G-BLASTN is a GPU-accelerated nucleotide alignment tool based on the widely used NCBI-BLAST. G-BLASTN can produce exactly the same results as NCBI-BLAST, and it also has very similar user commands. It also supports a pipeline mode, which can fully use the GPU and CPU resources when handling a batch of medium to large sized queries. Currently, G_BLASTN supports the blastn and megablast modes of NCBI-BLAST. The discontiguous megablast mode is not supported yet. More information: http://www.comp.hkbu.edu.hk/~chxw/software/G-BLASTN.html
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.
AMD CodeXL is a free set of tools for GPU debugging, GPU profiling, static analysis of OpenCL kernels, and CPU profiling, including support for remote servers. For more information and download links, see: http://developer.amd.com/community/blog/2013/11/08/codexl-1-3-released/
Bolt is an STL compatible C++ template library for creating data-parallel applications using C++ (no C++ AMP / OpenCL code required). For more information about the Bolt template library and download links, see: http://developer.amd.com/tools-and-sdks/heterogeneous-computing/amd-accelerated-parallel-processing-app-sdk/bolt-c-template-library/
AMD APP SDK has everything needed to get started with OpenCL and parallel programming. It includes OpenCL samples that are very easy to compile, as well as the Bolt and other libraries. For more information about AMD APP SDK and download links, see: http://developer.amd.com/tools-and-sdks/heterogeneous-computing/amd-accelerated-parallel-processing-app-sdk/
Allinea DDT is part of Allinea Software’s unified tools platform, which provides a single powerful and intuitive environment for debugging and profiling of parallel and multithreaded applications. It is widely used by computational scientists and scientific programmers to fix software defects of parallel applications running on hybrid GPU clusters and supercomputers. DDT 4.1.1 supports CUDA 5.5, C++11 and the GNU 4.8 compilers. Also introduced with Allinea DDT 4.1.1 is CUDA toolkit debugging support for ARMv7 architectures. More information: http://www.allinea.com
The Libra 3.0 Heterogeneous Cloud Computing SDK has recently been released by GPU Systems. It supports PC, Tablet and Mobile Devices and includes a new virtualizing function for cloud compute services of local and remote CPUs and GPUs. C/C++, Java, C# and Matlab are supported. Read the full press release here.
One of the keys to achieving maximum performance in CUDA is taking advantage of the various memory spaces. Part II of Acceleware’s tutorial has now been published. The tutorial uses a simple encryption kernel to test and compare read-only cache, constant cache and global memory. Read the full tutorial…
A free webinar on accelerating face-in-the-crowd recognition with GPU technology will be held on November 5th. It teaches how GPUs can be used to accelerate face detection and recognition of people in the crowd. The presentation will also cover the speakers’ use of ROS, OpenCV, OpenMP, and Armadillo libraries to develop fast reliable distributed video processing code. To register follow the link: https://www2.gotomeeting.com/register/292953058
This blog takes a closer look at constant cache and read-only cache. It highlights the differences between the two memory types and what circumstances they perform best in. Read the whole story here.
The rCUDA team is glad to announce that its remote GPU virtualization technology now supports the ARM processor architecture. The new release of rCUDA for this low-power processor has been developed for the Ubuntu 11.04 and Ubuntu 12.04 ARM linux distributions. With this new rCUDA release, it is also possible to leverage hybrid platforms where the application uses ARM CPUs while requesting acceleration services provided by remote GPUs installed in x86 nodes. The opposite is also possible: an application running in an x86 computer can access remote GPUs attached to ARM systems. Please visit rCUDA website for more information or for requesting a free copy of the rCUDA middleware.