Question

我试图在Metal中实现代码，在两个长度为的向量之间执行一维卷积。我已经实现了以下正常工作

kernel void convolve(const device float *dataVector [[ buffer(0) ]],
                     const device int& dataSize [[ buffer(1) ]],
                     const device float *filterVector [[ buffer(2) ]],
                     const device int& filterSize [[ buffer(3) ]],
                     device float *outVector [[ buffer(4) ]],
                     uint id [[ thread_position_in_grid ]]) {
    int outputSize = dataSize - filterSize + 1;
    for (int i=0;i<outputSize;i++) {
        float sum = 0.0;
        for (int j=0;j<filterSize;j++) {
            sum += dataVector[i+j] * filterVector[j];
        }
        outVector[i] = sum;
    }
}

我的问题是使用Metal处理（计算+与GPU之间的数据传输）相同的数据需要大约10倍的时间，而不是CPU上的Swift。我的问题是如何用单个向量操作替换内部循环还是有另一种方法来加速上面的代码？

Answer 1

在这种情况下利用GPU并行性的关键是让它为你管理外部循环。我们不会为整个数据向量调用一次内核，而是为数据向量中的每个元素调用它。内核函数简化了这个：

kernel void convolve(const device float *dataVector [[ buffer(0) ]],
                     const constant int &dataSize [[ buffer(1) ]],
                     const constant float *filterVector [[ buffer(2) ]],
                     const constant int &filterSize [[ buffer(3) ]],
                     device float *outVector [[ buffer(4) ]],
                     uint id [[ thread_position_in_grid ]])
{
    float sum = 0.0;
    for (int i = 0; i < filterSize; ++i) {
        sum += dataVector[id + i] * filterVector[i];
    }
    outVector[id] = sum;
}

为了分派这项工作，我们根据计算管道状态建议的线程执行宽度选择一个线程组大小。这里一个棘手的问题是确保输入和输出缓冲区中有足够的填充，这样我们就可以稍微超出数据的实际大小。这确实会导致我们浪费少量的内存和计算，但是为了节省我们执行单独调度的复杂性，只是为了计算缓冲区末尾元素的卷积。

// We should ensure here that the data buffer and output buffer each have a size that is a multiple of
// the compute pipeline's threadExecutionWidth, by padding the amount we allocate for each of them.
// After execution, we ignore the extraneous elements in the output buffer beyond the first (dataCount - filterCount + 1).

let iterationCount = dataCount - filterCount + 1
let threadsPerThreadgroup = MTLSize(width: min(iterationCount, computePipeline.threadExecutionWidth), height: 1, depth: 1)
let threadgroups = (iterationCount + threadsPerThreadgroup.width - 1) / threadsPerThreadgroup.width
let threadgroupsPerGrid = MTLSize(width: threadgroups, height: 1, depth: 1)

let commandEncoder = commandBuffer.computeCommandEncoder()
commandEncoder.setComputePipelineState(computePipeline)
commandEncoder.setBuffer(dataBuffer, offset: 0, at: 0)
commandEncoder.setBytes(&dataCount, length: MemoryLayout<Int>.stride, at: 1)
commandEncoder.setBuffer(filterBuffer, offset: 0, at: 2)
commandEncoder.setBytes(&filterCount, length: MemoryLayout<Int>.stride, at: 3)
commandEncoder.setBuffer(outBuffer, offset: 0, at: 4)
commandEncoder.dispatchThreadgroups(threadgroupsPerGrid, threadsPerThreadgroup: threadsPerThreadgroup)
commandEncoder.endEncoding()

在我的实验中，这种并行方法比问题中的串行版本运行快400-1000倍。我很想知道它与你的CPU实现相比如何。

Answer 2

以下代码显示了如何使用Objective-C Metal API在GPU上并行渲染编码命令（上面的线程代码仅将输出的渲染划分为网格部分以进行并行处理;计算仍未并行执行）。这是您在问题中所指的内容，即使它并不完全符合您的要求。我已经提供了这个答案，以帮助任何可能偶然发现这个问题的人，认为它会提供与并行渲染相关的答案（事实上，事实上并非如此）：

    - (void)drawInMTKView:(MTKView *)view
    {
        dispatch_async(((AppDelegate *)UIApplication.sharedApplication.delegate).cameraViewQueue, ^{
                    id <CAMetalDrawable> drawable = [view currentDrawable]; //[(CAMetalLayer *)view.layer nextDrawable];
                    MTLRenderPassDescriptor *renderPassDesc = [view currentRenderPassDescriptor];
                    renderPassDesc.colorAttachments[0].loadAction = MTLLoadActionClear;
                    renderPassDesc.colorAttachments[0].clearColor = MTLClearColorMake(0.0,0.0,0.0,1.0);
                    renderPassDesc.renderTargetWidth = self.texture.width;
                    renderPassDesc.renderTargetHeight = self.texture.height;
                    renderPassDesc.colorAttachments[0].texture = drawable.texture;
                    if (renderPassDesc != nil)
                    {
                        dispatch_semaphore_wait(self._inflight_semaphore, DISPATCH_TIME_FOREVER);
                        id <MTLCommandBuffer> commandBuffer = [self.metalContext.commandQueue commandBuffer];
                        [commandBuffer enqueue];
            // START PARALLEL RENDERING OPERATIONS HERE
                        id <MTLParallelRenderCommandEncoder> parallelRCE = [commandBuffer parallelRenderCommandEncoderWithDescriptor:renderPassDesc];
// FIRST PARALLEL RENDERING OPERATION
                        id <MTLRenderCommandEncoder> renderEncoder = [parallelRCE renderCommandEncoder];

                        [renderEncoder setRenderPipelineState:self.metalContext.renderPipelineState];

                        [renderEncoder setVertexBuffer:self.metalContext.vertexBuffer offset:0 atIndex:0];
                        [renderEncoder setVertexBuffer:self.metalContext.uniformBuffer offset:0 atIndex:1];

                        [renderEncoder setFragmentBuffer:self.metalContext.uniformBuffer offset:0 atIndex:0];

                        [renderEncoder setFragmentTexture:self.texture
                                                  atIndex:0];

                        [renderEncoder drawPrimitives:MTLPrimitiveTypeTriangleStrip
                                          vertexStart:0
                                          vertexCount:4
                                        instanceCount:1];

                        [renderEncoder endEncoding];
            // ADD SECOND, THIRD, ETC. PARALLEL RENDERING OPERATION HERE
.
.
.
// SUBMIT ALL RENDERING OPERATIONS IN PARALLEL HERE
                        [parallelRCE endEncoding];

                        __block dispatch_semaphore_t block_sema = self._inflight_semaphore;
                        [commandBuffer addCompletedHandler:^(id<MTLCommandBuffer> buffer) {
                            dispatch_semaphore_signal(block_sema);

                        }];

                        if (drawable)
                            [commandBuffer presentDrawable:drawable];
                        [commandBuffer commit];
                        [commandBuffer waitUntilScheduled];
                    }
        });
    }

在上面的示例中，您将为要并行执行的每个计算复制与renderEncoder相关的内容。我没有看到这对你的代码示例有什么好处，因为一个操作似乎依赖于另一个操作。那么，你可能希望的最好的是warrenm提供给你的代码，尽管这并不是真正有资格作为并行渲染的。

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