Greetings! I have been battling with a bit of a tough issue. My use case is running a pixelwise regression model on a 2D array of images using CIImageProcessorKernel and a custom Metal Shader. It mostly works great, but the issue that arises is that if the regression calculation in Metal takes too long, an error occurs and the resulting output texture has strange artifacts, for example: The specific error is: Error excuting command buffer = Error Domain=MTLCommandBufferErrorDomain Code=1 "Internal Error (0000000e:Internal Error)" UserInfo={NSLocalizedDescription=Internal Error (0000000e:Internal Error), NSUnderlyingError=0x60000320ca20 {Error Domain=IOGPUCommandQueueErrorDomain Code=14 "(null)"}} (com.apple.CoreImage) There are multiple levels of concurrency: Swift Concurrency calling the Core Image code (which shouldn't have an impact) and of course the Metal command buffer. Is there anyway to ensure the compute command encoder can complete its work? Here is the full implementation of my CIImageProcessorKernel subclass: class ParametricKernel: CIImageProcessorKernel { static let device = MTLCreateSystemDefaultDevice()! override class var outputFormat: CIFormat { return .BGRA8 } override class func formatForInput(at input: Int32) -> CIFormat { return .BGRA8 } override class func process(with inputs: [CIImageProcessorInput]?, arguments: [String : Any]?, output: CIImageProcessorOutput) throws { guard let commandBuffer = output.metalCommandBuffer, let images = arguments?["images"] as? [CGImage], let mask = arguments?["mask"] as? CGImage, let fillTime = arguments?["fillTime"] as? CGFloat, let betaLimit = arguments?["betaLimit"] as? CGFloat, let alphaLimit = arguments?["alphaLimit"] as? CGFloat, let errorScaling = arguments?["errorScaling"] as? CGFloat, let timing = arguments?["timing"], let TTRThreshold = arguments?["ttrthreshold"] as? CGFloat, let input = inputs?.first, let sourceTexture = input.metalTexture, let destinationTexture = output.metalTexture else { return } guard let kernelFunction = device.makeDefaultLibrary()?.makeFunction(name: "parametric") else { return } guard let commandEncoder = commandBuffer.makeComputeCommandEncoder() else { return } let imagesTexture = Texture.textureFromImages(images) let pipelineState = try device.makeComputePipelineState(function: kernelFunction) commandEncoder.setComputePipelineState(pipelineState) commandEncoder.setTexture(imagesTexture, index: 0) let maskTexture = Texture.textureFromImages([mask]) commandEncoder.setTexture(maskTexture, index: 1) commandEncoder.setTexture(destinationTexture, index: 2) var errorScalingFloat = Float(errorScaling) let errorBuffer = device.makeBuffer(bytes: &errorScalingFloat, length: MemoryLayout<Float>.size, options: []) commandEncoder.setBuffer(errorBuffer, offset: 0, index: 1) // Other buffers omitted.... let threadsPerThreadgroup = MTLSizeMake(16, 16, 1) let width = Int(ceil(Float(sourceTexture.width) / Float(threadsPerThreadgroup.width))) let height = Int(ceil(Float(sourceTexture.height) / Float(threadsPerThreadgroup.height))) let threadGroupCount = MTLSizeMake(width, height, 1) commandEncoder.dispatchThreadgroups(threadGroupCount, threadsPerThreadgroup: threadsPerThreadgroup) commandEncoder.endEncoding() } }