High Performance Graphics is the leading international forum for performance-oriented graphics systems research including innovative algorithms, efficient implementations, and hardware architecture. The conference brings together researchers, engineers, and architects to discuss the complex interactions of massively parallel hardware, novel programming models, efficient graphics algorithms, and novel applications. High Performance Graphics was founded in 2009 to synthesize and broaden on two important and well-respected conferences in computer graphics: Graphics Hardware and Interactive Ray Tracing.

HPG 2012 is co-sponsored by Eurographics and ACM SIGGRAPH and will take place on June 25-27, is co-located with the Eurographics Symposium on Rendering in Paris, France. We invite original and innovative performance-oriented contributions from all areas of graphics, including hardware architectures, rendering, physics, animation, simulation, and data structures, with topics including (but not limited to): Interactive rendering pipelines (hardware or software); Interactive rendering algorithms (hardware or software); Graphics hardware and systems; Languages and compilation; Parallel computing for graphics; and Mobile graphics. Please see the conference website for the full CFP.

This workshop is organized by Horacio Pérez-Sánchez and José M. Cecilia and takes place in conjunction with the International Conference on Modeling & Applied Simulation (MAS 2012). The goal is to explore the use of emerging parallel computing architectures as well as High Performance Computing systems (Supercomputers, Clusters, Grids) for the simulation of relevant biological systems. We welcome papers, not submitted elsewhere for review, with a focus in topics of interest ranging from but not limited to:

• Parallel stochastic simulation
• Biological and Numerical parallel computing
• Parallel and distributed architectures
• Emerging processing architectures (e.g. GPUs, FPGAs, mixed CPU-GPU or CPU-FPGA)
• Parallel Model checking techniques.
• Parallel algorithms for biological analysis.
• Cluster and Grid Deployment for system biology
• Tools and applications
• Biologically inspired algorithms.

More details, including dates, deadlines and submission instructions, are available on the workshop web page.

Version 1.2.0 of the OpenCL-based C++ linear algebra library ViennaCL is now available for download! It features a high-level interface compatible with Boost.ublas, which allows for compact code and high productivity. Highlights of the new release are the following features (all experimental):

• Several algebraic multigrid preconditioners
• Sparse approximate inverse preconditioners
• Fast Fourier transform
• Structured dense matrices (circulant, Hankel, Toeplitz, Vandermonde)
• Reordering algorithms (Cuthill-McKee, Gibbs-Poole-Stockmeyer)
• Proxies for manipulating subvectors and submatrices

The features are expected to reach maturity in the 1.2.x branch. More information about the library including download links is available at http://viennacl.sourceforge.net.

FortranCL is an interface to OpenCL from Fortran90 programs, and it is distributed under the LGPL free software license. It allows Fortran programmer to directly execute code on GPUs or other massively parallel processors. The interface is designed to be as close to the C OpenCL interface as possible, and it is written in native Fortran 90 with type checking. FortranCL is not complete yet, but it includes enough subroutines to write GPU accelerated code in Fortran. More information: http://code.google.com/p/fortrancl/

HOOMD-blue performs general-purpose particle dynamics simulations on a single workstation, taking advantage of NVIDIA GPUs to attain a level of performance equivalent to many cores on a fast cluster. Flexible and configurable, HOOMD-blue is currently being used for coarse-grained molecular dynamics simulations of nano-materials, glasses, and surfactants, dissipative particle dynamics simulations (DPD) of polymers, and crystallization of metals.

HOOMD-blue 0.10.0 adds many new features. Highlights include: Read the rest of this entry »

Abstract:

In this paper we investigate the use of distributed graphics processing unit (GPU)-based architectures to accelerate pipelined wavefront applications—a ubiquitous class of parallel algorithms used for the solution of a number of scientific and engineering applications. Specifically, we employ a recently developed port of the LU solver (from the NAS Parallel Benchmark suite) to investigate the performance of these algorithms on high-performance computing solutions from NVIDIA (Tesla C1060 and C2050) as well as on traditional clusters (AMD/InfiniBand and IBM BlueGene/P).

Benchmark results are presented for problem classes A to C and a recently developed performance model is used to provide projections for problem classes D and E, the latter of which represents a billion-cell problem. Our results demonstrate that while the theoretical performance of GPU solutions will far exceed those of many traditional technologies, the sustained application performance is currently comparable for scientific wavefront applications. Finally, a breakdown of the GPU solution is conducted, exposing PCIe overheads and decomposition constraints. A new k-blocking strategy is proposed to improve the future performance of this class of algorithm on GPU-based architectures.

(Pennycook, S.J., Hammond, S.D., Mudalige, G.R., Wright, S.A. and Jarvis, S.A.: “On the Acceleration of Wavefront Applications using Distributed Many-Core Architectures”,  The Computer Journal (in press) [DOI] [PREPRINT])

A major new release of the Intel SPMD Program Compiler (ispc) was posted on December 5, 2011. ispc is an extended version of the C programming language with support for “single program, multiple data” (SPMD) programming on the CPU; the SPMD model makes it easy to harness the full power of both the SIMD vector units and multiple cores on modern CPUs. The major features added in the 1.1 release include:

• Full support for pointers, including pointer arithmetic, function pointers, and all other features of pointers in C.
• A new parallel “foreach” statement, for more easily mapping computation to data.
• Substantially revised documentation, including a new Performance Guide.
• Many other small bug fixes and improvements.

ispc is open-source and is licensed under the BSD license. Source and binaries are available from http://ispc.github.com.

Since the last WCCM (Sydney 2009), where we organized a similarly themed minisymposium, the scientific and engineering communities have gained much experience in using GPU hardware for their applications. The number of publications addressing GPU applications has skyrocketed, while researchers have developed much common understanding of how to implement numerical methods in this architecture. Moreover, we now find that three of the five fastest computers in the world, as measured for the Top500 list, are GPU-based systems. There is much conversation about GPUs playing a leading role in the exascale computing world. In summary, this topic is of wide interest; frankly, it is all the rage. This minisymposium will concentrate presentations from the top researchers in the world using GPU hardware for applications in all branches of computational mechanics. We encourage contributions that address innovative methods to use GPUs efficiently, studies in numerical methods as they apply to adapting to the hardware and perspectives on the future of GPUs as we advance toward exascale.

WCCM will be held at São Paolo, Brazil, 8–13 July 2012.  The abstract submission deadline is  December 31, 2011. More information: http://www.wccm2012.com, http://barbagroup.bu.edu/Barba_group/Events.html.

The NVIDIA CUDA Toolkit 4.1 RC2 is now available for anyone to download. The key features of this release are:

• A new LLVM based compiler
• Over 1000 additional image processing function in the NPP library
• A Visual profiler

There is also a new version of Parallel Nsight 2.1 RC2 with support for CUDA 4.1. To download and to find out more follow: http://bit.ly/sRpQvr

Libra SDK is a sophisticated runtime including API, sample programs and documentation for massively accelerating software computations. This introduction tutorial provides an overview and usage examples of the powerful Libra API & math libraries executing on x86/x64, OpenCL, OpenGL and CUDA technology. Libra API enables generic and portable CPU/GPU computing within software development without the need to create multiple, specific and optimized code paths to support x86, OpenCL, OpenGL or CUDA devices. Link to PDF: www.gpusystems.com/doc/LibraGenericComputing.pdf