We have two key pieces of advice when it comes to AMP. On Apple Silicon, you can also leverage the machine learning accelerators more directly using the accelerate framework.Īnd, of course, everything in the accelerate, compression and SIMD frameworks all have highly tuned implementations for both Intel-based and Apple Silicon Macs. To be eligible to run on the Neural Engine, you want computeUnits set to "all," which is also the default. You might want to check that you're not explicitly configuring your model to run on cpuOnly, or cpuAndGPU. Your Core ML code should just run on the Neural Engine without you needing to make any changes. The functionality is available on Intel-based Macs too, but on Apple Silicon, Core ML is much faster and more efficient, and it takes advantage of the Neural Engine and the machine learning accelerators. ![]() Your same Core ML code can run on any Mac. I'm not going to attempt to read that, but just look out for the ones with BiPlanar in the name. Apple Silicon is particularly efficient at handling BiPlanar formats, such as this one. To get the very best performance, you'll want to use the pixel formats that the hardware is optimized for. To take advantage of the hardware video encoders and decoders, you can use the same AVFoundation and VideoToolbox frameworks that are in macOS today. On Apple Silicon, you'll just see a significant speed boost when running tasks that benefit from the unified memory architecture. To run work on the GPU, you should be using the Metal API on both Intel-based and Apple Silicon Macs. But we've been working for years to build a consistent set of APIs across all our platforms and to optimize those frameworks for Apple Silicon. So, how should your applications take advantage of these new capabilities from macOS? You might be expecting us to announce new APIs for you to adopt in your applications. macOS will use all these cores simultaneously, and applications are scheduled onto the appropriate cores depending on their current performance requirements. The cores support the same architectural features and command all the same software. We call this asymmetric multiprocessing, or AMP. ![]() The Mac has had a multi-core CPU for years, but for Intel-based Macs, all cores have similar performance.Īpple Silicon Macs have a mix of performance cores for when your application needs the maximum performance, and more power-efficient cores for less CPU-intensive tasks. Apple Silicon contains coprocessors, including powerful and efficient video encoders and decoders, the Neural Engine and matrix multiplication machine learning accelerators. Using Apple Silicon in the Mac also allows us to bring unique technologies developed for the iPhone and iPad over to the Apple Mac. Graphics resources, such as textures, images and geometry data, can be shared between the CPU and GPU efficiently, with no overhead, as there's no need to copy data across a PCIe bus. This means that the GPU and CPU are working over the same memory. Building everything into one chip gives the system a unified memory architecture. Now, the new Apple Silicon Macs combine all these components into a single system on a chip, or SoC. Machines with a discrete GPU have separate memory for the CPU and GPU. Intel-based Macs contain a multi-core CPU, and many have a discrete GPU, and recent Macs also have a T2 chip which enables features such as Apple Pay, TouchID and Hey Siri. Then I'll hand over to my colleague, Anand, who'll be taking you through boot features and recovery. We'll go over some security enhancements, and we'll touch on application compatibility. We're going to talk about new features and how to take advantage of them in your macOS applications. ![]() So I'm delighted to get to introduce some of the changes coming in these systems. I'm in the Core OS group, and my team have been working on bringing macOS to Apple Silicon.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |