Multi-CMP module system based on a look-ahead configured global network
PPAM'09 Proceedings of the 8th international conference on Parallel processing and applied mathematics: Part I
Multi-CMP system with data communication on the fly
The Journal of Supercomputing
Scheduling architecture---supported regions in parallel programs
PARA'10 Proceedings of the 10th international conference on Applied Parallel and Scientific Computing - Volume Part I
Data transfers on the fly for hierarchical systems of chip multi-processors
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
Scheduling parallel programs based on architecture: supported regions
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part II
Parallel matrix multiplication based on dynamic SMP clusters in SoC technology
ISPA'07 Proceedings of the 2007 international conference on Frontiers of High Performance Computing and Networking
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The paper presents an analysis of the suitability of the architecture of dynamic SMP clusters with communication on the fly for massively parallel fine grain numerical computations. It is assumed that the proposed architecture is implemented using the highly modular " system on chip" and "network on chip" technology. This technology is considered to provide soon a very large number of co-operating processors embedded in a single parallel system, thus enabling massively parallel computations. The proposed architecture of dynamic clusters with communication on the fly meets requirements of large scale fine grain computations and can be successfully applied in this technology. Experimental simulation results are presented concerning efficiency of fine grain parallel implementation of a typical numerical problem which is matrix multiplication based on recursive data decomposition. Selection of optimal parallel computation grain is discussed. Estimations of the efficiency of the proposed methods for fine grain computations for large problem size are presented.