The new Intel® SDK for OpenCL* Applications XE 2013 includes certified OpenCL 1.2 support for Intel® Xeon® processors and Intel® Xeon Phi™ coprocessors using Linux* operating systems. This SDK is targeted at developers of highly parallel applications including High Performance Compute (HPC), workstations, and data analytics, to name just a few. OpenCL broadens the parallel programming options on Intel® architecture and allows developers to maximize data parallel application performance on Intel Xeon Phi coprocessors.
The Intel SDK for OpenCL Applications XE 2013 provides developers OpenCL runtime and compiler, development tools, optimization guides, code samples, and training collaterals. More information: www.intel.com/software/opencl-xe

Abstract:

In this paper we evaluate the promise held by lowpower GPUs for non-graphic workloads that arise in embedded systems. Towards this, we map and implement 5 benchmarks, that find utility in very different application domains, to an embedded GPU. Our results show that apart from accelerated performance, embedded GPUs are promising also because of their energy efficiency which is an important design goal for battery-driven mobile devices. We show that adopting the same optimization strategies as those used for programming high-end GPUs might lead to worse performance on embedded GPUs. This is due to restricted features of embedded GPUs, such as, limited or no user-defined memory, small instruction-set, limited number of registers, among others. We propose techniques to overcome such challenges, e.g., by distributing the workload between GPUs and multi-core CPUs, similar to the spirit of heterogeneous computation.

(Arian Maghazeh, Unmesh D. Bordoloi, Petru Eles and Zebo Peng: “General Purpose Computing on Low-Power Embedded GPUs: Has It Come of Age?”, 13th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, Samos, Greece, July 15-18, 2013. [Preprint])

The 1st International Workshop on OpenCL (IWOCL) will be held on May 13th/14th at Georgia Institute of Technology Atlanta, Georgia. IWOCL is an annual meeting of vendors, researchers and developers to promote the evolution and advancement of the OpenCL standard. The first workshop has an exciting full program, including a full day of tutorials, followed by a full day of keynotes, papers, and panels. More information can can be found here: http://iwocl.org.

Abstract:

Recently, general-purpose computing on graphics processing units (GPGPU) has been enabled on mobile devices thanks to the emerging heterogeneous programming models such as OpenCL. The capability of GPGPU on mobile devices opens a new era for mobile computing and can enable many computationally demanding computer vision algorithms on mobile devices. As a case study, this paper proposes to accelerate an exemplar-based inpainting algorithm for object removal on a mobile GPU using OpenCL. We discuss the methodology of exploring the parallelism in the algorithm as well as several optimization techniques. Experimental results demonstrate that our optimization strategies for mobile GPUs have significantly reduced the processing time and make computationally intensive computer vision algorithms feasible for a mobile device. To the best of the authors’ knowledge, this work is the first published implementation of general-purpose computing using OpenCL on mobile GPUs.

(Guohui Wang, Yingen Xiong, Jay Yun and Joseph R. Cavallaro: “Accelerating Computer Vision Algorithms Using OpenCL on the Mobile GPU – A Case Study”, International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, May 2013, to appear. [PDF])

PARALUTION is a library for sparse iterative methods with special focus on multi-core and accelerator technology such as GPUs. In particular, it incorporates fine-grained parallel preconditioners designed to expolit modern multi-/many-core devices. Based on C++, it provides a generic and flexible design and interface which allow seamless integration with other scientific software packages. The library is open source and released under GPL. Key features are:

• OpenMP, CUDA and OpenCL support
• No special hardware/library requirement
• Portable code and results across all hardware
• Many sparse matrix formats
• Various iterative solvers/preconditioners
• Generic and robust design
• Plug-in for the finite element package Deal.II
• Documentation: user manual (pdf), reports, doxygen

More information, including documentation and case studies, is available at http://www.paralution.com.

From a recent press release:

Amdahl Software, a leading supplier of development tools for multi-core software, after extensive beta testing by evaluators over a dozen countries and numerous end-user application markets, today announced the production release of OpenCL CodeBench. OpenCL CodeBench is an OpenCL Code Creation tool. It simplifies parallel software development, enabling developers to rapidly generate and optimize OpenCL applications. Engineering productivity is increased through the automation of overhead tasks. The tools suite enables engineers to work at higher levels of abstraction, accelerating the code development process. OpenCL CodeBench benefits both expert and novice engineers through a choice of command line or guided, wizard-driven development methodologies. Close cooperation with IP, SOC and platform vendors will enable future releases of OpenCL CodeBench to more tightly optimize software for specific end user platforms and development environments.

OpenCL CodeBench is available for trial or purchase. For additional information, please visit www.amdahlsoftware.com.

AccelerEyes has released dates for their upcoming CUDA and OpenCL training courses.

CUDA

• Feb 25-26, Houston, TX
• Mar 4-5, Washington D.C.
• Mar 25-26, Los Angeles, CA
• Apr 9-10, Seattle, WA
• Apr 15-16, San Francisco, CA

OpenCL

• Feb 27-28, Houston, TX
• Mar 6-7, Washington D.C.
• Mar 27-28, Los Angeles, CA
• Apr 11-12, Seattle, WA
• Apr 17-18, San Francisco, CA

More information can be found on the courses’ webpages.

Acceleware has recently announced four courses on parallel programming:

• OpenCL on AMD APU CPUs: Jan 29 to Feb 1, 2013, Chicago, IL and Apr 9 to Apr 12, 2013, Los Angeles, CAL
• 4 Day CUDA Course with an Oil and Gas focus: Mar 12 to Mar 15, 2013, Houston, TX
• 4 Day C++ AMP Training: Apr 23 to Apr 26, 2013, Seattle, WA

More information is available on the courses’ webpages.

Abstract:

We present MGPU, a C++ programming library targeted at single-node multi-GPU systems. Such systems combine disproportionate floating point performance with high data locality and are thus well suited to implement real-time algorithms. We describe the library design, programming interface and implementation details in light of this specific problem domain. The core concepts of this work are a novel kind of container abstraction and MPI-like communication methods for intra-system communication. We further demonstrate how MGPU is used as a framework for porting existing GPU libraries to multi-device architectures. Putting our library to the test, we accelerate an iterative non-linear image reconstruction algorithm for real-time magnetic resonance imaging using multiple GPUs. We achieve a speed-up of about 1.7 using 2 GPUs and reach a final speed-up of 2.1 with 4 GPUs. These promising results lead us to conclude that multi-GPU systems are a viable solution for real-time MRI reconstruction as well as signal-processing applications in general.

(Sebastian Schaetz and Martin Uecker: “A Multi-GPU Programming Library for Real-Time Applications”,  Algorithms and Architectures for Parallel Processing (2012): 114-128. [DOI] [ARXIV])

amgcl is a simple and generic algebraic multigrid (AMG) hierarchy builder. Supported coarsening methods are classical Ruge-Stuben coarsening, and either plain or smoothed aggregation. The constructed hierarchy is stored and used with help of one of the supported backends including VexCL, ViennaCL, and CUSPARSE/Thrust.

With help of amgcl, solution of a large sparse system of linear equations may be easily accelerated through OpenCL, CUDA, or OpenMP technologies. Source code of the library is publicly available under MIT license at https://github.com/ddemidov/amgcl.