Recent activation and electrophysiological studies have demonstrated that sound recognition and localization are processed in two distinct cortical networks that are each present in both hemispheres. Sound recognition and/or localization may be, however, disrupted by purely unilateral damage, suggesting that Processing within one hemisphere may not be sufficient or may be disturbed by the contra lateral lesion. Based on the results of our research on acute and chronic lesions, patterns of recovery and functional imaging, evidence will be presented, which support the latter hypothesis.

This paper gives a new application of DEA to evaluate the scheduling solutions of Parallel Processing. It evaluates the scheduling solutions of Parallel Processing using the non-convex DEA model, FDH model. By introducing each solution of Parallel Processing scheduling as a DMU with some relevant inputs and outputs this paper shows that how the most efficient schedule(s) can be identified.

There are different variants of Particle Swarm Optimization (PSO) algorithm such as Adaptive Particle Swarm Optimization (APSO) and Particle Swarm Optimization with an Aging Leader and Challengers (ALC-PSO). These algorithms improve the performance of PSO in terms of finding the best solution and accelerating the convergence speed. However, these algorithms are computationally intensive. The goal of this paper is high performance implementations of Traditional PSO (TPSO), APSO and ALCPSO using CUDA technology. We have implemented these three algorithms on both central Processing unit (CPU) and graphics Processing unit (GPU) in order to analyze and improve their performance and reduce their computational times. We have achieved speedups up to 14. 5x, 31x, and 152x, for GPUTPSO, GPU-ALCPSO, and GPU-APSO, respectively. In addition, different number of threads has been chosen in order to find an appropriate number of threads per block for both APSO and ALC-PSO algorithms. Our experimental results show that the best choice for number of threads per block depends on the number of existing variables and constants in each algorithm and the number of registers per multiprocessor.

This paper presents an investigation into the development of stresses in anchorage zone in prestressed post-tensioned concrete beam using the finite element analysis. A finite element computer code on the platform of a supercomputer PARAM 10000 is developed and employed for the present study. A Parallel algorithm for matrix inversion method is developed and implemented in the presented finite element code. Concentric and eccentric prestressing forces are applied for prestressing of the beams. Effect of Poisson’s ratio over the bursting tensile force developed in the anchorage zone is studied and an equation to compute the magnitude of bursting tensile force by incorporating the effect of Poisson’s ratio is proposed. Development of spalling zone is also investigated and it was found that due to eccentric loading, stresses of higher magnitude were developed in this zone.Supercomputer PARAM 10000 is used to carry out present analysis and time reduction has been achieved by using multiple processors of the supercomputer PARAM 10000. At the end the performance of the developed Parallel code is studied and presented.

Background and Objectives: Louvain is a time-consuming community detection algorithm especially in large-scale networks. Using Graphic Processing Unit (GPU) in order to calculate modularity sigma, which is a major Processing section in Louvain algorithm, can reduce algorithm execution time and make it practical for large-scale networks. Methods: The proposed algorithm Dynamic CUDA Louvain Method (DCLM) blocks hardware threads dynamically on cores inside GPU. By considering the properties of GPU, this algorithm allocates the maximal number of Processing cores to each Stream Multi-Processor (SM) as number of threads in a block. If the number of nodes in the graph is smaller than all physical cores on GPU, number of threads per block Is equal to the ratio number of graph nodes over the number of SMs. Results: The implementation results demonstrated that the proposed algorithm is able to decrease the run time by 15% in comparison with the best past method in the large-scale graph. Conclusion: We have introduced DCLM algorithm based on GPU that accelerates Louvain community detection algorithm. Dynamic allocation of threads to each block has a significant effect on the reduction of algorithm execution time. However, incrementing the number of threads per block alone does not result to acceleration the speed of calculations.