Abstract:

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 3rd International Conference on Metaheuristics and Nature Inspired Computing, META’10, features a special session on Metaheuristics on graphics hardware, organized by Geir Hasle and Trond Runar Hagen. It focuses on the utilization of modern commodity computer architectures, in particular Graphics Processing Units (GPUs), to enhance the performance of metaheuristics. A broad spectrum of papers is invited, ranging from case studies focused on specific problems and applications to theoretical aspects and frameworks.

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

This symposium is organized by the University of Pittsburgh’s Center for Simulation & Modeling and Pittsburgh Supercomputing Center. The program includes a half-day hands-on tutorial, and several invited talks and presentations by experts from academia and industry. Registration is required. More information can be found at the symposium website.

Yellow Dog Enterprise Linux for CUDA (YDEL for CUDA) is an open source, Linux operating system built for faster, easier, and more reliable GPU Computing. YDEL for CUDA, released and supported by Fixstars, goes beyond the basic Linux OS and integrates support for GPUs, NVIDIA CUDA, and GPU development tools.

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 is an under-development python tool for OpenCL similar to Cython for C. CLyther is a python language extension intended to make writing OpenCL code as easy as Python itself. CLyther currently only supports a subset of the Python language definition but adds many new features for OpenCL.

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.

Read the rest of this entry »

The International Workshop on Parallel and Symbolic Computation (PASCO) is a series of workshops dedicated to the promotion and advancement of parallel algorithms and software in all areas of symbolic mathematical computation. The pervasive ubiquity of parallel architectures and memory hierarchy has led to the emergence of a new quest for parallel mathematical algorithms and software capable of exploiting the various levels of parallelism: from hardware acceleration technologies (multi-core and multi-processor system on chip, GPGPU, FPGA) to cluster and global computing platforms. To push up the limits of symbolic and algebraic computations, beyond the optimization of the application itself, the effective use of a large number of resources -memory and general or specialized computing units- is expected to enhance the performance multi-criteria objectives: time, energy consumption, resource usage, reliability. In this context, the design and the implementation of mathematical algorithms with provable and adaptive performances is a major challenge.

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: Read the rest of this entry »

Swan is a small tool that aids the reversible conversion of existing CUDA codebases to OpenCL. Its main features are the translation of CUDA kernel source-code to OpenCL, and a common API that abstracts both CUDA and OpenCL runtimes. Swan preserves the convenience of the CUDA <<< grid, block >>> kernel launch syntax by generating C source-code for kernel entry-point functions. Possible uses include:

• 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.

Abstract:

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.)

GMAC (Global Memory for ACcelerators) is a user-level library that implements an Asymmetric Distributed Shared Memory model to be used by CUDA programs. An ADSM model allows CPU code to access data hosted in accelerator (GPU) memory. In this model, a single pointer is used for data structures accessed both in the CPU and the GPU and the coherency of the data is transparently handled by the library. Moreover, the data allocated with GMAC can be accessed by all the host threads of the program. That makes your code simpler and cleaner. GMAC currently supports programs programmed with CUDA, but OpenCL support is planned.

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.)

Abstract:

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.)