AMD Releases APP SDK 2.8.1 with support for Bolt C++ Template Library, OpenCV, and GCN

July 14th, 2013

From a recent press release:

AMD’s APP SDK is an essential resource for developers who wish to leverage the processing power of heterogeneous computing. OpenCL™ is the primary mechanism for achieving this today, but AMD’s goal is to enable developers to accelerate applications with the programming paradigm of their choice. Toward that end, AMD has added support for heterogeneous libraries such as the newly released Bolt open source C++ template library and OpenCV computer vision library which now includes heterogeneous acceleration.

New to APP SDK 2.8.1:

Bolt: With the recent launch of Bolt 1.0, AMD has added several samples to the APP SDK to demonstrate Bolt 1.0 features. These showcase the usage of Bolt APIs such as scan, sort, reduce and transform. Other new samples highlight the ease of porting from STL and the performance benefits achieved over equivalent STL implementations. We’ve also included samples to demonstrate the different fallback options available in Bolt 1.0 when no GPU is available which ensure your code runs correctly on any platform.

OpenCV: AMD has been working closely with the OpenCV open source community to add heterogeneous acceleration capability to the world’s most popular computer vision library. These changes are already integrated into OpenCV and are readily available for developers who want to improve performance and efficiency of their computer vision applications. AMD has included samples to illustrate these improvements and highlight how simple it is to include them in your app.

GCN: AMD recently launched its new Graphics Core Next (GCN) architecture on several AMD products. GCN is based on a scalar architecture vs. the VLIW vector architecture of prior generations, so hand-tuned vectorization to optimize hardware utilization is no longer needed. We’ve modified several samples in AMD APP SDK 2.8.1 to show the ease of writing scalar code as compared to vectorization.

For more information, see developer.amd.com.

Thrust v1.7 Released

July 4th, 2013

The Thrust team is pleased to announce the release of Thrust v1.7, an open-source C++ library for developing high-performance parallel applications. Modeled after the C++ Standard Template Library, Thrust brings a familiar abstraction layer to the realm of parallel computing

Thrust 1.7.0 introduces a new interface for controlling algorithm execution as well as several new algorithms and performance improvements. With this new interface, users may directly control how algorithms execute as well as details such as the allocation of temporary storage. Key/value versions of thrust::merge and the set operation algorithms have been added, as well stencil versions of partitioning algorithms. For 32b types, new CUDA merge and set operations provide 2-15x faster performance while a new CUDA comparison sort provides 1.3-4x faster performance.

Thrust is open-source software distributed under the OSI-approved Apache License 2.0.

Intel Releases SDK with OpenCL* 1.2 support for Intel® Xeon Phi™ Coprocessors

May 11th, 2013

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

Fast GPU Debayer Software

March 13th, 2013

The GPU Debayer software developed by Fastvideo can be used for demosaicing of raw 8-bit Bayer images to full-color 24-bit RGB format. The application employs the HQLI and DFPD algorithms and is tuned for NVIDIA GPUs, which results in very fast conversion, e.g., only 1.25 ms for Full HD image demosaicing on GeForce GTX 580. The software is freely available.

PARALUTION – A fast, user-friendly library for sparse iterative methods on CPUs and GPUs

February 25th, 2013

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.

Lab4241 GP-GPU profiler

February 21st, 2013

A free, pre-alpha release of Lab4241′s GPGPU profiler is now available at www.lab4241.com. It provides source-code-line performance profiling for C or C++ code and CUDA kernels in a non-intrusive way. The profiler enables the developer to a seamless evaluation of used GPU resources (execution counts, memory access, branch diversions, etc.) per source-line, along with result evaluation in a simple, intuitive GUI, similar as with known CPU profilers like Quantify or valgrind.

Free online course on parallel programming on Udacity

February 10th, 2013

This class teaches the fundamentals of parallel computing with the GPU and the CUDA programming environment. Examples are based on a series of image processing algorithms, such as those in Photoshop or Instagram. Programming and running assignments on high-end GPUs is possible, even if you don’t own one yourself. The course started Monday 4th Feb 2013 so there is still time to join. More information and enrollment: https://www.udacity.com/course/cs344.

Amdahl Software announces the general availability of OpenCL CodeBench

February 7th, 2013

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.

amgcl: an accelerated algebraic multigrid for C++

December 21st, 2012

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.

rCUDA 4.0 released

December 18th, 2012

rCUDA (remote CUDA) v4.0 has just been released. It provides full binary compatibility with CUDA applications (no need to modify the application source code or recompile your program), native InfiniBand support, enhanced data transfers, and CUDA 5.0 API support (excluding graphics interoperability). This new release of rCUDA allows to execute existing GPU-accelerated applications by leveraging remote GPUs within a cluster (both via sharing and/or aggregating GPUs) with a negligible overhead. The new version is available free of charge ar www.rCUDA.net, along with examples, manuals and additional information.

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