February 11th, 2015
December 27th, 2014
After four pre-releases, the stable 2.0.0 version of cf4ocl, the C Framework for OpenCL, is now available.
Since the last beta release, a number of tests were added, and a few bug fixes have been fixed. Support for device fission and native kernels has also been implemented. A complete list of features and fixes is available at https://github.com/FakenMC/cf4ocl/releases.
Cf4ocl has been tested on Linux, OS X and Windows, and offers a pure C object-oriented framework for developing and benchmarking OpenCL projects in C. It aims to:
1. Promote the rapid development of OpenCL host programs in C (with support for C++) and avoid the tedious and error-prone boilerplate code usually required. Read the rest of this entry »
October 22nd, 2014
Boost.Compute is an open-source, header-only C++ library for GPGPU and parallel-computing based on OpenCL. It provides a low-level C++ wrapper over OpenCL and high-level STL-like API with containers and algorithms for the GPU. Boost.Compute is available on GitHub and its documentation can be found here. See the full announcement here: http://kylelutz.blogspot.com/2014/12/boost-compute-0.4-released.html
May 27th, 2014
The Cf4ocl project is a GPLv3/LGPLv3 initiative to provide an object-oriented interface to the OpenCL C API with integrated profiling, promoting the rapid development of OpenCL host programs and avoiding boilerplate code. Its main goal is to allow developers to focus on OpenCL device code. After two alpha releases, the first beta is out, and can be tested on Linux, Windows and OS X. The framework is independent of the OpenCL platform version and vendor, and includes utilities to simplify the analysis of the OpenCL environment and of kernel requirements. While the project is making progress, it doesn’t yet offer OpenGL/DirectX interoperability, support for sub-devices, and doesn’t support pipes and SVM.
Cf4ocl can be downloaded from http://fakenmc.github.io/cf4ocl/.
May 15th, 2014
Analysis of functional magnetic resonance imaging (fMRI) data is becoming ever more computationally demanding as temporal and spatial resolutions improve, and large, publicly available data sets proliferate. Moreover, methodological improvements in the neuroimaging pipeline, such as non-linear spatial normalization, non-parametric permutation tests and Bayesian Markov Chain Monte Carlo approaches, can dramatically increase the computational burden. Despite these challenges, there do not yet exist any fMRI software packages which leverage inexpensive and powerful GPUs to perform these analyses. Here, we therefore present BROCCOLI, a free software package written in OpenCL that can be used for parallel analysis of fMRI data on a large variety of hardware configurations. BROCCOLI has, for example, been tested with an Intel CPU, an Nvidia GPU, and an AMD GPU. These tests show that parallel processing of fMRI data can lead to significantly faster analysis pipelines. This speedup can be achieved on relatively standard hardware, but further speed improvements require only a modest investment in GPU hardware. BROCCOLI (running on a GPU) can perform non-linear spatial normalization to a 1 mm3 brain template in 4–6 s, and run a second level permutation test with 10,000 permutations in about a minute. These non-parametric tests are generally more robust than their parametric counterparts, and can also enable more sophisticated analyses by estimating complicated null distributions. Additionally, BROCCOLI includes support for Bayesian first-level fMRI analysis using a Gibbs sampler. The new software is freely available under GNU GPL3 and can be downloaded from github: https://github.com/wanderine/BROCCOLI.
(A. Eklund, P. Dufort, M. Villani and S. LaConte: “BROCCOLI: Software for fast fMRI analysis on many-core CPUs and GPUs”. Front. Neuroinform. 8:24, 2014. [DOI])
February 26th, 2014
Boost.Compute v0.2 has been released! Boost.Compute is a header-only C++ library for GPGPU and parallel-computing based on OpenCL. It is available on GitHub and instructions for getting started can be found in the documentation. Since version 0.1 (released almost two months ago) new algorithms including unique(), search() and find_end() have been added, along with several bug fixes. See the project page on GitHub for more information: https://github.com/kylelutz/compute
December 23rd, 2013
The new free open-source PyViennaCL 1.0.0 release provides the Python bindings for the ViennaCL linear algebra and numerical computation library for GPGPU and heterogeneous systems. ViennaCL itself is a header-only C++ library, so these bindings make available to Python programmers ViennaCL’s fast OpenCL and CUDA algorithms, in a way that is idiomatic and compatible with the Python community’s most popular scientific packages, NumPy and SciPy. Support through the Google Summer of Code 2013 for the primary developer Toby St Clere Smithe is greatly appreciated.
More information and download: PyViennaCL Home
November 20th, 2013
The latest release 1.5.0 of the free open source linear algebra library ViennaCL is now available for download. The library provides a high-level C++ API similar to Boost.ublas and aims at providing the performance of accelerators at a high level of convenience without having to deal with hardware details. Some of the highlights from the ChangeLog are as follows: Vectors and matrices of integers are now supported, multiple OpenCL contexts can be used in a fully multi-threaded manner, products of sparse and dense matrices are now available, and certain BLAS functionality is also provided through a shared library for use with programming languages other than C++, e.g. C, Fortran, or Python.
December 21st, 2012
VexCL is a modern C++ library created for ease of GPGPU development with C++. VexCL strives to reduce the amount of boilerplate code needed to develop GPGPU applications. The library provides a convenient and intuitive notation for vector arithmetic, reduction, sparse matrix-vector multiplication, etc. The source code is available under the permissive MIT license. As of v1.0.0, VexCL provides two backends: OpenCL and CUDA. Users may choose either of those at compile time with a preprocessor macro definition. More information is available at the GitHub project page and release notes page.
December 3rd, 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.
The latest release 1.4.0 of the free open-source linear algebra library ViennaCL features the following highlights:
- Two computing backends in addition to OpenCL: CUDA and OpenMP
- Improved performance for (Block-) ILU0/ILUT preconditioners
- Optional level scheduling for ILU substitutions on GPUs
- Mixed-precision CG solver
- Initializer types from Boost.uBLAS (unit_vector, zero_vector, etc.)
Any contributions of fast CUDA or OpenCL computing kernels for future releases of ViennaCL are welcome! More information is available at http://viennacl.sourceforge.net.
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