ArrayFire announced the release of ArrayFire v3.5, an open source library of parallel computing functions supporting CUDA, OpenCL, and CPU devices. This new version of ArrayFire improves features and performance for applications in machine learning, computer vision, signal processing, statistics, finance, and more. Release notes are available and the source code can be found on Github.
In collaboration with Google, Codeplay is proud to announce the release of a new open-source tool allowing the compilation of OpenCL C language kernels to run on the Vulkan API. The tool, named ‘clspv’, allows a subset of the OpenCL C language to be targeted at the Vulkan API. This tool allows developers to port code containing more than a million lines of OpenCL C to run on the Vulkan API. The source is available on Github.
Synopsys announced that it has enhanced the convolutional neural network (CNN) engine in its DesignWare EV6x Vision Processors to address the increasing video resolution and frame rate requirements of high-performance embedded vision applications. To simplify software application development, the EV6x processors are supported by a comprehensive suite of tools and software. Combined with software development tools based on OpenVX, OpenCV and OpenCL C embedded vision standards, the MetaWare EV Development Toolkit offers a full suite of tools needed to accelerate embedded software development.
Apple announced several updates to the Mac lineup earlier this month at WWDC. Geekbench 4, which includes a new GPU Compute Benchmark that measures the performance of GPUs at performing compute tasks, shows that GPU performance with OpenCL has improved considerably with an increase of up to 80% when compared to the equivalent 2015 model. If you’re interested in how your computer compares you can download Geekbench 4. Find the complete benchmark results on the Geekbench website.
Duskborn Labs has just released part II of their OpenCL to Vulkan porting guide. Part I covers cl_platform_id -> VkInstance, cl_device_id -> VkPhysicalDevice and cl_context -> VkDevice. Part II covers porting from OpenCL’s cl_command_queue to Vulkan’s VkQueue.
Futuremark is launching PCMark 10, their seventh major update to the PCMark series of benchmarks first launched in 2002. PCMark 10 builds upon the PCMark 8 platform, adds a few workloads and streamlines the rest in order to present a vendor-neutral, complete, and easy-to-use benchmark for home and office environments. Anandtech has a nice review showing a little bit of OpenGL and OpenCL usage.
Codeplay has added OpenCL hardware support to Eigen, to offer a wider range of hardware to developers via the SYCL open standard. In this post, Codeplay talks about how they implemented the SYCL backend for Eigen to enable support for OpenCL hardware.
PC Perspective had the opportunity to have a phone interview with Neil Trevett, president of the Khronos Group and chairman of the OpenCL working group, and Tom Olson, chairman of the Vulkan working group. While OpenCL is planning to merge into the Vulkan API, the Khronos Group wants to make it clear that “all of the merging” is coming from the OpenCL working group. The Vulkan API roadmap is not affected by this decision. Of course, the Vulkan working group will be able to take advantage of technologies that are dropping into their lap, but those discussions have not even begun yet. Read the entire post to learn more about the exciting future of OpenCL and Vulkan.
The Intel Compute Library for Deep Neural Networks (clDNN) is an open source performance library for Deep Learning (DL) applications intended for acceleration of DL inference on Intel® Processor Graphics (Intel® HD Graphics and Intel® Iris® and Intel® Iris® Pro). clDNN includes highly optimized building blocks to implement convolutional neural networks (CNN) with C and C++ interfaces. This library is also used in the Deep Learning Toolkit found in the Intel Computer Vision SDK Beta. The clDNN library can be accessed at github. To learn more on how to use clDNN see whitepaper