Efficient Memory Management For SMPs Running Parallel And Sequential Workloads
Document Type
Conference Proceeding
Publication Date
2002
Published In
Proceedings Of The 14th IASTED International Conference On Parallel And Distributed Computing And Systems
Abstract
Shared memory multiprocessor systems are becoming common as server, workstation, and desktop systems. These systems are likely to run a mixed sequential and shared memory parallel workload. To efficiently manage memory on such a system, the memory manager must make page replacement decisions when more than one process shares a memory page either explicitly as part of a shared memory parallel application, or implicitly through system support for read-only sharing of dynamically linked library code. Many current systems implement some type of LRU replacement scheme and have coarse-grained solutions for considering sharing degree in the replacement policy. For systems running a predominantly parallel workload, these solutions are unlikely to work well as either shared pages are replaced too frequently or the set of pageable pages be comes very small when shared un-replaceable pages fill physical memory. We propose a memory management algorithm that approximates the Working Set model for shared and private pages. Our algorithm is an extension of Carr and Hennessy’s WSClock algorithm that supports efficiently maintaining working set information for shared pages. Based on preliminary tests, our algorithm that makes replacement decisions based on working set infor mation of processes sharing a page, will result in better memory utilization.
Keywords
SMP, Memory Management, Working Set
Published By
ACTA Press
Editor(s)
S.G. Akl and T. Gonzalez
Conference
14th IASTED International Conference On Parallel And Distributed Computing And Systems
Conference Dates
November 4-6, 2002
Conference Location
Cambridge, MA
Recommended Citation
Tia Newhall and Patrick Boe , '01.
(2002).
"Efficient Memory Management For SMPs Running Parallel And Sequential Workloads".
Proceedings Of The 14th IASTED International Conference On Parallel And Distributed Computing And Systems.
https://works.swarthmore.edu/fac-comp-sci/82