Beowulf Cluster Computing with Linux
Beowulf Cluster Computing with Linux
High-Performance Computing: Clusters, Constellations, MPPs, and Future Directions
Computing in Science and Engineering
International Journal of High Performance Computing Applications
FCCM '07 Proceedings of the 15th Annual IEEE Symposium on Field-Programmable Custom Computing Machines
Dynamic load balancing with adaptive factoring methods in scientific applications
The Journal of Supercomputing
Exploiting Partial Runtime Reconfiguration for High-Performance Reconfigurable Computing
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
A view of the parallel computing landscape
Communications of the ACM - A View of Parallel Computing
The Biggest Need: a New Model of Computation
International Journal of High Performance Computing Applications
International Journal of High Performance Computing Applications
High-Performance Cloud Computing: A View of Scientific Applications
ISPAN '09 Proceedings of the 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks
A dynamic framework for integrated management of all types of resources in P2P systems
The Journal of Supercomputing
Highly scalable dynamic load balancing in the atmospheric modeling system COSMO-SPECS+FD4
PARA'10 Proceedings of the 10th international conference on Applied Parallel and Scientific Computing - Volume Part I
Scheduling heterogeneous multi-cores through Performance Impact Estimation (PIE)
Proceedings of the 39th Annual International Symposium on Computer Architecture
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High Performance Cluster Computing Systems (HPCSs) represent the best performance because their configuration is customized regarding the features of the problem to be solved at design time. Therefore, if the problem has static nature and features, the best customized configuration can be done. New generations of scientific and industrial problems usually have dynamic nature and behavior. A drawback of this dynamicity is that the customized HPCSs face challenges at runtime, and consequently show the worse performance. The reason for this might be due to the fact that dynamic problems are not adapted to configuration of the HPCS. Hence, requests of the dynamic problem are not in the direction of the HPCS configuration. The main proposed solutions for this challenge are dynamic load balancing or using reconfigurable platforms.In this paper, a vector algebra-based model for HPCS reconfiguration at runtime is presented and named AMRC. This model determines the element causing the dynamic behavior and analyzes the reason regarding both software and hardware at runtime. Some results of the presented model show that by defining a general state vector whose direction is toward reaching high performance computing and whose weight is based on the initial features and explicit requirements of the problem, as well as by defining a vector for each process in the problem at runtime, we can trace changes in the directions and uncover the reason for them.