The Pan-American Advanced Studies Institute (PASI)—”Scientific Computing in the Americas: the challenge of massive parallelism”—was held in Valparaiso, Chile on 3–14 January 2011. The event hosted 14 lecturers and 68 participants, thanks to NSF/DOE funding. Lecture materials are now available publicly: PDFs of the lecture slides on the PASI website, and screencasts (video) via an iTunes U course and on YouTube also).
Exploitation of novel computer architectures, such as general purpose GPUs, is allowing researchers to accelerate the realization of frontier models in particle-based simulation, by enabling an increase in the level of realism in the description of the particles and their interactions and increasing both the number of particles and the timescales simulated.
This one-day meeting focuses on the new and exciting area of the exploitation of GPUs and related technology in the area of biomolecular simulations.
In addition to a programme of national and international speakers in the field, there is the opportunity to present a poster on your research. Read the rest of this entry »
The Parallel Processing for Imaging Applications conference, part of IS&T/SPIE’s Electronic Imaging conference, was held on January 24–25 in San Francisco. The conference had a large number of GPU papers (SPIE digital library link):
- Using a commercial graphical processing unit and the CUDA programming language to accelerate scientific image processing applications by Broussard and Ives
- GPGPU real-time texture analysis framework by Akhloufi et al.
- A parallel implementation of 3D Zernike moment analysis by Berjón et al.
- Visualization assisted by parallel processing by Lange et al.
- GPU color space conversion by Chase and Vondran
- Acceleration of the Retinex algorithm for image restoration by GPGPU/CUDA by Wang and Huang
- Video transcoding using GPU accelerated decoder by Hsu
- Real-time image deconvolution on the GPU by Klosowski and Krishnan
- GPU-completeness: theory and implications by Lin
- A parallel error diffusion implementation on a GPU by Zhang et al.
- Evaluation of CPU and GPU architectures for spectral image analysis algorithms by Fresse et al.
- Real-time 3D flash ladar imaging through GPU data processing by Wong et al.
- Advanced MRI reconstruction toolbox with accelerating on GPU by Wu et al.
- Accelerating image recognition on mobile devices using GPGPU by López et al.
- A GPU accelerated PDF transparency engine by Recker et al.
SpeedIT Extreme 1.2 introduces support for complex numbers in single and double precision for all SpeedIT methods, such as fast sparse matrix vector multiplication, CG and BiCGSTAB solver.
This new report covers all the performance improvements in the latest CUDA Toolkit 3.2 release, and compares CUDA parallel math library performance vs. commonly used CPU libraries.
Learn about the performance advantages of using the CUDA parallel math libraries for FFT, BLAS, sparse matrix operations, and random number generation.
We implemented a GPU based parallel code to perform Monte Carlo simulations of the two dimensional q-state Potts model. The algorithm is based on a checkerboard update scheme and assigns independent random number generators to each thread (one thread per spin). The implementation allows to simulate systems up to ~10^9 spins with an average time per spin flip of 0.147ns on the fastest GPU card tested, representing a speedup up to 155x, compared with an optimized serial code running on a standard CPU. The possibility of performing high speed simulations at large enough system sizes allowed us to provide a positive numerical evidence about the existence of metastability on very large systems based on Binder’s criterion, namely, on the existence or not of specific heat singularities at spinodal temperatures different of the transition one.
(Ezequiel E. Ferrero, Juan Pablo De Francesco, Nicolás Wolovick and Sergio A. Cannas: “q-state Potts model metastability study using optimized GPU-based Monte Carlo algorithms”. [arXiv:1101.0876] [code and additional information])
This meeting is organized by Toby Breckon & Stuart Barnes (Cranfield University) and the British Machine Vision Association and Society for Pattern Recognition. It will be held in London, UK, on 18 May 2011. The CfP poster is available at http://www.cranfield.ac.uk/~toby.breckon/events/bmva_symp_gpu11.pdf.
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Tina’s Random Number Generator Library (TRNG) version 4.11 has been released. TRNG is a state of the art open-source C++ pseudo-random number generator library for sequential and parallel Monte Carlo simulations. Its design principles are based on a proposal for an extensible random number generator facility that will be part of the forthcoming revision of the ISO C++ standard. The TRNG library features an object oriented design, is easy to use and has been speed optimized. Its implementation does not depend on any communication library or hardware architecture. TRNG is suited for shared memory as well as for distributed memory computers and may be used in various parallel programming environments, e.g. Message Passing Interface Standard or OpenMP. As an outstanding new feature of the latest TRNG release 4.11 it also supports CUDA. All generators that are implemented by TRNG have been subjected to thorough statistical tests in sequential and parallel setups. Download and further information: http://trng.berlios.de/
The fourth International workshop and tutorial on Computational Intelligence on Consumer Games and Graphics Hardware (CIGPU 2011) will be held as a workshop in the GECCO-2011 conference in Dublin 12-16 July 2011. Submissions are invited in (but not limited to) the following areas:
- Parallel genetic programming (GP) on GPU
- Parallel genetic algorithms (GA) on GPU
- Parallel evolutionary programming (EP) on GPU
- Associated or hybrid computational intelligence techniques on GPU
- Particle Swarm Optimisation (PSO)
- Ant colony
- Parallel search algorithms
- Data mining
- Differential Evolution on GPU
- Computational Biology or Bioinformatics on GPU
- Evolutionary computation on video game platforms
- Evolutionary computation on mobile devices
See: http://www.sigevo.org/gecco-2011/workshops.html#cigpu and http://www.cs.ucl.ac.uk/staff/W.Langdon/cigpu/ for more information.
Although trivial background subtraction (BGS) algorithms (e.g. frame differencing, running average…) can perform quite fast, they are not robust enough to be used in various computer vision problems. Some complex algorithms usually give better results, but are too slow to be applied to real-time systems. We propose an improved version of the Extended Gaussian mixture model that utilizes the computational power of Graphics Processing Units (GPUs) to achieve real-time performance. Experiments show that our implementation running on a low-end GeForce 9600GT GPU provides at least 10x speedup. The frame rate is greater than 50 frames per second (fps) for most of the tests, even on HD video formats.
(Vu Pham, Phong Vo, Vu Thanh Hung and Le Hoai Bac: “GPU Implementation of Extended Gaussian Mixture Model for Background Subtraction”. IEEE International Conference on Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2010. [DOI] [code and additional information])