. This paper shows the complexity involved in optimizing applications for one such system and one relatively simple methodology for reducing the workload involved in the optimization process. This work is based on one such highly-parallel system, the GeForce 8800 GTX using CUDA. Its flexible allocation

, it might be necessary to visualize 3D datasets obtained from CT or MRI scanners as a meaningful 2D image, in a process called volume rendering. As a result of the popularity and usefulness of volume data, a broad class of volume rendering techniques has emerged. Ray casting is one of these techniques

. In this paper we have proposed technique that process image for face detection and recognition in parallel on NVIDIA GeForce GTX 770 GPU which has compute Capability of 3.0. We have used Viola and Jones for face detection and PCA Eigenfaces algorithm for face recognition. The viola and jones shown result

by either genome tiling array analysis (ChIP-chip) 1–3 or massively parallel sequencing (ChIP-seq) 4–10 enables transcriptional regulation to be studied on a genome-wide scale (Supplementary Fig. 1 online). By systematically identifying protein-DNA interactions of interest, studies using these

. In contrast, we select sample points adaptively in a single pass, enabling parallel computation. As a result, our algorithm runs entirely on the GPU, achieving interactive rates for scenes with complex illumination effects.

Providing realistic, high-resolution and high-fidelity representation of motions ia essential in the cloth simulation problem. In order to make high resolution simulations tractable, several algorithms have been developed that manage cloth-object interactions efficiently through specialized data

We propose a Parallel Banding Algorithm (PBA) on the GPU to compute the exact Euclidean Distance Transform (EDT) for a binary image in 2D and higher dimensions. Partitioning the image into small bands to process and then merging them concurrently, PBA computes the exact EDT with optimal linear

the same numerical results. In the experiments on large scale datasets, it is shown that, by using a parallel implementation on the Geforce GTX260 GPU, classifying unseen images is 4.8 times faster than a quad-core CPU version on the Core i7 920, while giving the exact same numerical results. In addition

be visualized interactively as a falsecolor image of the directions by showing (X,Y,Z) lighting direction vector components as (R,G,B) colors (see Fig. 1, c). Users can add multiple, indpendently constrained lights to create more complicated lighting configurations. Figure 2: Illumination data from 480 light

method that exploits the computational power of a GPU cluster to solve this task interactively. We use object space decomposition of the 3D volume. A single CPU/GPU node solves the Navier-Stokes equations only for its subvolume, taking into account the data from neighboring nodes as boundary conditions