September 20th, 2012
September 4th, 2012
The Vrije Universiteit Brussel, Erasmus Hogeschool Brussel and Lessius Hogeschool have the pleasure to invite you to a symposium on Personal High-Performance Computing. The symposium aims at bringing together academia and industry to discuss recent advances in using accelerators such as GPUs or FPGAs for speeding up computational-intensive applications. We target single systems such as PCs, laptops or processor boards, hence the name ‘personal’ HPC.
Scientists are encouraged to submit abstracts to be presented at the poster session. All information can be found at https://sites.google.com/site/phpc2012bxl.
August 20th, 2012
In this work, we evaluate OpenCL as aprogramming tool for developing performance-portable applications for GPGPU. While the Khronos group developed OpenCL with programming portability in mind, performance is not necessarily portable. OpenCL has required performance-impacting initializations that do not exist in other languages such as CUDA. Understanding these implications allows us to provide a single library with decent performance on a variety of platforms. We choose triangular solver (TRSM) and matrix multiplication (GEMM) as representative level 3 BLAS routines to implement in OpenCL. We profile TRSM to get the time distribution of the OpenCL runtime system. We then provide tuned GEMM kernels for both the NVIDIA Tesla C2050 and ATI Radeon 5870, the latest GPUs offered by both companies. We explore the benefits of using the texture cache, the performance ramifications of copying data into images, discrepancies in the OpenCL and CUDA compilers’ optimizations, and other issues that affect the performance. Experimental results show that nearly 50% of peak performance can be obtained in GEMM on both GPUs in OpenCL. We also show that the performance of these kernels is not highly portable. Finally, we propose the use of auto-tuning to better explore these kernels’ parameter space using search harness.
(Peng Du, Rick Weber, Piotr Luszczek, Stanimire Tomov, Gregory Peterson, Jack Dongarra, “From CUDA to OpenCL: Towards a performance-portable solution for multi-platform GPU programming”, Parallel Computing 38(8):391–407, Aug. 2012. [DOI] [early techreport])
August 11th, 2012
The Computing Language Utility (CLU) is a lightweight API designed to help programmers explore, learn, and rapidly prototype programs with OpenCL. This API reduces the complexity associated with initializing OpenCL devices, contexts, kernels and parameters, etc. while preserving the ability to drop down to the lower level OpenCL API at will when programmers wants to get their hands dirty. The CLU release includes an open source implementation along with documentation and samples that demonstrate how to use CLU in real applications. It has been tested on Windows 7 with Visual Studio.
August 10th, 2012
This workshop is concerned with the comparison of high-performance computing systems through performance modeling, benchmarking or the use of tools such as simulators. We are particularly interested in research which reports the ability to measure and make tradeoffs in software/hardware co-design to improve sustained application performance. We are also keen to capture the assessment of future systems, for example through work that ensures continued application scalability through peta- and exa-scale systems.
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August 9th, 2012
The MOSIX group announces the release of the Virtual OpenCL (VCL) cluster platform version 1.14. This version includes the SuperCL extension that allows micro OpenCL programs to run efficiently on devices of remote nodes. VCL provides an OpenCL platform in which all the cluster devices are seen as if they are located in the hosting-node. This platform benefits OpenCL applications that can use many devices concurrently. Applications written for VCL benefit from the reduced programming complexity of a single computer, the availability of shared-memory, multi-threads and lower granularity parallelism, as well as concurrent access to devices in many nodes. With SuperCL, a programmable sequence of kernels and/or memory operations can be sent to remote devices in cluster nodes, usually with just a single network round-trip. SuperCL also offers asynchronous communication with the host, to avoid the round-trip waiting time, as well as direct access to distributed file-systems. The VCL package can be downloaded from mosix.org.
August 9th, 2012
Graphics Core Next Architecture Overview
GCN is Designed to push not only the boundaries of DirectX® 11 gaming, the GCN Architecture is also AMD’s first design specifically engineered for general computing. Equipped with up to 32 compute units (2048 stream processors), each containing a scalar coprocessor, AMD’s 28nm GPUs are more than capable of handling workloads-and programming languages-traditionally exclusive to the processor. Coupled with the dramatic rise of GPU-aware programming languages like C++ AMP and OpenCL™, the GCN Architecture is truly the right architecture for the right time. Participate in this webinar to learn how you can take advantage of this new architecture in your GPGPU programs (North America – August 14, 2012 10AM Pacific Daylight savings Time; India- August 21, 2012, 5:30PM India Standard Time).
Performance Evaluation of AMD APARAPI Using Real World Applications
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August 6th, 2012
The fall schedule for Acceleware’s training courses is now available.
- OpenCL: August 21-24, 2012, Houston, TX
- CUDA: October 2-5, 2012, San Jose, CA
- OpenCL: October 16-19, 2012, Calgary, AB
- CUDA: November 6-9, 2012, Houston, TX
- CUDA: December 4-7, 2012, New York, NY – Finance Focus
- AMP: December 11-14, 2012, Chicago, IL
More information: http://www.acceleware.com/training
August 1st, 2012
The 2012 International Workshop on GPU Computing in Clouds (GPU-Cloud 2012) will he held December 03-06 2012 in Taipei, Taiwan, in conjunction with the 4th International Conference on Cloud Computing Technology and Science. Important Dates:
- Submission Deadline: August 17, 2012
- Authors Notification: September 11, 2012
- Final Manuscript Due: September 28, 2012
- Workshop: December 04, 2012
Submission site: http://www.easychair.org/conferences/?conf=gpucloud2012
August 1st, 2012
We present an efficient algorithm for computation of surface representations enabling interactive visualization of large dynamic particle data sets. Our method is based on a GPU-accelerated data-parallel algorithm for computing a volumetric density map from Gaussian weighted particles. The algorithm extracts an isovalue surface from the computed density map, using fast GPU-accelerated Marching Cubes. This approach enables interactive frame rates for molecular dynamics simulations consisting of millions of atoms. The user can interactively adjust the display of structural detail on a continuous scale, ranging from atomic detail for in-depth analysis, to reduced detail visual representations suitable for viewing the overall architecture of molecular complexes. The extracted surface is useful for interactive visualization, and provides a basis for structure analysis methods.
(Michael Krone, John E. Stone, Thomas Ertl, and Klaus Schulten, “Fast visualization of Gaussian density surfaces for molecular dynamics and particle system trajectories”, In EuroVis – Short Papers 2012, pp. 67-71, 2012. [WWW])
Parameter estimation (PE) of biological systems is one of the most challenging problems in Systems Biology. Here we present a PE method that integrates particle swarm optimization (PSO) to estimate the value of kinetic constants, and a stochastic simulation algorithm to reconstruct the dynamics of the system. The fitness of candidate solutions, corresponding to vectors of reaction constants, is defined as the point-to-point distance between a simulated dynamics and a set of experimental measures, carried out using discrete-time sampling and various initial conditions. A multi-swarm PSO topology with different modalities of particles migration is used to account for the different laboratory conditions in which the experimental data are usually sampled. The whole method has been specifically designed and entirely executed on the GPU to provide a reduction of computational costs. We show the effectiveness of our method and discuss its performances on an enzymatic kinetics and a prokaryotic gene expression network.
(M. Nobile, D. Besozzi, P. Cazzaniga, G. Mauri and D. Pescini: “A GPU-based multi-swarm PSO method for parameter estimation in stochastic biological systems exploiting discrete-time target series”, in M. Giacobini, L. Vanneschi, W. Bush, editors, Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, Springer, vol. 7246 of LNCS. pp. 74-85, 2012. [DOI])