Category: Computer Science
CUDA 5 Preview Release Now Available
The CUDA 5 Preview release is now available to you via the CUDA Registered Developer program. CUDA 5 introduces several new tools and features that make it easier than ever to add GPU acceleration to your applications.
Realtime Computer Vision with OpenCV
OpenCV have made it easier for application developers to use computer vision. They are well-documented and vibrant open source projects that keep growing, and they are being adapted to new computing technologies
GPU Acceleration of Functional Neuroimaging
GPUs accelerate functional neuroimaging, by using three GPUs, 850 TB of data can be analyzed in 10 days, compared to 100 years with conventional software!
A Parallel Front Propagation Method: Simulating geological folds on parallel architectures
In this thesis, a novel three-dimensional anisotropic front propagation algorithm for simulation of geological folds on parallel architecture is presented. The algorithm’s abundant parallelism is demonstrated on multi-core CPUs and GPU architectures.
Language identification using multi-core processors
We explore the application of GPUs to speech pattern processing, using language identification (LID) to demonstrate their benefits. Realization of the full potential of GPUs requires both effective coding of predetermined algorithms, and, if there is a choice, selection of the algorithm or technique for a specific function that is most able to exploit the GPU
A Fair Comparison of Modern CPUs and GPUs Running the Genetic Algorithm under the Knapsack Benchmark
We show the performance comparison supported by architecture characteristics narrowing the performance gain of GPUs
The Spy Element Method – A universal approach to complex computing on manycore processors
This work presents a general approach, coined “Spy Element Method” (SEM), for parallelising workloads to run on manycore processors and featuring dynamic dependencies between different data items, such as graph traversals, remeshing methods and particle simulations
Fast Parallel Markov Clustering in Bioinformatics Using Massively Parallel Computing on GPU with CUDA and ELLPACK-R Sparse Format
Thus, large-scale parallel computation on off-the-shelf desktop-machines, that were previously only possible on supercomputing architectures, can significantly change the way bioinformaticians and biologists deal with their data
Optimizing ion channel models using a parallel genetic algorithm on graphical processors
We substantially reduced computing time by solving the ODEs so as to massively reduce memory transfers to and from the GPU.
GPU implementations of a relaxation scheme for image partitioning: GLSL versus CUDA
We compare the performance of the implementations, discuss the implementation details, and show that suitability of this algorithm for GPU allows it to become a comparable alternative to the modern partitioning algorithm.
