There are several different methods to make an efficient strategy for steganalysis of digital images. A very powerful method in this area is rich model consisting of a large number of diverse sub-models in both spatial and transform domain that should be utilized. However, the extraction of a various types of features from an image is so time consuming in some steps, especially for training phase with a large number of high resolution images that consist of two steps: train and test.Multithread programming is a near solution to decreasing the required time but it’s limited and it ‘snot so scalable too. In this paper, we present a CUDA based approach for data-parallelization and optimization of sub-model extraction process. Also, construction of the rich model is analyzed in detailed, presenting more efficient solution. Further, some optimization techniques are employed to reduce the total number of GPU memory accesses. Compared to single-thread and multi-threaded CPU processing, 10x-12x and 3x-4x speedups are achieved with implementing our CUDA-based parallel program on GT 540M and it can be scaled with several CUDA cards to achieve better speedups…..

Among different discretization approaches, Finite Difference Method (FDM) is widely used for acoustic and elastic full-wave form modeling. An inevitable deficit of the technique, however, is its sever requirement to computational resources. A promising solution is parallelization, where the problem is broken into several segments, and the calculations are distributed over different processors. For the present FD routines, however, such parallelization technique inevitably needs domain-decomposition and inter-core data exchange, due to the coupling of the governing equations. In this study, a new FD-based procedure for seismic wave modeling, named as ‘ Modal Finite Difference Method (MFDM)” is introduced, which deals with the simulation in the decoupled modal space; thus, neither domaindecomposition nor inter-core data exchange is anymore required, which greatly simplifies parallelization for both MPI-and CUDA implementations over CPUs and GPUs. With MFDM, it is also possible to simply cut off less-significant modes and run the routine for just the important ones, which will effectively reduce computation and storage costs. The efficiency of the proposed MFDM is shown by some numerical examples.

A double-GPU code is developed to accelerate WENO schemes. The test problem is a compressible viscous flow. The convective terms are discretized using third-to ninth-order WENO schemes and the viscous terms are discretized by the standard fourth-order central scheme. The code written in CUDA programming language is developed by modifying a single-GPU code. The OpenMP library is used for parallel execution of the code on both the GPUs. Data transfer between GPUs which is the main issue in developing the code, is carried out by defining halo points for numerical grids and by using a CUDA built-in function. The code is executed on a PC equipped with two heterogeneous GPUs. The computational times of different schemes are obtained and the speedups with respect to the single-GPU code are reported for different number of grid points. Furthermore, the developed code is analyzed by CUDA profiling tools. The analyze helps to further increase the code performance.

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.