Fifth-year student Adam Kelly focused on finding the most efficient way possible to simulate quantum computing. Adam's project QCGPU is a high performance, hardware accelerated quantum computer simulator written with Python and OpenCL. News coverage and short interview with Adam are online, as well as the research paper. Congratulations Adam!
Interested in heterogeneous programming for CPUs, GPUs and FPGAs in #OpenCL? Then submit a paper, technical presentation, poster, workshop or tutorial at the annual International Workshop on OpenCL. Deadline Jan 27th
The Khronos Group OpenCL API is a SIMD programming model which maps well to the GPU but mostly bypass the fixed graphics-specific logic. The latest Radeon GPU Profiler 1.4 (RGP) now has the ability to profile OpenCL workloads in RGP. Most of the major RGP features that you’re used to using for profiling graphics workloads generated by Vulkan and DirectX 12 are there when profiling OpenCL applications, including the workload and barrier overviews.
Alibaba's datacenter uses Xilinx FPGAs to accelerate billions of transactions for shoppers and Microsoft, in a recent announcement, said it would deploy Xilinx in its datacenter as well. This is good news for OpenCL, as Xilinx FPGA acceleration includes support for high-level programming languages and tools, including C, C++, and OpenCL.
Intel’s open-source programming function computer vision library OpenCV has released the first stable version in its 4.0 line. Release highlights list the dnn module now includes experimental Vulkan backend, and the popular Kinect Fusion algorithm has been implemented and optimized for CPU and GPU using OpenCL.
The Khronos Group has been thinking about ways that we can provide the community with a new space to chat, ask questions and learn from one another. With this in mind we have created a Slack that is open to all developers interested in or currently developing with Khronos Standards.
There are channels for each active standard and some more casual channels where you can hang out, share your work, and discuss more general topics. We encourage everyone to take part in the “Ask Anything” channel either to ask questions or help others. Although representatives of Khronos Member’s may be in a channel, non-public and/or internal only information about standards or a specific members technology will not be shared in this Slack. The Slack will be moderated and the standard Khronos Code of Conduct applies.
Nallatech and BittWare have announced their FPGA products supporting OpenCL-based tool flows for Xilinx and Intel will be marketed under the BittWare brand, part of Molex. Customers will be able to program applications using traditional HDL or higher abstraction C, C++ and OpenCL-based tool flows. Read the full press release. Nallatech also announced it will deliver its family of OpenCL-compatible accelerator cards featuring Altera Stratix V FPGAs to the High Performance Computing (HPC) market. More on this here.
The reference cards for OpenCL 1.1, 1.2, 2.0 and 2.1 have been updated. What changed: Most Preprocessor directives & macros in OpenCL begin with a double-underscore (__), however the “CL_VERSION_X” ones do not. These revised reference cards remove the double underscore from those. The reference cards on Lulu for purchase are also updated.
Qualcomm has introduced the new Qualcomm Snapdragon 675 Mobile Platform. The Snapdragon 675 offers outstanding gaming, a leap in artificial intelligence (AI) capability and a cutting-edge camera. Premium features in the Snapdragon 675 are enabled by the Qualcomm AI Engine, Qualcomm Spectra ISP, Qualcomm Kryo CPU and Qualcomm Adreno GPU. A number of specific games and game engines have been optimized including Unity, Unreal, Messiah, and NeoX. Qualcomm Technologies also supports popular tools and APIs, including Vulkan, OpenGL 3.2, OpenCL, and Snapdragon profiler.
NXP delivers a wide range of processing solutions on which machine-learning (ML) applications can run. Developers will need the associated software and tools to make them work and this is where eIQ framework and development tools come into play. The eIQ framework is designed to work with hardware abstraction layers like OpenCL, OpenVX, and the Arm Compute Library, as well as inference engines like the Arm NN (neural net), Android NN, GLOW, and OpenCV.