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USENIX 2005 Annual Technical Conference, General Track — Abstract

Pp. 237–250 of the Proceedings

Adaptive Main Memory Compression

Irina Chihaia Tuduce and Thomas Gross, ETH Zürich


Applications that use large data sets frequently exhibit poor performance because the size of their working set exceeds the real memory, causing excess page faults, and ultimately exhibit thrashing behavior.

This paper describes a memory compression solution to this problem that adapts the allocation of real memory between uncompressed and compressed pages and also manages fragmentation without user involvement. The system manages its resources dynamically on the basis of the varying demands of each application and also on the situational requirements that are data dependent. The technique used to localize page fragments in the compressed area allows the system to reclaim or add space easily if it is advisable to shrink or grow the size of the compressed area.

The design is implemented in Linux, runs on both 32-bit and 64-bit architectures, and has been demonstrated to work in practice under complex workload conditions and memory pressure. The benefits from our approach depend on the relationship between the size of the compressed area, the application's compression ratio, and the access pattern of the application. For a range of benchmarks and applications, the system shows an increase in performance by a factor of 1.3 to 55.

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