Spatial data partitioning techniques are important for obtaining fast and efficient simulations of N-Body particle and spatial agent based models where they considerably reduce redundant entity interaction computation times. Highly parallel techniques based on concurrent threading can be deployed to further speed up such simulations. We study the use of GPU accelerators and highly data parallel...

General purpose graphic processing unit (GPGPU) provides high performance resource for computing. CUDA (Compute Unified Device Architecture) and OpenCL (Open Computing Language) permit writing of parallel computing programs that utilize multiple central processing units (CPU) and GPGPUs. The image processing library, OpenCV (Open Source Computer Vision library), may benefit greatly from paralle...

Several recent methods have been proposed to obtain significant speed-ups in MRI image reconstruction by leveraging the computational power of GPUs. Previously, we implemented a GPU-based image reconstruction technique called the Illinois Massively Parallel Acquisition Toolkit for Image reconstruction with ENhanced Throughput in MRI (IMPATIENT MRI) for reconstructing data collected along arbitr...

High throughput sequencing has become a powerful technique for genome analysis after this concept was raised in recent years. Currently, there is a huge demand from patients that have genetic diseases which cannot be satisfied due to the limitation of computation power. Though several softwares are developed using currently most efficient algorithm to deal with various types of sequencing probl...

This paper presents a novel approach, termed GPU-CDT, to compute the constrained Delaunay triangulation (CDT) for a planar straight line graph (PSLG), consisting of points and edges, using the graphics processing unit (GPU). Although there are many algorithms for constructing the 2D CDT using the CPU, there has been no known prior approach using the parallel computing power of the GPU efficient...

A* search is a fundamental topic in artificial intelligence. Recently, the general purpose computation on graphics processing units (GPGPU) has been widely used to accelerate numerous computational tasks. In this paper, we propose the first parallel variant of the A* search algorithm such that the search process of an agent can be accelerated by a single GPU processor in a massively parallel fa...

The lattice Boltzmann method (LBM) is an innovative and promising approach in computational fluid dynamics. From an algorithmic standpoint it reduces to a regular data parallel procedure and is therefore well-suited to high performance computations. Numerous works report efficient implementations of the LBM for the GPU, but very few mention multi-GPU versions and even fewer GPU cluster implemen...

The Single-Source Shortest Path (SSSP) problem arises in many different fields. In this paper we present a GPUbased version of the Crauser et al. SSSP algorithm. Our work significantly speeds up the computation of the SSSP, not only with respect to the CPU-based version, but also to other state-ofthe-art GPU implementation based on Dijkstra, due to Martı́n et al. Both GPU implementations have be...

Image segmentation is one of the most common steps in digital image processing. The area many image segmentation algorithms (e.g., thresholding, edge detection, and region growing) employed for classifying a digital image into different segments. In this connection, finding a suitable algorithm for medical image segmentation is a challenging task due to mainly the noise, low contrast, and steep...

Graphics processing units (GPUs) can act as an attractive alternative to CPUs for general purpose computation in certain scenarios. Traditionally, the GPU has been developed to offload graphics processing from the CPU. In recent years the GPU has continued to become a more flexible and powerful device, responding to the demand of the games industry to execute more and more complex custom graphi...