- Opencl Programming Guide For Mac Os X 10 11
- Opencl Programming Guide For Mac Os X Lion
- Opencl Programming Guide For Mac Os X 10 11 Download Free
OpenCL Programming Guide Aaftab Munshi Benedict R. Gaster Timothy G. Mattson James Fung Dan Ginsburg Upper Saddle River, NJ. Boston. Indianapolis. San Francisco New York. Toronto. Montreal. London. Munich. Paris. Madrid Capetown. Sydney. Tokyo. Singapore. Mexico City. Dec 21, 2018 Technical standards were finalized in late 2008, and in 2009 Apple released OpenCL 1.0 with their Mac OS X Snow Leopard. During 2008 and 2009, OpenCL was officially embraced by AMD, NVIDIA, and IBM. Several new features and refinements were introduced in 2010 and 2011, and in 2013 OpenCL 2.0 was released, which included, amongst other things. Jun 04, 2018 OpenCL™ (Open Computing Language) is an open standard for cross-platform, programming of modern highly-parallel processor architectures. Introduced with OS X v10.6, OpenCL consists of a C99-based programming language designed for parallelism, a powerful scheduling API, and a flexible runtime that executes kernels on the CPU or GPU. Dec 21, 2018 OpenCL, or Open Computing Language, is a software framework designed exclusively for building applications that can be run across diverse computer systems. It is an open standard for developing cross-platform, parallel programming applications, which has a number of open-source implementations.
OpenCL lets you tap into the parallel computing power of modern GPUs and multicore CPUs to accelerate compute-intensive tasks in your Mac apps.Use OpenCL to incorporate advanced numerical and data analytics features, perform cutting-edge image and media processing, and deliver accurate physics and AI simulationin games.
Reference, Guides, and Sample Code
Using the OpenCL Logo
If your app uses OpenCL or you’ve created your own implementation, learn about licensing the OpenCL logo.Transitioning to Metal
If you are using OpenCL for computational tasks in your Mac app, we recommend that you transition to Metal and Metal Performance Shaders.
Learn about Metal
Spin had a great blog post a few days ago on Mean Shift Clustering. It’s a powerful algorithm with a ton of applications, but an Achille’s heel:
The most glaring disadvantage is its slowness. …it can take a long time to execute. The one silver lining is that, while it is slow, it is also embarrassingly parallelizable.
How many times has that happened to you? Your code solves a small problem perfectly, but it just isn’t fast enough for the real world. Sometimes the solution can be to just find a bigger computer. Luckily almost every computer has a bigger computer inside it: the graphics hardware. Where your computer’s CPU might have 8 cores, its GPU can have hundreds. OpenCL is a standard framework that gives you access to all that power.
The sample I’m going to show was built on Mac OS 10.10 running Xcode 6. Apple has done a nice job integrating OpenCL into their environment while preserving the data types and language features defined in OpenCL. Hopefully Windows and Linux will be in similar good situations. Much of it will come down to your graphics hardware vendor support for OpenCL–debugging especially may be rough. I’ve found online materials and Apple’s documentation to be usable, but a good book really helped. “OpenCL Programming Guide” was a nice introduction and also included useful summaries of data types and built-in functions that really helped.
I started by creating a Mac OS X command line app in Xcode. Then I added the OpenCL framework. The OpenCL documentation on Apple’s developer site is brief and worth reading.
Apple’s OpenCL can use a grand central dispatch queue on either the GPU or the CPU. Be very careful running OpenCL code on a CPU queue because it’s a lot less efficient than the GPU for running massively parallel code. My machine was completely hammered at 800% utilization with the fans at max. Running on the GPU queue in comparison has machine utilization at less than 100% with the fan off.
That said, there’s one good reason to run on the CPU during development: your GPU has no memory protection. A bug in the code running on the GPU can be very difficult to find. I strongly recommend a disciplined approach going from verification of the algorithm in plain C code, followed by porting to OpenCL on the CPU and finally to the GPU.
Which brings us to the code that actually runs on the GPU:
That looks like C code, but it’s really a “kernel” that is compiled by a separate OpenCL toolchain provided by your graphics hardware vendor. The language has nice support for small vectors (
float2
for example is a 2D point) and lots of built-in and optimized math routines. Chrome java plugin enable. The main difficulty in writing OpenCL code is getting the data in and out of the GPU.You have two copies of the data that the kernel will work on: one is accessible by the CPU and the other by the GPU (called the “device” by OpenCL). The CPU is responsible for preparing the data, loading it from disk, sending it to the GPU, etc. The copy of the data in the GPU is only for use by the GPU–it’s not in the same memory as your CPU uses.
![Opencl Programming Guide For Mac Os X Opencl Programming Guide For Mac Os X](/uploads/1/2/6/5/126526853/255973740.jpg)
Opencl Programming Guide For Mac Os X 10 11
Here are the data buffers that the CPU uses:
Here are the associated data buffers that the GPU uses:
Opencl Programming Guide For Mac Os X Lion
At first I was confused as why they were not properly typed like the CPU data buffers, but then I realized that’s sort of a feature to help prevent you from accidentally using those buffers on the CPU. The only thing you can do with them is pass them to OpenCL functions.
Everything is now setup and ready to schedule the kernel on the GPU. This code fits into Apple’s normal multi-threading approach quite well. It will look much different on Windows or Linux, but I’m not so worried about portability that this glue code bothers me.
Opencl Programming Guide For Mac Os X 10 11 Download Free
If you’re interested in building the code yourself, it’s on github under an MIT license.