Andreas Sewe, Dingwen Yuan, Jan Sinschek, M. Mezini
{"title":"基于headroom的假装:动态地假装存在“足够长”的对象","authors":"Andreas Sewe, Dingwen Yuan, Jan Sinschek, M. Mezini","doi":"10.1145/1852761.1852767","DOIUrl":null,"url":null,"abstract":"Many modern garbage collectors are generational, operating under the assumption that \"most objects die young.\" Such collectors allocate all objects in a frequently collected nursery and tenure only surviving objects to a less-frequently collected, older generation. But these survivors induce copying costs upon tenuring. To avoid these costs, pretenuring schemes construct classifiers to predict whether an object will be long-lived or short-lived; accordingly, it is tenured or not immediately upon allocation. Up to now, however, these predictions did not account for one important fact: the proximity of the next collection. In contrast, headroom-based pretenuring does take this into account; thus, it can dynamically pretenure objects whenever they live \"long enough.\"\n We devised two ways to estimate an object's lifetime from garbage collection traces. This led to two headroom-based pre-tenuring schemes, which we implemented on top of Jikes RVM and MMTk. Our experiments show that the dynamic, headroom-based pretenuring schemes outperform static schemes in terms of collector performance, albeit at the cost of increased mutator overhead.","PeriodicalId":169989,"journal":{"name":"Principles and Practice of Programming in Java","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Headroom-based pretenuring: dynamically pretenuring objects that live \\\"long enough\\\"\",\"authors\":\"Andreas Sewe, Dingwen Yuan, Jan Sinschek, M. Mezini\",\"doi\":\"10.1145/1852761.1852767\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Many modern garbage collectors are generational, operating under the assumption that \\\"most objects die young.\\\" Such collectors allocate all objects in a frequently collected nursery and tenure only surviving objects to a less-frequently collected, older generation. But these survivors induce copying costs upon tenuring. To avoid these costs, pretenuring schemes construct classifiers to predict whether an object will be long-lived or short-lived; accordingly, it is tenured or not immediately upon allocation. Up to now, however, these predictions did not account for one important fact: the proximity of the next collection. In contrast, headroom-based pretenuring does take this into account; thus, it can dynamically pretenure objects whenever they live \\\"long enough.\\\"\\n We devised two ways to estimate an object's lifetime from garbage collection traces. This led to two headroom-based pre-tenuring schemes, which we implemented on top of Jikes RVM and MMTk. Our experiments show that the dynamic, headroom-based pretenuring schemes outperform static schemes in terms of collector performance, albeit at the cost of increased mutator overhead.\",\"PeriodicalId\":169989,\"journal\":{\"name\":\"Principles and Practice of Programming in Java\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Principles and Practice of Programming in Java\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1852761.1852767\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Principles and Practice of Programming in Java","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1852761.1852767","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Headroom-based pretenuring: dynamically pretenuring objects that live "long enough"
Many modern garbage collectors are generational, operating under the assumption that "most objects die young." Such collectors allocate all objects in a frequently collected nursery and tenure only surviving objects to a less-frequently collected, older generation. But these survivors induce copying costs upon tenuring. To avoid these costs, pretenuring schemes construct classifiers to predict whether an object will be long-lived or short-lived; accordingly, it is tenured or not immediately upon allocation. Up to now, however, these predictions did not account for one important fact: the proximity of the next collection. In contrast, headroom-based pretenuring does take this into account; thus, it can dynamically pretenure objects whenever they live "long enough."
We devised two ways to estimate an object's lifetime from garbage collection traces. This led to two headroom-based pre-tenuring schemes, which we implemented on top of Jikes RVM and MMTk. Our experiments show that the dynamic, headroom-based pretenuring schemes outperform static schemes in terms of collector performance, albeit at the cost of increased mutator overhead.