## Posts

Oct, 28

### Parallel Algorithm for GPU Processing; for use in High Speed Machine Vision Sensing of Cotton Lint Trash

One of the main hurdles standing in the way of optimal cleaning of cotton lint isthe lack of sensing systems that can react fast enough to provide the control system withreal-time information as to the level of trash contamination of the cotton lint. This researchexamines the use of programmable graphic processing units (GPU) as an […]

Oct, 28

### GPU support for batch oriented workloads

This paper explores the ability to use graphics processing units (GPUs) as co-processors to harness the inherent parallelism of batch operations in systems that require high performance. To this end we have chosen bloom filters (space-efficient data structures that support the probabilistic representation of set membership) as the queries these data structures support are often […]

Oct, 28

### The gputools package enables GPU computing in R

Motivation: By default, the R statistical environment does not make use of parallelism. Researchers may resort to expensive solutions such as cluster hardware for large analysis tasks. Graphics processing units (GPUs) provide an inexpensive and computationally powerful alternative. Using R and the CUDA toolkit from Nvidia, we have implemented several functions commonly used in microarray […]

Oct, 28

### Accelerating Large Graph Algorithms on the GPU Using CUDA

Large graphs involving millions of vertices are common in many practical applications and are challenging to process. Practical-time implementations using high-end computers are reported but are accessible only to a few. Graphics Processing Units (GPUs) of today have high computation power and low price. They have a restrictive programming model and are tricky to use. […]

Oct, 28

### Implementing Decision Trees and Forests on a GPU

We describe a method for implementing the evaluation and training of decision trees and forests entirely on a GPU, and show how this method can be used in the context of object recognition. Our strategy for evaluation involves mapping the data structure describing a decision forest to a 2D texture array. We navigate through the […]

Oct, 28

### Adaptive Mesh Fluid Simulations on GPU

We describe an implementation of compressible inviscid fluid solvers with block-structured adaptive mesh refinement on Graphics Processing Units using NVIDIA’s CUDA. We show that a class of high resolution shock capturing schemes can be mapped naturally on this architecture. Using the method of lines approach with the second order total variation diminishing Runge-Kutta time integration […]

Oct, 28

### Stochastic DT-MRI Connectivity Mapping on the GPU

We present a method for stochastic fiber tract mapping from diffusion tensor MRI (DT-MRI) implemented on graphics hardware. From the simulated fibers we compute a connectivity map that gives an indication of the probability that two points in the dataset are connected by a neuronal fiber path. A Bayesian formulation of the fiber model is […]

Oct, 28

### GPU virtualization on VMware’s hosted I/O architecture

Modern graphics co-processors (GPUs) can produce high fidelity images several orders of magnitude faster than general purpose CPUs, and this performance expectation is rapidly becoming ubiquitous in personal computers. Despite this, GPU virtualization is a nascent field of research. This paper introduces a taxonomy of strategies for GPU virtualization and describes in detail the specific […]

Oct, 28

### GPU accelerated image aligned splatting

Splatting is a popular technique for volume rendering, where voxels are represented by Gaussian kernels, whose pre-integrated footprints are accumulated to form the image. Splatting has been mainly used to render pre-shaded volumes, which can result in significant blurring in zoomed views. This can be avoided in the image-aligned splatting scheme, where one accumulates kernel […]

Oct, 28

### GPU-accelerated large-scale quantum molecular dynamics simulation of 3-dimensional C60 polymers

Polymerization of C60 molecular crystal under high pressure and high temperature is simulated by using linear scaling tight binding molecular dynamics (TBMD) with Graphic Processing Unit (GPU) as a computational accelerator for matrix-matrix multiplication. Two sets of tight binding parameters were tested.

Oct, 28

### An Implementation of the Smooth Particle Mesh Ewald Method on GPU Hardware

The smooth particle mesh Ewald summation method is widely used to efficiently compute long-range electrostatic force terms in molecular dynamics simulations, and there has been considerable work in developing optimized implementations for a variety of parallel computer architectures. We describe an implementation for Nvidia graphical processing units (GPUs) which are general purpose computing devices with […]

Oct, 28

### Fuzzy ART Neural Network Parallel Computing on the GPU

Graphics Processing Units (GPUs) have evolved into powerful programmable processors, faster than Central Processing Units (CPUs) regarding the execution of parallel algorithms. In this paper, an implementation of a Fuzzy ART Neural Network on the GPU is presented. Experimental results show training process is slower on the GPU than on a dual-core Pentium 4 at […]