Scheduling memory constrained jobs on distributed memory parallel computers
Proceedings of the 1995 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Exploiting process lifetime distributions for dynamic load balancing
ACM Transactions on Computer Systems (TOCS)
ICS '01 Proceedings of the 15th international conference on Supercomputing
Paging tradeoffs in distributed-shared-memory multiprocessors
Proceedings of the 1994 ACM/IEEE conference on Supercomputing
IPPS '95 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Gang Scheduling with Memory Considerations
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
Adaptive scheduling under memory constraints on non-dedicated computational farms
Future Generation Computer Systems - Selected papers from CCGRID 2002
LOMARC: Lookahead Matchmaking for Multiresource Coscheduling on Hyperthreaded CPUs
IEEE Transactions on Parallel and Distributed Systems
LOMARC — lookahead matchmaking for multi-resource coscheduling
JSSPP'04 Proceedings of the 10th international conference on Job Scheduling Strategies for Parallel Processing
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We present a simple scheduling strategy that copes with the adverse effects of paging on multiprogrammed SMPs. We consider open, multiuser SMP servers, typically found in academic or industrial environments. Our strategy incorporates four uniquely combined features. It is adaptive, in the sense that the programs themselves take scheduling actions upon detecting memory pressure; it is dynamic, since programs detect the likelihood of paging at runtime by communicating with the operating system through a lightweight interface; it is preventive, because it takes scheduling actions before paging occurs; and it is non-intrusive, because the local scheduling actions taken by a program do not affect adversely, but act to the benefit of other programs sharing the system. We present an efficient implementation of our strategy in Linux and show with a realistic production workload that it can improve the response time of the Linux kernel under memory pressure by up to a factor of eight and the throughput by up to a factor of four.