Parallelizing TUNAMI-N1 Using GPGPU

We present a high performance tsunami-prediction system using General Purpose Graphics Processing Units (GPGPU). It is based on TUNAMI-N1, a Numerical Analysis Model for Investigation of near-field tsunamis. It uses linear shallow water wave equations, commonly accepted approximation for tsunami propagation, taking the input from a bathymetry file containing a large data set. Due to the largeness of the data set, the model is more amenable to parallelization. The system maps the TUNAMI-N1 model into the massively parallel GPU architecture using Nvidia CUDA framework. It employs multiple kernels that contain inherently parallel portion of the model and uses the concepts of data and hybrid parallelism to fully exploit the hardware capabilities of the GPUs. Experimental results show that our system achieves a speed up of six time

Growth characteristics modeling of Bifidobacterium bifidum using RSM and ANN

The aim of this work was to optimize the biomass production by Bifidobacterium bifidum 255 using the response surface methodology (RSM) and artificial neural network (ANN) both coupled with GA. To develop the empirical model for the yield of probiotic bacteria, additional carbon and nitrogen content, inoculum size, age, temperature and pH were selected as the parameters. Models were developed using ¼ fractional factorial design (FFD) of the experiments with the selected parameters. The normalized percentage mean squared error obtained from the ANN and RSM models were 0.05 and 0.1%, respectively. Regression coefficient (R²) of the ANN model showed higher prediction accuracy compared to that of the RSM model. The empirical yield model (for both ANN and RSM) obtained were utilized as the objective functions to be maximized with the help of genetic algorithm. The optimal conditions for the maximal biomass yield were 37.4 °C, pH 7.09, inoculum volume 1.97 ml, inoculum age 58.58 h, carbon content 41.74% (w/v), and nitrogen content 46.23% (w/v). The work reported is a novel concept of combining the statistical modeling and evolutionary optimization for an improved yield of cell mass of B. bifidum 255