OpenCL tagged news

Codeplay announces SPIR-V support for ComputeCpp in v0.3.0. This beta implementation of SPIR-V for OpenCL support means that developers can use SYCL and ComputeCpp to develop for any OpenCL hardware that includes a driver that consumes SPIR-V.

The landscape of APIs for accelerating vision and neural network software using specialized processors continues to rapidly evolve. Many industry-standard APIs, such as OpenCL and OpenVX, are being upgraded to increasingly focus on deep learning, and the industry is rapidly adopting the new generation of low-level, explicit GPU APIs, such as Vulkan, that tightly integrate graphics and compute. Neil Trevett presented the "Vision Acceleration API Landscape: Options and Trade-offs" tutorial at the May 2017 Embedded Vision Summit.

Amazon AppStream 2.0 is introducing Graphics Desktop and Graphics Pro instance families to deliver high performance graphics applications from AWS. The Graphics Desktop instance family offers a single instance type with an NVIDIA GPU based on K520 with 1,536 CUDA cores, 8 vCPUs, 15 GiB system memory, and 4 GiB graphics memory. This instance type is ideal for running desktop graphics applications such as Siemens NX, SolidWorks, ESRI ArcGIS, and other applications that use DirectX, OpenGL, OpenCL, and CUDA.

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.

Radeon ProRender is a powerful physically-based rendering engine that enables creative professionals to produce stunningly photorealistic images. Built on efficient, high-performance Radeon Rays technology, Radeon ProRender’s complete, scalable ray tracing engine uses open industry standards to harness GPU and CPU performance for swift, impressive results. One of those open industry standards is OpenCL 1.2. AMD's requirements state: "Hardware agnostic – if your computer can run OpenCL 1.2, it can run Radeon ProRender." Download the Windows or Linux version directly or learn more about Radeon Pro.

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.

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.

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