Communications of the ACM
Memory-reference characteristics of multiprocessor applications under MACH
SIGMETRICS '88 Proceedings of the 1988 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Problems in characterizing barrier performance
Instrumentation for future parallel computing systems
Multiprocessor performance
ACM Computing Surveys (CSUR)
Minimizing wasted space in partitioned segmentation
Communications of the ACM
Dynamic space-sharing in computer systems
Communications of the ACM
Operating Systems
Dynamic decentralized cache schemes for mimd parallel processors
ISCA '84 Proceedings of the 11th annual international symposium on Computer architecture
Dynamic and static load scheduling performance on a NUMA shared memory multiprocessor
ICS '91 Proceedings of the 5th international conference on Supercomputing
Performance Prediction and Evaluation of Parallel Processing on a NUMA Multiprocessor
IEEE Transactions on Software Engineering
ICS '93 Proceedings of the 7th international conference on Supercomputing
Analytical performance prediction on multicomputers
Proceedings of the 1993 ACM/IEEE conference on Supercomputing
Performance prediction based loop scheduling for heterogeneous computing environment
SAC '97 Proceedings of the 1997 ACM symposium on Applied computing
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Shared-memory multiprocessor performance is strongly affected by factors such as sequential code, barriers, cache coherence, virtual memory paging, and the multiprocessor system itself with resource scheduling and multiprogramming. Several timing models and analysis for these effects are presented. A modified Ware model based on these timing models is given to evaluate comprehensive performance of a shared-memory multiprocessor. Performance measurement has been done on the Encore Multimax, a shared-memory multiprocessor. The evaluation models are the analyses based on a general shared-memory multiprocessor system and architecture and can be applied to other types of shared-memory multiprocessors. Analytical and experimental results give a clear understanding of the various effects and a correct measure of the performance, which are important for the effective use of a shared-memory multiprocessor.