Basic uniform pseudo-random number generators are implemented on ATI Graphics Processing Units (GPU). The performance results of the realized generators (multiplicative linear congruential (GGL), XOR-shift (XOR128), RANECU, RANMAR, RANLUX and Mersenne Twister (MT19937)) on CPU and GPU are discussed. The obtained speed-up factor is hundreds of times in comparison with CPU. RANLUX generator is found to be the most appropriate for using on GPU in Monte Carlo simulations. The brief review of the pseudo-random number generators used in modern software packages for Monte Carlo simulations in high-energy physics is present.

(Vadim Demchik, “Pseudo-random number generators for Monte Carlo simulations on Graphics Processing Units”, Mar. 2010, arXiv:1003.1898 [hep-lat])

The conference will take place October 28-30, 2010, on Djerba Island, Tunisia. More information is available at the conference web page.

From the YDEL for CUDA website:

Key benefits of Yellow Dog Enterprise Linux for CUDA:

• YDEL for CUDA users can experience up to a 9% performance improvement in some applications.
• Comprehensive support is offered to paid subscriptions with our skilled team able to assist you with both Linux and CUDA.
• YDEL’s unparalleled integrations means everything you need to write and run CUDA applications is included and configured.
• YDEL includes multiple versions of CUDA and can easily switch between them via a setting in a configuration file or an environment variable.
• Never worry about updates affecting your system, Fixstars offers YDEL users greater reliability with our strenuous test procedures that validate GPU computing functionality and performance.

For more information, visit the YDEL for CUDA website.

CLyther exposes both the OpenCL C library and language to python. It’s features include:

• Fast prototyping of OpenCL code.
• OpenCL kernel function creation using the Python language definition.
• Strong OOP programming in OpenCL code.
• Passing functions as arguments to kernel functions.
• Python emulation mode for OpenCL code.
• Fancy indexing of arrays.
• Dynamic compilation at runtime.

CLyther is currently under development, and the developers plan to release a beta version before April 14. They are looking for feedback and developers to help with this project. If you are interested, post a comment to the CLyther forum.

The workshop PASCO 2010 will be a three-day event including invited presentations and tutorials, contributed research papers and posters, and a programming contest. Specific topics include, but are not limited to:

• Design and analysis of parallel algorithms for computer algebra
• Practical parallel implementation of symbolic or symbolic-numeric algorithms
• High-performance software tools and libraries for computer algebra
• Applications of high-performance computer algebra
• Distributed data-structures for computer algebra
• Hardware acceleration technologies (multi-cores, GPUs, FPGAs) applied to computer algebra
• Cache complexity and cache-oblivious algorithms for computer algebra
• Compile-time and run-time techniques for automating optimization and platform adaptation of computer algebra algorithms

The conference invites submission of papers presenting original research, either in the form of extended abstracts (2 pages) or full papers (up to 10 pages) in ACM format. As in previous years, PASCO 2010 will publish formal proceedings of the accepted papers. Please refer to the conference web site for details, important dates, and the programming contest.

• Evaluating OpenCL performance of an existing CUDA code
• Maintaining a dual-target OpenCL and CUDA code
• Reducing dependence on NVCC when compiling host code
• Support multiple CUDA compute capabilities in a single binary

Swan is developed by the MultiscaleLab, Barcelona, and is available under the GPL2 license.

We have previously suggested mixed precision iterative solvers specifically tailored to the iterative solution of sparse linear equation systems as they typically arise in the finite element discretization of partial differential equations. These schemes have been evaluated for a number of hardware platforms, in particular single precision GPUs as accelerators to the general purpose CPU. This paper reevaluates the situation with new mixed precision solvers that run entirely on the GPU: We demonstrate that mixed precision schemes constitute a significant performance gain over native double precision. Moreover, we present a new implementation of cyclic reduction for the parallel solution of tridiagonal systems and employ this scheme as a line relaxation smoother in our GPU-based multigrid solver. With an alternating direction implicit variant of this advanced smoother we can extend the applicability of the GPU multigrid solvers to very ill-conditioned systems arising from the discretization on anisotropic meshes, that previously had to be solved on the CPU. The resulting mixed precision schemes are always faster than double precision alone, and outperform tuned CPU solvers consistently by almost an order of magnitude.

(Dominik Göddeke and Robert Strzodka: “Cyclic Reduction Tridiagonal Solvers on GPUs Applied to Mixed Precision Multigrid” , accepted in: IEEE Transactions on Parallel and Distributed Systems, Special Issue: High Performance Computing with Accelerators, Mar. 2010. Link.)

A paper describing the Asymmetric Distributed Shared Memory model and its implementation in GMAC has been accepted in the ASPLOS XV conference. GMAC is being developed by the Operating System Group at the Universitat Politecnica de Catalunya and the IMPACT Research Group at the University of Illinois. Binary pre-compiled packages, the source code, documentation and examples are available at the project website.

(Isaac Gelado, Javier Cabezas, John Stone, Sanjay Patel, Nacho Navarro and Wen-mei Hwu,  “An Asymmetric Distributed Shared Memory Model for Heterogeneous Parallel Systems”, accepted in: Fifteenth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2010), March 2010.)

We present an efficient method for the simulation of laminar fluid flows with free surfaces including their interaction with moving rigid bodies, based on the two-dimensional shallow water equations and the Lattice-Boltzmann method. Our implementation targets multiple fundamentally different architectures such as commodity multicore CPUs with SSE, GPUs, the Cell BE and clusters. We show that our code scales well on an MPI-based cluster; that an eightfold speedup can be achieved using modern GPUs in contrast to multithreaded CPU code and, finally, that it is possible to solve fluid-structure interaction scenarios with high resolution at interactive rates.

(Markus Geveler, Dirk Ribbrock, Dominik Göddeke and Stefan Turek: “Lattice-Boltzmann Simulation of the Shallow-Water Equations with Fluid-Structure Interaction on Multi- and Manycore Processors”, Accepted in: Facing the Multicore Challenge, Heidelberg, Germany, Mar. 2010. Link.)