We present an algorithm for NVIDIA (CUDA) platform based on Smith-Waterman algorithm for sequence alignment problem. (CUDA) is a new programming language which is very similar to the standard C language, with some extensions. By using the Smith-Waterman algorithm which is used to find similarity between two sequences by making a scoring matrix; this application tries to find the similarity between a sequence which called query sequence and sequences in a database file. In this program, each thread is used for calculating one scoring matrix between the query sequence and one of the sequences in database. The algorithm utilizes small but fast shared memory which is inside the GPU for holding four intermediate columns in each cycle for each thread. If the database is large enough, it can keep the GPU in full working order and results in better speedup in comparison to CPU. To demonstrate the performance, we used a high-end CPU, Intel Core 2 Due E6600 and a mid-end GPU, GeForce 8600GT and saw the speedup in about twenty times more than CPU.

Bacterial foraging algorithm is one of the population-based optimization algorithms that used for solving many search problems in various branches of sciences. One of the issues discussed today is Parallel implementation of population-based optimization algorithms on Graphic Processor Units. Due to the low speed of bacterial foraging algorithm in the face of complex problem and also lack the ability to solve large-scale problems by this algorithm, Implementation on the graphics processor is a suitable solution to cover the weaknesses of this algorithm. In this paper, we proposed a Parallel version of bacterial foraging algorithm which designed by (CUDA) and has ability to run on GPUs. The performance of this algorithm is evaluated by using a number of famous optimization problems in comparison with the standard bacterial foraging optimization algorithm. The results show that Parallel Algorithm is faster and more efficient than standard bacterial foraging optimization algorithm.

For many years, researchers have studied high accuracy methods for recognizing the handwriting and achieved many significant improvements. However, an issue that has rarely been studied is the speed of these methods. Considering the computer hardware limitations, it is necessary for these methods to run in high speed. One of the methods to increase the processing speed is to use the computer Parallel processing power. This paper introduces one of the best feature extraction methods for the handwritten recognition, called DPP (Derivative Projection Profile), which is employed for isolated Persian handwritten recognition. In addition to achieving good results, this (computationally) light feature can easily be processed. Moreover, Hamming Neural Network is used to classify this system. To increase the speed, some part of the recognition method is executed on GPU (graphic processing unit) cores implemented by (CUDA) platform. HADAF database (Biggest isolated Persian character database) is utilized to evaluate the system. The results show 94.5% accuracy. We also achieved about 5.5 times speed-up using GPU.

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.

Direction of Arrival (DOA) estimation of sound sources using phased array-based methods has a lot of importance in various fields, including sonar, robot vision and mechanical defect detection. Adaptive beamforming methods, such as the MVDR (Minimum Variance Distortionless Response) algorithm, have high resolution compared to non-adaptive methods; but this advantage is achieved in return for the computational complexity of these algorithms. This makes it hard to use these algorithms in applications that require real-time sound source DOA estimation. On the other hand, an important feature of the adaptive beamforming methods including MVDR is the high potential of these algorithms for Parallelization. The purpose of this paper is the Parallel implementation of the MVDR algorithm by employing GPU instead of CPU to increase the execution speed and achieve real-time mode. To achieve this purpose, the (CUDA) programming model has been used to implement the algorithm on the GPU. In order to investigate the performance of Parallel implementation of the MVDR algorithm, two different GPUs, as well as CPUs, have been used. The performance validity of various implementations in this paper was confirmed by real sonar data as well as simulation data. The results show that using an array of 64 sensors, it is possible to estimate the DOA of underwater sound sources in real-time and with high resolution using the MVDR algorithm.

Evaluation of Solid-Fluid interaction is of high significance in all engineering fields. In this paper, the Smoothed Particle Hydrodynamics (SPH) and the Discrete Element Method (DEM) were employed to simulate solids and fluids, respectively. As for the simulation of water behavior, first, a single-phase “dam break” experiment is modeled by the SPH. In this model, variable smoothing length and an almost novel boundary method were utilized which resulted in a tremendous boost in its resemblance to the experiment. To couple these methods (SPH and DEM), three approaches were proposed and validated against a famous experimental test named “dam break with an elastic gate test”. Finally, to be sure of the correctness of these approaches, an elastic plate under a water column in the hydrostatic condition was simulated and the error was 2 percent in comparison with the analytical solution. In pursuit of having short run-times, Parallel computing on GPU ((CUDA)) was employed, and a robust nearest neighbor search (NNS) algorithm was modified and developed.