I have a Core Image filter in my app that uses Metal. I cannot compile it because it complains that the executable tool metal is not available, but I have installed it in Xcode. If I go to the "Components" section of Xcode Settings, it shows it as downloaded. And if I run the suggested command, it also shows it as installed. Any advice? Xcode Version Version 26.0 beta (17A5241e) Build Output Showing All Errors Only Build target Lessons of project StudyJapanese with configuration Light RuleScriptExecution /Users/chris/Library/Developer/Xcode/DerivedData/StudyJapanese-glbneyedpsgxhscqueifpekwaofk/Build/Intermediates.noindex/StudyJapanese.build/Light-iphonesimulator/Lessons.build/DerivedSources/OtsuThresholdKernel.ci.air /Users/chris/Code/SerpentiSei/Shared/iOS/CoreImage/OtsuThresholdKernel.ci.metal normal undefined_arch (in target 'Lessons' from project 'StudyJapanese') cd /Users/chris/Code/SerpentiSei/StudyJapanese /bin/sh -c xcrun\ metal\ -w\ -c\ -fcikernel\ \"\$\{INPUT_FILE_PATH\}\"\ -o\ \"\$\{SCRIPT_OUTPUT_FILE_0\}\"' ' error: error: cannot execute tool 'metal' due to missing Metal Toolchain; use: xcodebuild -downloadComponent MetalToolchain /Users/chris/Code/SerpentiSei/StudyJapanese/error:1:1: cannot execute tool 'metal' due to missing Metal Toolchain; use: xcodebuild -downloadComponent MetalToolchain Build failed 6/9/25, 8:31 PM 27.1 seconds Result of xcodebuild -downloadComponent MetalToolchain (after switching Xcode-beta.app with xcode-select) xcodebuild -downloadComponent MetalToolchain Beginning asset download... Downloaded asset to: /System/Library/AssetsV2/com_apple_MobileAsset_MetalToolchain/4d77809b60771042e514cfcf39662c6d1c195f7d.asset/AssetData/Restore/022-19457-035.dmg Done downloading: Metal Toolchain (17A5241c). Screenshots from Xcode Result of "Copy Information" Metal Toolchain 26.0 [com.apple.MobileAsset.MetalToolchain: 17.0 (17A5241c)] (Installed)

Hey, I've been struggling with this for some days now. I am trying to write to a sparse texture in a compute shader. I'm performing the following steps: Set up a sparse heap and create a texture from it Map the whole area of the sparse texture using updateTextureMapping(..) Overwrite every value with the value "4" in a compute shader Blit the texture to a shared buffer Assert that the values in the buffer are "4". I have a minimal example (which is still pretty long unfortunately). It works perfectly when removing the line heapDesc.type = .sparse. What am I missing? I could not find any information that writes to sparse textures are unsupported. Any help would be greatly appreciated. import Metal func sparseTexture64x64Demo() throws { // ── Metal objects guard let device = MTLCreateSystemDefaultDevice() else { throw NSError(domain: "SparseNotSupported", code: -1) } let queue = device.makeCommandQueue()! let lib = device.makeDefaultLibrary()! let pipeline = try device.makeComputePipelineState(function: lib.makeFunction(name: "addOne")!) // ── Texture descriptor let width = 64, height = 64 let format: MTLPixelFormat = .r32Uint // 4 B per texel let desc = MTLTextureDescriptor() desc.textureType = .type2D desc.pixelFormat = format desc.width = width desc.height = height desc.storageMode = .private desc.usage = [.shaderWrite, .shaderRead] // ── Sparse heap let bytesPerTile = device.sparseTileSizeInBytes let meta = device.heapTextureSizeAndAlign(descriptor: desc) let heapBytes = ((bytesPerTile + meta.size + bytesPerTile - 1) / bytesPerTile) * bytesPerTile let heapDesc = MTLHeapDescriptor() heapDesc.type = .sparse heapDesc.storageMode = .private heapDesc.size = heapBytes let heap = device.makeHeap(descriptor: heapDesc)! let tex = heap.makeTexture(descriptor: desc)! // ── CPU buffers let bytesPerPixel = MemoryLayout<UInt32>.stride let rowStride = width * bytesPerPixel let totalBytes = rowStride * height let dstBuf = device.makeBuffer(length: totalBytes, options: .storageModeShared)! let cb = queue.makeCommandBuffer()! let fence = device.makeFence()! // 2. Map the sparse tile, then signal the fence let rse = cb.makeResourceStateCommandEncoder()! rse.updateTextureMapping( tex, mode: .map, region: MTLRegionMake2D(0, 0, width, height), mipLevel: 0, slice: 0) rse.update(fence) // ← capture all work so far rse.endEncoding() let ce = cb.makeComputeCommandEncoder()! ce.waitForFence(fence) ce.setComputePipelineState(pipeline) ce.setTexture(tex, index: 0) let threadsPerTG = MTLSize(width: 8, height: 8, depth: 1) let tgCount = MTLSize(width: (width + 7) / 8, height: (height + 7) / 8, depth: 1) ce.dispatchThreadgroups(tgCount, threadsPerThreadgroup: threadsPerTG) ce.updateFence(fence) ce.endEncoding() // Blit texture into shared buffer let blit = cb.makeBlitCommandEncoder()! blit.waitForFence(fence) blit.copy( from: tex, sourceSlice: 0, sourceLevel: 0, sourceOrigin: MTLOrigin(x: 0, y: 0, z: 0), sourceSize: MTLSize(width: width, height: height, depth: 1), to: dstBuf, destinationOffset: 0, destinationBytesPerRow: rowStride, destinationBytesPerImage: totalBytes) blit.endEncoding() cb.commit() cb.waitUntilCompleted() assert(cb.error == nil, "GPU error: \(String(describing: cb.error))") // ── Verify a few texels let out = dstBuf.contents().bindMemory(to: UInt32.self, capacity: width * height) print("first three texels:", out[0], out[1], out[width]) // 0 1 64 assert(out[0] == 4 && out[1] == 4 && out[width] == 4) } Metal shader: #include <metal_stdlib> using namespace metal; kernel void addOne(texture2d<uint, access::write> tex [[texture(0)]], uint2 gid [[thread_position_in_grid]]) { tex.write(4, gid); }

hi When analyzing our game using Instruments, I've always been confused about the two items "Drawable Present" and "Drawable Presented" in the GPU column. The timing of Drawable Present seems to be when the CPU layer calls commandbuffer:present, rather than when the actual encoding is completed on the GPU. Also, what does drawable presented specifically mean? In our case, when a CPU stall occurs, it appears that the vsync interval changes in the next frame, and a surface that has already been calculated is not displayed. Why is this happening?

Hello! I'm a developer working on a plugin for the Elgato Stream Deck, called GPU Metrics. The plugin currently only works on Windows but I'd like to bring it to macOS. However, based on forum posts I've read (and StackOverflow) there isn't a very clear path to query GPU metrics like usage, temperature, used GPU memory, and power consumption. There are some tools out there that do similar things, but I wanted to see what would be the recommendation from Apple's engineering team to get this data via a public API. Requirements: Access GPU utilization, temperature, memory usage, power usage C/C++ based API for querying the metrics so I can expose the data to JavaScript via Node Addon No need to compatibile with Intel-based Macs, as Apple silicon will be fine for now Plugin GitHub Thank you! Noah

In the Creating A 3D Application With Hydra Rendering tutorial on the Apple Developer website, on the last step where I execute this command: cmake -S ~/Users/macuser/CreatingA3DApplicationWithHydraRendering/ -B ~/Users/macuser/CreatingA3DApplicationWithHydraRendering/ I keep getting an error: CMake Error at CMakeLists.txt:5 (include): include could not find requested file: /Users/macuser/USDInstall/bin/pxrConfig.cmake I've tried to follow the instructions as mentioned in the README.md file included in the project files at least 5 times as well as moving the pxrConfig.cmake file around and copying it in different folders, then executed the command and was still unsuccessful into generating the proper file expected to compile and render the HydraPlayer renderer. How do I get cmake to generate the Xcode file to create the HydraPlayer renderer?

I have run into an issue where I am trying to use atomic_float in a swift package but I cannot get things to compile because it appears that the Swift Package Manager doesn't support Metal 3 (atomic_float is Metal 3 functionality). Is there any way around this? I am using // swift-tools-version: 6.1 and my Metal code includes: #include <metal_stdlib> #include <metal_geometric> #include <metal_math> #include <metal_atomic> using namespace metal; kernel void test(device atomic_float* imageBuffer [[buffer(1)]], uint id [[ thread_position_in_grid ]]) { } But I get an error on the definition of atomic_float . Any help, one more importantly, where I could have found this information about this limitation, would be helpful. -RadBobby

Hi, seems MSL is missing support for a clock() shader instruction available in other graphics APIs like Vulkan or OpenGL for example.. useful for counting cost in number of clock cycles of some code insider shader with much finer granularity than launching a micro kernel with same instructions and measuring cycles cost from CPU.. also useful for MoltenVK to support that extensions.. thanks..

The game physics work as expected using GTPK 2.0 using Crossover 24 or Whisky. However, using GPTK 2.1 with Crossover 25, the player and camera physics misbehave. See https://www.reddit.com/r/WWEGames/comments/1jx9mph/the_siamese_elbow/ and https://www.reddit.com/r/WWEGames/comments/1jx9ow4/camera_glitch/ Full video also linked in the Reddit post. I have also submitted this bug via the feedback assistant.

The code is pretty simple kernel void naive( constant RunParams *param [[ buffer(0) ]], const device float *A [[ buffer(1) ]], // [N, K] device float *output [[ buffer(2) ]], uint2 gid [[ thread_position_in_grid ]]) { uint a_ptr = gid.x * param->K; for (uint i = 0; i < param->K; i++, a_ptr++) { val += A[b_ptr]; } output[ptr] = val; } when uint a_ptr = gid.x * param->K, the code got 150 GFLops when uint a_ptr = gid.y * param->K, the code got 860 GFLops param->K = 256; thread per group: [16, 16] I'd like to understand why the performance is so different, and how can I profile/diagnose this to help with further optimization.

View Layout Add the following views in a view controller: Label View A, with a subview of the same size: MTKView A View B, with a subview of the same size: MTKView B Refresh Rates of Each View The label view refreshes at 60fps (driven by CADisplayLink). MTKView A and B refresh at 15fps. MTKView Implementation Details The corresponding CAMetalLayer's maximumDrawableCount is set to 2, changed to double buffering. The scheduling mechanism is modified; drawing is not driven by the internal loop but is done manually. The draw call is triggered immediately upon receiving a frame. self.metalView.enableSetNeedsDisplay = NO; self.metalView.paused = YES; A new high-priority queue is created for drawing, instead of handling it on the main queue. MTKView Latency Tracking The GPU completion time T1 is observed through the addCompletedHandler callback of the CommandBuffer. The presentation time T2 of the frame is observed through the addPresentedHandler callback of the currentDrawable in MTKView. Testing shows that T2 - T1 > 16.6ms (the Vsync period at 60Hz). This means that after the GPU rendering in MTLView is finished, the frame is not actually displayed at the next Vsync instruction but only at the Vsync instruction after that. I believe there is an extra 16.6ms of latency here, which I want to eliminate by adjusting the rendering mechanism. Observation from Instruments From Instruments, the Surface presentation aligns with the above test results. After the Metal encoder finishes, the Surface in Display switches only after the next-next Vsync instruction. See the image in the link for details. Questions According to a beginner's understanding, after MTKView's GPU rendering is finished, the next Vsync instruction should officially display (make it visible). However, this is not what is observed. Does the subview MTKView need to wait for another Vsync cycle to be drawn to the actual display buffer? The label updates its text at 60fps, so the entire interface should be displayed at 60fps. Is the content of MTKView not synchronized when the display happens? Explanation of the Reasoning Behind Some MTKView Code Details Changing from the default triple buffering to double buffering helps reduce the latency introduced by rendering. Not using MTKView's own scheduling mechanism but using manual triggering of the draw method is because MTKView's own scheduling mechanism is driven by CADisplayLink. Therefore, if a frame falls within a Vsync window, it needs to wait for the next Vsync window to trigger the draw operation, which introduces waiting latency.

I'm implementing optimized matmul on metal: https://github.com/crynux-ai/metal-matmul/blob/main/metal/1_shared_mem.metal I notice that performance is significantly different with different threadgroup memory set in [computeEncoder setThreadgroupMemoryLength] All other lines are exactly same, the only difference is this parameter. Matmul performance is roughly 250 GFLops if I set 32768 (max bytes allowed on this M1 Max), but 400 GFLops if I set 8192. Why does this happen? How can I optimize it?

Hello I am trying to get thread group memory access in fragment shader. In essence, I would like to have all the fragments in a tile to bitwiseOR some value. My idea was to use simd_or across the SIMD group, then make each SIMD group thread 0 to atomic or the value into thread group memory. Finally very first thread of the tile would be tasked with writing the value down to texture with write access. Now, I can allocate the thread group memory argument to the fragment function all right. MTLRenderEncoder has setThreadgroupMemoryLength call, which I am using the following way [renderEncoder setThreagroupMemoryLength: 16 offset: 0 atIndex:0] Unfortunately, all I am getting is the following error (runtime assertion) -[MTLDebugRenderCommandEncoder setThreadgroupMemoryLength:offset:atIndex:]:3487: failed assertion Set Threadgroup Memory Length Validation offset + length(16) must be <= threadgroupMemoryLength(0).` What I am doing wrong? How I can get thread group memory in the fragment shader? I know I could use tile shading and compute function but the problem is that here I really like to use fragment stuff. Will be grateful for help.

So I've been trying out GPTK with Elite Dangerous Horizons game and it looks like from what I can tell. The VRAM keeps going up until it goes over the limit where it drops the FPS to 1-3 FPS and then crashes the game. From the Performance HUD I can see that it looks like when using GPTK, the VRAM usage just keeps climbing and I never saw it drop down at all. I did some limited testing, and from that I think I can conclude that it is probably not a VRAM leak, but it might be caching it. The reason for this is because I noticed that if I went back to the area that I've been before. It won't increase the VRAM usage. So either there is something wrong with the freeing VRAM memory part, or it could be that GPTK might not be reporting the right amount of VRAM available to use? So maybe that's why it keeps allocating VRAM until it went out of memory and crashed the game. Just to test, I did try running the game with DXVK+MoltenVK combo, and I can see that it works just fine. VRAM is being freed up when it's no longer used. Is this a known issue in some games?

I am trying to load some PNG data with MTKTextureLoader newTextureWithData，but the result shows wrong at the alpha area. Here is the code. I have an image URL, after it downloads successfully, I try to use the data or UIImagePNGRepresentation (image), they all show wrong. UIImage *tempImg = [UIImage imageWithData:data]; CGImageRef cgRef = tempImg.CGImage; MTKTextureLoader *loader = [[MTKTextureLoader alloc] initWithDevice:device]; id<MTLTexture> temp1 = [loader newTextureWithData:data options:@{MTKTextureLoaderOptionSRGB: @(NO), MTKTextureLoaderOptionTextureUsage: @(MTLTextureUsageShaderRead), MTKTextureLoaderOptionTextureCPUCacheMode: @(MTLCPUCacheModeWriteCombined)} error:nil]; NSData *tempData = UIImagePNGRepresentation(tempImg); id<MTLTexture> temp2 = [loader newTextureWithData:tempData options:@{MTKTextureLoaderOptionSRGB: @(NO), MTKTextureLoaderOptionTextureUsage: @(MTLTextureUsageShaderRead), MTKTextureLoaderOptionTextureCPUCacheMode: @(MTLCPUCacheModeWriteCombined)} error:nil]; id<MTLTexture> temp3 = [loader newTextureWithCGImage:cgRef options:@{MTKTextureLoaderOptionSRGB: @(NO), MTKTextureLoaderOptionTextureUsage: @(MTLTextureUsageShaderRead), MTKTextureLoaderOptionTextureCPUCacheMode: @(MTLCPUCacheModeWriteCombined)} error:nil]; }] resume];

I am building a MacOS desktop app (https://anukari.com) that is using Metal compute to do real-time audio/DSP processing, as I have a problem that is highly parallelizable and too computationally expensive for the CPU. However it seems that the way in which I am using the GPU, even when my app is fully compute-limited, the OS never increases the power/performance state. Because this is a real-time audio synthesis application, it's a huge problem to not be able to take advantage of the full clock speeds that the GPU is capable of, because the app can't keep up with real-time. I discovered this issue while profiling the app using Instrument's Metal tracing (and Game tracing) modes. In the profiling configuration under "Metal Application" there is a drop-down to select the "Performance State." If I run the application under Instruments with Performance State set to Maximum, it runs amazingly well, and all my problems go away. For comparison, when I run the app on its own, outside of Instruments, the expensive GPU computation it's doing takes around 2x as long to complete, meaning that the app performs half as well. I've done a ton of work to micro-optimize my Metal compute code, based on every scrap of information from the WWDC videos, etc. A problem I'm running into is that I think that the more efficient I make my code, the less it signals to the OS that I want high GPU clock speeds! I think part of why the OS is confused is that in most use cases, my computation can be done using only a small number of Metal threadgroups. I'm guessing that the OS heuristics see that only a small fraction of the GPU is saturated and fail to scale up the power/clock state. I'm not sure what to do here; I'm in a bit of a bind. One possibility is that I intentionally schedule busy work -- spin threadgroups just to waste energy and signal to the OS that I need higher clock speeds. This is obviously a really bad idea, but it might work. Is there any other (better) way for my app to signal to the OS that it is doing real-time latency-sensitive computation on the GPU and needs the clock speeds to be scaled up? Note that game mode is not really an option, as my app also runs as an AU plugin inside hosts like Garageband, so it can't be made fullscreen, etc.

In my project I need to do the following: In runtime create metal Dynamic library from source. In runtime create metal Executable library from source and Link it with my previous created Dynamic library. Create compute pipeline using those two libraries created above. But I get the following error at the third step: Error Domain=AGXMetalG15X_M1 Code=2 "Undefined symbols: _Z5noisev, referenced from: OnTheFlyKernel " UserInfo={NSLocalizedDescription=Undefined symbols: _Z5noisev, referenced from: OnTheFlyKernel } import Foundation import Metal class MetalShaderCompiler { let device = MTLCreateSystemDefaultDevice()! var pipeline: MTLComputePipelineState! func compileDylib() -> MTLDynamicLibrary { let source = """ #include <metal_stdlib> using namespace metal; half3 noise() { return half3(1, 0, 1); } """ let option = MTLCompileOptions() option.libraryType = .dynamic option.installName = "@executable_path/libFoundation.metallib" let library = try! device.makeLibrary(source: source, options: option) let dylib = try! device.makeDynamicLibrary(library: library) return dylib } func compileExlib(dylib: MTLDynamicLibrary) -> MTLLibrary { let source = """ #include <metal_stdlib> using namespace metal; extern half3 noise(); kernel void OnTheFlyKernel(texture2d<half, access::read> src [[texture(0)]], texture2d<half, access::write> dst [[texture(1)]], ushort2 gid [[thread_position_in_grid]]) { half4 rgba = src.read(gid); rgba.rgb += noise(); dst.write(rgba, gid); } """ let option = MTLCompileOptions() option.libraryType = .executable option.libraries = [dylib] let library = try! self.device.makeLibrary(source: source, options: option) return library } func runtime() { let dylib = self.compileDylib() let exlib = self.compileExlib(dylib: dylib) let pipelineDescriptor = MTLComputePipelineDescriptor() pipelineDescriptor.computeFunction = exlib.makeFunction(name: "OnTheFlyKernel") pipelineDescriptor.preloadedLibraries = [dylib] pipeline = try! device.makeComputePipelineState(descriptor: pipelineDescriptor, options: .bindingInfo, reflection: nil) } }

Hi, Introducing Swift Concurrency to my Metal app has been a bit challenging as Swift Concurrency is limited by the cooperative thread pool. GPU work is obviously not CPU bound and can block forward moving progress, especially when using waitUntilCompleted on the command buffer. For concurrent render work this has the potential of under utilizing the CPU and even creating dead locks. My question is, what is the Metal's teams general recommendation when it comes to concurrency? It seems to me that Dispatch or OperationQueues are still the preferred way for Metal bound tasks in order to gain maximum performance? To integrate with Swift Concurrency my idea is to use continuations that kick off render jobs via Dispatch or Queues? Would this be the best solution to bridge async tasks with Metal work? Thanks!