Profile-guided proactive garbage collection for locality optimization

Profile-guided proactive garbage collection for locality optimization (2006)

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by Wen-ke Chen , Sanjay Bhansali , Trishul Chilimbi , Xiaofeng Gao , Weihaw Chuang

Venue:	In Proceedings of ACM SIGPLAN Conference on Programming Languages Design and Implementation
Citations:	13 - 0 self

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Datum	Value	Source
TITLE	Profile-guided proactive garbage collection for locality optimization	INFERENCE
AUTHOR NAME	Wen-ke Chen	SVM HeaderParse 0.2
AUTHOR AFFIL	Microsoft Research; One Microsoft	SVM HeaderParse 0.2
AUTHOR ADDR	Way; Redmond, WA 98052	SVM HeaderParse 0.2
AUTHOR NAME	Sanjay Bhansali	SVM HeaderParse 0.2
AUTHOR AFFIL	Microsoft Research; One Microsoft	SVM HeaderParse 0.2
AUTHOR ADDR	Way; Redmond, WA 98052	SVM HeaderParse 0.2
AUTHOR NAME	Trishul Chilimbi	SVM HeaderParse 0.2
AUTHOR AFFIL	Microsoft Research; One Microsoft	SVM HeaderParse 0.2
AUTHOR ADDR	Way; Redmond, WA 98052	SVM HeaderParse 0.2
AUTHOR NAME	Xiaofeng Gao	SVM HeaderParse 0.2
AUTHOR AFFIL	Dept. of Computer Scienc...	SVM HeaderParse 0.2
AUTHOR NAME	Weihaw Chuang	SVM HeaderParse 0.2
AUTHOR AFFIL	Dept. of Computer Scienc...	SVM HeaderParse 0.2
ABSTRACT	Many applications written in garbage collected languages have large dynamic working sets and poor data locality. We present a new system for continuously improving program data locality at run time with low overhead. Our system proactively reorganizes the heap by leveraging the garbage collector and uses profile information collected through a low-overhead mechanism to guide the reorganization at run time. The key contributions include making a case that garbage collection should be viewed as a proactive technique for improving data locality by triggering garbage collection for locality optimization independently of normal garbage collection for space, combining page and cache locality optimization in the same system, and demonstrating that sampling provides sufficiently detailed data access information to guide both page and cache locality optimization with low runtime overhead. We present experimental results obtained by modifying a commercial, state-of-the-art garbage collector to support our claims. Independently triggering garbage collection for locality optimization significantly improved optimizations benefits. Combining page and cache locality optimizations in the same system provided larger average execution time improvements (17%) than either alone (page 8%, cache 7%). Finally, using sampling limited profiling overhead to less than 3%, on average. Categories and Subject Descriptors D.3.4 [Programming Languages]: Processors – code generation, memory management (garbage collectors), optimization, run-time	SVM HeaderParse 0.2
YEAR	2006	INFERENCE
VENUE	In Proceedings of ACM SIGPLAN Conference on Programming Languages Design and Implementation	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	332--340	INFERENCE
CITATIONS	25 found	ParsCit 1.0