Savant is a asymptotic ray-tracing CEM tool used to predict the performance of antennas installed on electrically large platforms, including far-field antenna patterns, near-field distributions, and antenna-to-antenna coupling. Savant is based on the shooting and bouncing rays (SBR) formulation. While asymptotic solvers like Savant have significantly smaller computational and memory requirements for electrically large problems than full-wave techniques, the computation costs still increase significantly with frequency and simulation fidelity, and such solvers benefit greatly from parallelization techniques. Graphics processing units (GPUs) are throughput-oriented processing devices that are well suited for the mathematically intensive workloads found in CEM solvers. Current GPUs contain hundreds of processing units, leverage thousands of threads, and can execute over one trillion floating-point operations per second. A hybrid CPU and GPU parallelization approach has been developed for Savant, providing significant speedups compared to CPU-only implementations. Results from the execution of GPU-accelerated Savant on multiple case studies will be presented.

(T. Courtney, J. E. Stone and R. Kipp, “Using GPUs to Accelerate installed antenna performance simulations,” Proc. Allerton Antenna Symposium, Sept. 2011, Monticello, IL. [PDF])

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.

• 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.

HOOMD-blue 0.10.0 adds many new features. Highlights include:

• Added pair.dpdlj which uses the DPD thermostat and the Lennard-Jones potential. In previous versions, this could be accomplished by using two pair commands but at the cost of reduced performance.
• Additional example scripts are now present in the documentation. The example scripts are cross-linked to the commands that are used in them.
• Most dump commands now accept the form: dump.ext(filename=”filename.ext”) which immediately writes out filename.ext.
• Specify rigid bodies in XML input files
• Simulations that contain rigid body constraints applied to groups of particles in BDNVT, NVE, NVT, and NPT ensembles.
• Energy minimization of rigid bodies ( integrate.mode_minimize_rigid_fire )
• Existing commands are now rigid-body aware
• NVT integration using the Berendsen thermostat ( integrate.berendsen )
• Bonds, angles, dihedrals, and impropers can now be created and deleted with the python data access API.
• and more

HOOMD-blue 0.10.0 is available for download under an open source license. Check out the quick start tutorial to get started, or check out the full documentation to see everything it can do.

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])

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 computing landscape has undergone significant transformation with the emergence of more powerful processing elements such as GPUs, FPGAs, multi-cores, etc. On the multi-core front, Moore’s Law has transcended beyond the single processor boundary with the prediction that the number of cores will double every 18 months. Going forward, the primary method of gaining processor performance will be through parallelism. Multi-core technology has visibly penetrated the global market. Accordingly to the latest Top500 lists the HPC landscape has evolved from supercomputer systems into large clusters of dual or quad-core processors. Furthermore, GPUs, FPGAs and multi-cores have been shown to be formidable computing alternatives, where certain classes of applications witness more than one order of magnitude improvement over their GPP counterpart. Therefore, future computational science centers will employ resources such as FPGA and GPU architectures to serve as co-processors to offload appropriate compute-intensive portions of applications from the servers.

This workshop provides a forum for exploring the capabilities of emerging parallel architectures to accelerate computational science applications. Papers are being sought on a wide variety of topics related to the field of using emerging parallel architectures for computational science including but not limited to:

• Application studies on emerging architectures such as GPUs, FPGAs and Intel MIC
• Parallel algorithms and methodologies on emerging architectures
• Languages, models, tools, and compilation techniques for emerging architectures
• Hybrid computer systems consisting of a combination of GPUs, FPGAs, etc.
• Use of emerging architectures in clusters, grids and supercomputers

More information, including submission deadlines and instructions, are available at http://www.staff.uni-mainz.de/schmi033.