LogP: towards a realistic model of parallel computation
PPOPP '93 Proceedings of the fourth ACM SIGPLAN symposium on Principles and practice of parallel programming
Parallel approach to tracking edge segments in dynamic scenes
Image and Vision Computing
LoPC: modeling contention in parallel algorithms
PPOPP '97 Proceedings of the sixth ACM SIGPLAN symposium on Principles and practice of parallel programming
Predictive analysis of a wavefront application using LogGP
Proceedings of the seventh ACM SIGPLAN symposium on Principles and practice of parallel programming
LogGPS: a parallel computational model for synchronization analysis
PPoPP '01 Proceedings of the eighth ACM SIGPLAN symposium on Principles and practices of parallel programming
Background Compensation and an Active-Camera Motion Tracking Algorithm
ICPP '97 Proceedings of the international Conference on Parallel Processing
Predicting and Evaluating Distributed Communication Performance
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
Transformations to Parallel Codes for Communication-Computation Overlap
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Techniques for pipelined broadcast on ethernet switched clusters
Journal of Parallel and Distributed Computing
Architectural Optimizations in Multi-Core Processors
Architectural Optimizations in Multi-Core Processors
| Hi-index | 0.00 |
Despite the advancement and availability of the multiple core microprocessors, it remains an issue on how to fully utilize this relatively new computing platform to achieve optimal performance for a parallel algorithm. There are limitations to the existing theoretical model in analyzing parallel algorithms for multi-core microprocessor systems. The proposed Multi-core LogP (MLogP) model is a more realistic model for parallel computing with multi-core microprocessor. The MLogP model is a variant of the popular LogP model for parallel computation. Experiment with parallel image processing algorithms were used to determine the abilities of LogP and MLogP models in predicting the performance of parallel image processing algorithms on a Intel Core2 Quad 2.44 GHz microprocessor.