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}
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.