Superlinear speedup of an efficient sequential algorithm is not possible
Parallel Computing
Algorithm 806: SPRNG: a scalable library for pseudorandom number generation
ACM Transactions on Mathematical Software (TOMS)
Domain Decomposition Models for Parallel Monte Carlo Transport
The Journal of Supercomputing
Parallelization of Geant4 Using TOP-C and Marshalgen
NCA '06 Proceedings of the Fifth IEEE International Symposium on Network Computing and Applications
Multithreaded Geant4: semi-automatic transformation into scalable thread-parallel software
Euro-Par'10 Proceedings of the 16th international Euro-Par conference on Parallel processing: Part II
Future Generation Computer Systems
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GATE/Geant4 Monte Carlo simulations are computationally demanding applications, requiring thousands of processor hours to produce realistic results. The classical strategy of distributing the simulation of individual events does not apply efficiently for Positron Emission Tomography (PET) experiments, because it requires a centralized coincidence processing and large communication overheads. We propose a parallel computational model for GATE that handles event generation and coincidence processing in a simple and efficient way by decentralizing event generation and processing but maintaining a centralized event and time coordinator. The model is implemented with the inclusion of a new set of factory classes that can run the same executable in sequential or parallel mode. A Mann-Whitney test shows that the output produced by this parallel model in terms of number of tallies is equivalent (but not equal) to its sequential counterpart. Computational performance evaluation shows that the software is scalable and well balanced.