12387
Maria G. Sanchez, Vicente Vidal, Josep Arnal, Anna Vidal
A parallel algorithm to remove impulsive noise in digital images using heterogeneous CPU/GPU computing is proposed. The parallel denoising algorithm is based on the peer group concept and uses an Euclidean metric. In order to identify the amount of pixels to be allocated in multi-core and GPUs, a performance analysis using large images is presented. […]
View View   Download Download (PDF)   
M.G. Sanchez, V. Vidal, J. Bataller, J. Arnal
A parallel algorithm for image noise removal is proposed. The algorithm is based on peer group concept and uses a fuzzy metric. An optimization study on the use of the CUDA platform to remove impulsive noise using this algorithm is presented. Moreover, an implementation of the algorithm on multi-core platforms using OpenMP is presented. Performance […]
View View   Download Download (PDF)   
M. G. Sanchez, V. Vidal, J. Bataller, G. Verdu
In this paper, we present an efficient implementation of parallel algorithms to remove noise in digital images using different Graphics Processing Units (GPUs). The algorithm, based on the concept of peer group, uses a fuzzy metric for finding wrong pixels and the Arithmetic Mean Filter (AMF) to correct it. There are many factors to study […]
View View   Download Download (PDF)   
Mutsuo Saito, Makoto Matsumoto
This paper proposes a type of pseudorandom number generator, Mersenne Twister for Graphic Processor (MTGP), for efficient generation on graphic processessing units (GPUs). MTGP supports large state sizes such as 11213 bits, and uses the high parallelism of GPUs in computing many steps of the recursion in parallel. The second proposal is a parameter-set generator […]
P. H. Hauschildt, E. Baron
AIMS: We discuss an implementation of our 3D radiative transfer (3DRT) framework with the OpenCL paradigm for general GPU computing. METHODS: We implemented the kernel for solving the 3DRT problem in Cartesian coordinates with periodic boundary conditions in the horizontal (x,y) plane, including the construction of the nearest neighbor ^* and the operator splitting step. […]
View View   Download Download (PDF)   
Mutsuo Saito
The author proposes pseudorandom number generators suitable to execute on a graphic processor. They generate pseudorandom numbers in device memory on graphic processors. Each generator uses shared memory on graphic processors as its internal state space, and uses constant memory as a look-up table for a linear transformation. Output formats of the generator are 32-bit […]
Peter H. Hauschildt, E. Baron
We discuss an implementation of our 3D radiative transfer (3DRT) framework with the OpenCL paradigm for general GPU computing. We implement the kernel for solving the 3DRT problem in Cartesian coordinates with periodic boundary conditions in the horizontal $(x,y)$ plane, including the construction of the nearest neighbor $Lstar$ and the operator splitting step. We present […]
View View   Download Download (PDF)   
J. L. Herraiz, S. Espana, S. Garcia, R. Cabido, A. S. Montemayor, M. Desco, J. J. Vaquero, J. M. Udias
A CUDA implementation of the existing software FIRST (Fast Iterative Reconstruction Software for (PET) Tomography) is presented. This implementation uses consumer graphics processing units (GPUs) to accelerate the compute-intensive parts of the reconstruction: forward and backward projection. FIRST was originally developed in FORTRAN, and it has been migrated to C language to be used with […]
View View   Download Download (PDF)   
Geoffrey Blake, Ronald G. Dreslinski, Trevor Mudge, Krisztian Flautner
As the effective limits of frequency and instruction level parallelism have been reached, the strategy of microprocessor vendors has changed to increase the number of processing cores on a single chip each generation. The implicit expectation is that software developers will write their applications with concurrency in mind to take advantage of this sudden change […]
View View   Download Download (PDF)   

* * *

* * *

Like us on Facebook

HGPU group

184 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1314 peoples are following HGPU @twitter

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: AMD APP SDK 2.9
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 6.0.1, AMD APP SDK 2.9

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2014 hgpu.org

All rights belong to the respective authors

Contact us: