High-performance computing capability enabled the development of a multi-energy X-ray imaging system which requires image reconstruction from multi-energy images. Usually, pararell processing of CUDA is faster than CPU. However, the CUDA could not be taking advantage depending on kind of program. In this paper, we investigate the performance of the CUDA for parallel processing in order to evalu...

Modern computers have graphics cards with much higher theoretical efficiency than conventional CPU. The paper presents application possibilities GPU CUDA acceleration for encryption of data using the new architecture tailored to the 3DES algorithm, characterized by increased security compared to the normal DES. The algorithm used in ECB mode (Electronic Codebook), in which 64-bit data blocks ar...

The future of computation is the Graphical Processing Unit, i.e. the GPU. The promise that the graphics cards have shown in the field of image processing and accelerated rendering of 3D scenes, and the computational capability that these GPUs possess, they are developing into great parallel computing units. It is quite simple to program a graphics processor to perform general parallel tasks. Bu...

Soluble epoxide hydrolase (sEH) plays a major role in regulating vascular epoxyeicosatrienoic acid metabolism and function, and substituted urea derivatives that inhibit sEH activity reduce blood pressure in hypertensive rats. We found that substituted urea derivatives containing a dodecanoic acid group, besides effectively inhibiting sEH, increased peroxisome proliferator-activated receptor (P...

The main problem of the kernel density estimation methods is the huge computational requirements, especially for large data sets. One way for accelerating these methods is to use the parallel processing. Recent advances in parallel processing have focused on the use Graphics Processing Units (GPUs) using Compute Unified Device Architecture (CUDA) programming model. In this work we discuss a nai...

The use of modern, high-performance graphical processing units (GPUs) for acceleration of scientific computation has been widely reported. The majority of this work has used the CUDA programming model supported exclusively by GPUs manufactured by NVIDIA. An industry standardisation effort has recently produced the OpenCL specification for GPU programming. This offers the benefits of hardware-in...

Nowadays, the paradigm of parallel computing is changing. CUDA is now a popular programming model for general purpose computations on GPUs and a great number of applications were ported to CUDA obtaining speedups of orders of magnitude comparing to optimized CPU implementations. Hybrid approaches that combine the message passing model with the shared memory model for parallel computing are a so...

We investigate multi-level parallelism on GPU clusters with MPI-CUDA and hybrid MPI-OpenMP-CUDA parallel implementations, in which all computations are done on the GPU using CUDA. We explore efficiency and scalability of incompressible flow computations using up to 256 GPUs on a problem with approximately 17.2 billion cells. Our work addresses some of the unique issues faced when merging fine-g...