addresses these shortcomings. Heap objects can be arbitrarily classified, enabling users to group objects based on their needs. Instead of inspecting the size of those object groups at a single point in time, our approach tracks the growth of each object group over time. This growth is then visualized using time-series charts, making it easy to identify suspicious object groups. A drill-down feature enables users to investigate these object groups in more detail. Our approach has been integrated into AntTracks, a trace-based memory monitoring tool, to demonstrate its feasibility.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信