NDLI: Program-level adaptive memory management

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Program-level adaptive memory management

Content Provider	ACM Digital Library
Author	Kelsey, Kirk Zhang, Chengliang Ogihara, Mitsunori Ding, Chen Shen, Xipeng Hertz, Matthew
Abstract	Most application's performance is impacted by the amount of available memory. In a traditional application, which has a fixed working set size, increasing memory has a beneficial effect up until the application's working set is met. In the presence of garbage collection this relationship becomes more complex. While increasing the size of the program's heap reduces the frequency of collections, collecting a heap with memory paged to the backing store is very expensive. We first demonstrate the presence of an optimal heap size for a number of applications running on a machine with a specific configuration. We then introduce a scheme which adaptively finds this good heap size. In this scheme, we track the memory usage and number of page faults at a program's phase boundaries. Using this information, the system selects the soft heap size. By adapting itself dynamically, our scheme is independent of the underlying main memory size, code optimizations, and garbage collection algorithm. We present several experiments on real applications to show the effectiveness of our approach. Our results show that program-level heap control provides up to a factor of 7.8 overall speedup versus using the best possible fixed heap size controlled by the virtual machine on identical garbage collectors.
Starting Page	174
Ending Page	183
Page Count	10
File Format	PDF
ISBN	1595932216
DOI	10.1145/1133956.1133979
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-06-10
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Garbage collection Heap sizing Adaptive Paging Program-level
Content Type	Text
Resource Type	Article

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