How to get started with Metal Compute on macOS

If you program in macOS and you want to get the most out of your GPU MacSooner or later, you're going to come across Metal. It's not just "another graphics API": it's the gateway to the raw performance of Apple Silicon and everything that comes with it (AAA games, video editing, IA, scientific simulations…).

In recent generations, especially with Metal 3 and Metal 4, Apple has made a huge leap in graphics, general computing (Metal Compute) and machine learningIn this article, we'll take a detailed and practical look at how to get started with Metal Compute on macOS, what Apple Silicon's architecture offers, how to properly organize queues, command buffers, and encoders, and how to take advantage of more advanced features like tensors, MetalFX, ray tracing, and shader precompilation.

What is Metal and why use Metal Compute on macOS

Metal is the Apple's low-level graphics and compute APIIt was born as a replacement for OpenGL/OpenCL and, over the years, has become the standard for the entire ecosystem: macOS, iOS, iPadOStvOS and visionOS. It offers a model of programming low overhead, very close to hardware, with direct control over the GPU.

In recent years, Metal has been the foundation of demanding games like Cyberpunk 2077, Assassin's Creed Shadows, or Control Ultimate EditionIn addition to professional applications for video editing, 3D, CAD, and science, it relies on both the graphics component and Metal Compute, the component designed to run computing kernels on the GPU.

Metal 3 and, above all, Metal 4 take this approach much further: new command structures, explicit memory management, detailed synchronization barriers, scattered resources, argument tables, and residence sets, ray tracing, MetalFX for upscaling and frame interpolation, and first-class compatibility with machine learning through dedicated tensors and encoders.

Basic architecture of Metal Compute in macOS

To understand how to get started with Metal Compute on macOS, you need to be clear on a few basic building blocks: device, queues, command buffers, command encoders and resourcesAll of them are available through Apple's Metal framework.

The entry point is MTLDeviceThis represents the GPU you'll be working with. On a Mac with Apple Silicon, you'll typically have a single device, although Macs with dedicated GPUs can have multiple ones. From this device, you create a MTLCommandQueue, which is the queue where the jobs for the GPU will be queued.

You generate on that tail MTLCommandBufferThese are "packages" of work that are sent to the GPU. Within each command buffer, you use different encoders to encode specific operations: MTLComputeCommandEncoder for computing kernels, MTLRenderCommandEncoder for rendering and MTLBlitCommandEncoder for copies and memory operations.

Compute operations in Metal are implemented as functions written in Metal Shading Language (MSL). You compile those functions into a computing pipeline (MTLComputePipelineState) and then, from the encoder, you dispatch them with a certain number of threads and thread groups to process your data in parallel.

The data that these kernels work with is stored in resources: MTLBuffer for raw data (arrays, structures, etc.) and MTLTexture for data with image structure (images, depth maps, cubemaps…). Metal 4 also includes the MTLTensor, designed specifically for multi-dimensional machine learning workloads.

Execution model and best practices with command buffers

Metal's philosophy is very clear: minimize CPU overhead and let the GPU work at full capacity. This involves creating and filling command buffers with sufficient work, avoiding flooding the GPU with overly small operations that only add latency.

The typical sequence for a computation job is: create a command buffer from the queue, add a compute encoder, establish the compute pipeline, link the necessary buffers and textures, dispatch the kernel (once or several times), close the encoder and, finally, c from the command buffer. Optionally, you can use completion handlers or wait methods to synchronize with the CPU.

One key detail is that Metal allows encoding multiple command buffers in parallel from different CPU threadsIf you want to scale well on a MacBook Pro with an M1 Pro or M1 Max (16 or 32 GPU cores), you'll want to use multiple CPU threads in parallel, each preparing its own command buffer with its own set of resources and encoders.

However, each command buffer introduces some transmission latency. If you create too many with very little work, you'll lose time at the driver layer. Apple's guides recommend the following: group multiple encoders within the same command buffer and send a moderate number of buffers per frame, typically one main buffer per frame and, if necessary, some very specific auxiliary buffers.

When the order between command buffers matters, you can resort to enqueue to reserve their position in the queue or simply call commit in the correct order. The most demanding cases (streaming (of resources, ray tracing, etc.) can combine multiple queues and synchronize them using MTLEvent events.

Unified Memory Architecture and Memory Management

Apple Silicon's Unified Memory Architecture is one of the reasons why Metal Compute shines on macOSCPU and GPU share a single pool of physical memory, so a resource can be accessed from both without explicit copies.

Metal exposes this reality through shared resources: you can have a MTLBuffer in shared memory that the CPU fills and the GPU kernel reads, or vice versa. In these cases, the focus is no longer on copying data from one place to another (as was the case with traditional dedicated VRAM), but on synchronize access properly so that the CPU and GPU do not use the same resource at the same time.

In situations where there is a potential conflict (for example, the CPU updating a buffer of constants for the next frame while the GPU is still processing the current one), the usual approach is to adopt a model of double or triple bufferingby keeping multiple copies of the buffer in rotation. Thus, each frame GPU and CPU work on different instances.

Metal also introduces the concept of working setThis is the amount of memory a single command encoder can reference at any given time without incurring excessively expensive memory relocations. The device provides a clue via the `recommendedMaxWorkingSetSize` property, which should be checked and respected to avoid surprises.

On modern machines with Apple Silicon, the figures are spectacular: an M1 Max with 32 GB of RAM allows the GPU to access around 21 GB memoryThe 64GB model offers approximately 48GB of VRAM accessible to the GPU. This enables massive scenes and datasets that were previously only feasible on desktop workstations with massive VRAM.

Resources, argument tables, and residence sets in Metal 4

Modern applications, especially complex game engines, no longer work with four textures and a couple of buffers, but with thousands of Simultaneous resources: meshes, high-resolution textures, light maps, acceleration structures for ray tracing and so on. The traditional way of linking resources (fixed slots per draw/dispatch) falls short.

A common solution in the latest generations of APIs is the "bindless" model: instead of linking dozens of resources per draw, you group the information into a argument buffer and the shader indexes it. Metal 4 takes it a step further with the argument tables, which function as containers for links by stage (vertex, fragment, compute…), but which you can share and configure in advance.

Each argument table is scaled to the number of slots you need. In a completely bindless model, a single binding to an argument buffer containing descriptors for many resources may suffice. When drawing or dispatching kernels, Metal compiles the arguments and ensures that the access is secure even if you change a resource between drawing calls.

The other pillar is the resource residenceEven with unified memory, the GPU needs to know which resources should be "resident" for a given body of work. Metal 4 introduces the residential complexes, where you group resources that you want the GPU to be able to access while a command buffer or an entire queue is running.

The recommended practice is to create a few large sets (instead of many small sets) and fill them in at the beginning of the app's lifeThus, by attaching them to the queue or to specific command buffers, Metal can prepare those resources in bulk and minimize the runtime residency management overhead.

A real-world example: In Control Ultimate Edition, Remedy divided its resources into several residence sets based on usage (base scene, effects, ray tracing, etc.) and moved the updating of these sets to a background thread. This reduced both residence overhead and memory consumption when ray tracing was disabled, improving performance. performance and stability.

Dispersed resources and fine memory management

When a game or app handles huge worlds, giant textures, or large datasets, not everything can fit into memory at once in the traditional way. This is where the dispersed resources with manual allocation, introduced in Metal 4 for buffers and textures.

The idea is to decouple the logical creation of the resource from its physical memory backup. You create a "sparse" buffer or texture, and the actual memory comes from a placement heapFrom that heap you assign "pages" (tiles) that cover ranges of bytes or pixel regions of the resource.

This approach lets you decide which parts of a resource are actually backed up at any given time. For example, you could have a global mega-texture where only some areas are in high resolution and the rest remain at low quality or unassigned, depending on what the player has near the camera.

Metal 4 is heavily focused on concurrency, so these mapping updates need to be well synchronized. A barriers API is provided for this purpose. stage synchronization with low overhead, in line with what other modern APIs (Vulkan, DirectX 12) offer. This allows you to update memory allocation in one thread while other command buffers are being encoded or the GPU is processing useful work.

In practice, to integrate dispersed resources into an existing Metal app, you can dedicate a Metal 4-specific command queue for mapping operations and use MTLEvent To synchronize that queue with the one you already use for rendering or traditional computing, the CPU sends work that doesn't depend on the resources being updated, and when the event indicates that the mapping is ready, you continue rendering using the new allocation.

Command coding in Metal 4: render, compute, and barriers

Metal 4 reorganizes and simplifies the way work is coded. Instead of multiple specialized encoders, there are two main components: MTL4RenderCommandEncoder and a unified compute encoder which includes shipments, blits, and the construction of acceleration structures.

In computing terms, this means you can encode blits, kernels, and ray tracing-related operations in a single encoder, and Metal will execute everything in parallel that doesn't have explicit data dependencies. When a dependency does exist (for example, a blit filling a buffer that a kernel then reads), it is expressed using barriers.

In rendering, Metal 4 introduces a very powerful mechanism: the color attachment allocation mapInstead of binding a pipeline to a fixed layout of render targets, you can define a “superset” of attachments in the render pass descriptor and then use the color map to translate the logical outputs of the fragment shader to concrete physical attachments.

This avoids having to create new render encoders every time you want to write to a different set of outputs. You configure all the attachments you'll need in a single encoder, create reusable color maps, and assign them to the different pipelines. You drastically reduce the number of encoders and the number of render passes necessary.

To synchronize between compute and render, or between different encoders in the same queue, Metal 4 offers the queue barriers, filtered by stages: fragment, vertex, compute dispatch, blit, acceleration, machine learning, etc. This allows you to say, for example, "the fragmentation stage cannot read this texture until the dispatch stage that writes it is finished."

A classic example: a compute kernel performs a atmospheric simulation and writes to a texture; then, a render pass uses that texture in the fragment shader to light the scene, while the vertex shading can overlap seamlessly with the compute. With a well-placed barrier (from the dispatch stage to the fragment stage), you ensure the GPU takes full advantage of overlap without violating any data dependencies.

Compiling shaders and pipelines in Metal 4

Modern apps, and especially games, manage hundreds or thousands of shaders and process states (pipelines). Compiling all that poorly managed data is a recipe for stutters and endless loading screens. Metal 4 introduces several pieces to mitigate this problem.

On one hand, there is the new MTL4CompilerA separate interface from the device used to more explicitly control when shaders and pipelines are compiled. You can create build contexts, use multiple threads or Grand Central Dispatch queues, and let the system prioritize builds according to QoS of the thread that invokes them.

On the other hand, there are flexible rendering process statesInstead of compiling three separate processes for, say, a holographic house (additive mixing), a house under construction (transparent), and a finished house (opaque), you can generate a non-specialized pipeline which contains the vertex binary, the fragment shader body, and a default fragment output.

From that non-specialized pipeline, you generate specialized pipelines by only changing the color settings (pixel format, write mask, blending state). Metal reuses the Metal IR already compiled and only adjusts the fragment output, greatly shortening the creation times of derived pipelines.

It is true that there are cases where this flexibility introduces a small cost to the GPU (for example, when the fragment shader writes more channels than the attachment actually has), but you can identify the most critical variants with Metal System Trace in Instruments and compile "full state" versions in the background to replace them when they are ready.

To go even further, Metal 4 offers a complete early access build. You can serialize pipeline configuration scripts in JSON format (mtl4-json) from your own game, feed them to the metal-tt command-line tool along with your Metal IR libraries and produce .metal files with pre-compiled GPU binaries.

At runtime, loading a MTL4Archive From those files, you can search for pipelines by descriptor exactly as if you were compiling them on-device. If the search fails (because the pipeline is missing or due to operating system or architecture incompatibility), you can always resort to on-device compilation as a backup plan.

Metal Compute and optimization on Apple Silicon

In modern MacBook Pros with chips like the M1 Pro and M1 Max, the GPU has many more cores than the basic M1 and features a much higher memory bandwidthTo get the most out of Metal Compute, it's advisable to adjust both the job dispatch and the kernel structure.

At the GPU level, Metal exposes separate caches for buffer reads and texture reads. If your kernels only use buffers, you're not taking advantage of the cache dedicated to texturesA common technique is to transfer certain data to textures (even if conceptually they are matrices or volumes), taking advantage of the swizzling and optimized access offered by the hardware.

In addition, textures can benefit from Lossless compression transparent to the shader When they are GPU-deprived, and ASTC/BC compression (at a much higher ratio) is used when the loss of quality is acceptable. This reduces memory and bandwidth consumption, which is key for read-intensive kernels.

At the MSL kernel level, there are several recommendations: use signed integers for indexing arrays (avoid disabling vectorized loads due to the overflow behavior of unsigned threads), minimize the use of global atomics except when absolutely necessary, and take care of occupancy, which measures how many active threads the GPU has in relation to its maximum.

Low occupancy along with low limiter counters usually indicates that you are running out of space. thread-group registers or memoryMetal and Xcode allow you to view register spill and other compiler data. Reducing the size of local structures, preferring 16-bit types when possible, and avoiding large arrays dynamically indexed on the stack are measures that typically alleviate the pressure.

Finally, playing with maxThreadsPerThreadgroup (either in the pipeline descriptor or as an attribute in the kernel) helps the compiler spill more efficiently to find the sweet spot of thread group size that best utilizes the hardware without spiking register consumption.

Machine Learning and Metal Compute: Tensors and Shader ML

Metal 4 integrates machine learning into the very heart of the API. It's no longer just about using CoreML as a black box for isolated inferences, but about Interweave neural networks with your computing and rendering passes on the same GPU timeline.

The basic piece is the MTLTensorMultidimensional resources designed to represent ML data: weights, activations, inputs, and outputs. Unlike textures, which are limited to two dimensions and a few channels, tensors can have arbitrary range, and each dimension has its own embedded extent and stride, greatly simplifying indexing.

You can create tensors from the device (receiving an opaque layout optimized for the GPU) or from an existing MTLBuffer, in which case you manually specify the strides for the tensor to wrap the desired memory region, including possible unused fills or columns.

To run full networking on the GPU timeline, Metal 4 provides MTL4MachineLearningCommandEncoderThe workflow is divided into two phases: offline you convert your model (for example, from PyTorch or TensorFlow) to CoreML and then to a MTLPackage Using the metal-package-builder tool, at runtime you open that package as a library, define a function descriptor with the main network input, and build a MTL4MachineLearningPipelineState.

Once you have the AA pipeline, you create an ML encoder, assign the pipeline to it, link the input and output tensors, and dispatch the network using a specific method that utilizes a MTLHeap of type placement for storing intermediatesThe minimum memory required is indicated by the pipeline's intermediateHeapSize, allowing you to size the heap precisely as needed and reuse it across multiple offices.

What's interesting is that this AA work fully integrates with Metal 4's sync primitives. You can use barriers and fences with the MTLStageMachineLearning stage to synchronize, for example, the rendering of a frame with the output of a temporal antialiasing network, or to run independent parts of the graphics pipeline in parallel while the network processes its data.

For small networks or for integrating AI into existing shaders, it comes into play Shader MLHere, instead of treating the network as a black box launched from an AA encoder, you incorporate the operations directly into your shaders using MTLTensor and the Metal Performance Primitives (MPP) for tensioners, such as matmul2d for matrix multiplications and optimized convolutions.

A very representative example is the compression of neuronal materialsIn the classic workflow, a fragment shader samples textures such as albedo, normals, roughness, etc., and uses them for shading. With neural materials, you instead sample "latent" textures, build an input tensor from those values, evaluate a small network within the shader (using matmul2d and ReLU-like activations), and obtain a material decompressed into thread memory that feeds the shading algorithm.

This approach reduces memory usage and disk space —in practical demonstrations it has been achieved compressing materials to half the size of traditional block compression— maintaining a virtually indistinguishable visual quality when the material is integrated into the final lighting.

Shader ML operations are not restricted to fragment shaders; they can be used at any stage (vertex, compute, etc.). However, when configuring matmul2d or similar tools, you must consider whether the operation will be executed by a single thread or by larger groups (simdgroup, threadgroup), and whether the flow of control and indexing on the tensors will be... uniform or divergentto choose the appropriate execution mode and avoid surprises.

MetalFX, ray tracing and high-performance gaming

In the realm of gaming, Metal 4 combines with MetalFX To tackle two critical issues: performance and image quality. Rendering at native resolution with ray tracing, complex reflections, and advanced effects can saturate even powerful GPUs, so having an upscaling and frame interpolation solution integrated into the ecosystem is invaluable.

MetalFX lets your game Render at a lower resolution and scale to the final output with techniques based on machine learning and temporal knowledge. The result is that There The combined cost of rendering and upscaling per frame is less than native rendering, freeing up budget to prepare the next frame or to activate more expensive effects.

In addition, MetalFX adds frame interpolation: from consecutive frames and auxiliary data (such as motion vectors) it generates intermediate frames in less time than it would take to render everything from scratch. This can increase perceived frame rates without pushing the GPU to its limits.

In ray tracing scenarios, where you fire few rays per pixel and noise becomes a problem, MetalFX offers integrated denoising with upscalingThe pipeline can take an undersampled and noisy image, remove the noise, and scale it to full size, resulting in a clean image without the need for extremely dense tracings.

Metal 4 also incorporates a proper ray tracing stack: acceleration structures, specific commands, and support in the compute and render encoders for casting rays. Along with everything mentioned above (residence, sparse resources, efficient pipeline compilation, MetalFX…), it allows carry sophisticated AAA games to macOS without major compromises.

Modern engines are already taking advantage of these capabilities. There are examples of titles that stream gigabytes of geometry and textures, and execute thousands of shaders broken down into flexible pipelines and use advanced streaming techniques based on placement heaps and sparse resources to adapt to the memory available on each device.

Debugging tools and templates to get started

All this power would be unmanageable without good tools. Xcode and Apple's tools offer a fairly comprehensive set for Debug and optimize Metal applications, including those that use Metal Compute intensively.

First is the GPU Frame Debugger from Xcode, which allows you to capture a frame, inspect the command list, view linked resources, study pipelines and, very importantly, analyze performance counters (ALU utilization, memory usage, occupancy, limiters, etc.).

In terms of synchronization, the Dependency Viewer It graphically shows how command buffers, encoders, barriers, and events are related, helping to locate synchronization mismatches or over-synchronization that blocks the GPU more than it should.

For machine learning workloads, the new ML Network Debugger It is especially useful. It visualizes the network structure (operations, connections, intermediate tensors) and allows you to inspect the output of each node to detect where artifacts or unexpected values ​​appear. In combination with the MTLTensor viewer, you can compare inputs and outputs and isolate implementation or export errors in the model.

At the day-to-day development level, Xcode includes Metal API validation and shader validation It warns of dangerous or outright incorrect patterns, and offers a Metal 4 game project template to get started quickly: simply start a project, choose "Game Templates" and select Metal 4 as the technology.

If you develop using third-party engines like Unity, you can enable Metal as the default graphics API on macOS, iOS, and tvOS. Unity offers support for advanced features (compute shaders, tessellation, memoryless render targets on iOS/tvOS, etc.), although it's worth remembering that Metal does not support geometry shaders and that on older devices Metal support is limited to Metal 2 or Metal 3.

Finally, Apple's official documentation, code samples, and regularly published technical talks are a goldmine to watch. real-world use cases, optimization patterns, and port strategies from other APIs like DirectX or Vulkan to Metal.

With everything Metal 4 has to offer—from a more flexible and efficient command model, to advanced memory and sparse resource management, to deep integration with machine learning and technologies like MetalFX and ray tracing—getting started with Metal Compute on macOS means to have in your hands a platform capable of running next-generation gamesTop-tier professional applications and complex AI solutions are possible, provided you dedicate some time to understanding their architecture, leveraging their tools, and structuring your resources and pipelines effectively.