计算GPU上的彩色像素 - 理论

Question

我有一个128 x 128像素的图像.

它分为8乘8格.

每个网格块包含16乘16像素.

需求

我想计算我的图像包含多少个黑色像素.

直截了当的方式:

我可以通过逐行,逐列,整个图像并检查像素是否为黑色来做到这一点.

GPU的方式

...但我想知道如果使用GPU,我可以将图像分解为块/块并计算每个块中的所有像素,然后对结果求和.

例如:

如果你看一下图片的左上角:

第一个块,'A1'(行A,列1)包含一个16乘16像素的网格,我知道通过手动计算,有16个黑色像素.

第二个块:'A2',(行A,列2)包含一个16乘16像素的网格,我知道通过手动计算,有62个黑色像素.

此示例的所有其他块都为空/空.

如果我通过我的程序运行我的图像,我应该得到答案:16 + 62 = 78黑色像素.

推理

据我所知,GPU可以并行运行大量数据,有效地在分布在多个GPU线程上的大量数据上运行一个小程序.我并不担心速度/性能,我只是想知道这是GPU可以/可以做的事情吗？

Answer 1

实际上，通用 GPU（例如 A8 以后的 Apple 设备中的 GPU）不仅能够解决此类并行数据处理问题，而且还旨在解决此类问题。

Apple 在他们的平台中引入了使用 Metal 的数据并行处理，通过一些简单的代码，你可以使用 GPU 解决像你这样的问题。即使这也可以使用其他框架来完成，我将包含一些 Metal+Swift 案例的代码作为概念证明。

以下内容在 OS X Sierra 上作为 Swift 命令行工具运行，并使用 Xcode 9 构建（是的，我知道它是测试版）。你可以从我的github repo获取完整的项目。

作为main.swift：

import Foundation
import Metal
import CoreGraphics
import AppKit

guard FileManager.default.fileExists(atPath: "./testImage.png") else {
    print("./testImage.png does not exist")
    exit(1)
}

let url = URL(fileURLWithPath: "./testImage.png")
let imageData = try Data(contentsOf: url)

guard let image = NSImage(data: imageData),
    let imageRef = image.cgImage(forProposedRect: nil, context: nil, hints: nil) else {
    print("Failed to load image data")
    exit(1)
}

let bytesPerPixel = 4
let bytesPerRow = bytesPerPixel * imageRef.width

var rawData = [UInt8](repeating: 0, count: Int(bytesPerRow * imageRef.height))

let bitmapInfo = CGBitmapInfo(rawValue: CGImageAlphaInfo.premultipliedFirst.rawValue).union(.byteOrder32Big)
let colorSpace = CGColorSpaceCreateDeviceRGB()

let context = CGContext(data: &rawData,
                        width: imageRef.width,
                        height: imageRef.height,
                        bitsPerComponent: 8,
                        bytesPerRow: bytesPerRow,
                        space: colorSpace,
                        bitmapInfo: bitmapInfo.rawValue)

let fullRect = CGRect(x: 0, y: 0, width: CGFloat(imageRef.width), height: CGFloat(imageRef.height))
context?.draw(imageRef, in: fullRect, byTiling: false)

// Get access to iPhone or iPad GPU
guard let device = MTLCreateSystemDefaultDevice() else {
    exit(1)
}

let textureDescriptor = MTLTextureDescriptor.texture2DDescriptor(
    pixelFormat: .rgba8Unorm,
    width: Int(imageRef.width),
    height: Int(imageRef.height),
    mipmapped: true)

let texture = device.makeTexture(descriptor: textureDescriptor)

let region = MTLRegionMake2D(0, 0, Int(imageRef.width), Int(imageRef.height))
texture.replace(region: region, mipmapLevel: 0, withBytes: &rawData, bytesPerRow: Int(bytesPerRow))

// Queue to handle an ordered list of command buffers
let commandQueue = device.makeCommandQueue()

// Buffer for storing encoded commands that are sent to GPU
let commandBuffer = commandQueue.makeCommandBuffer()

// Access to Metal functions that are stored in Shaders.metal file, e.g. sigmoid()
guard let defaultLibrary = device.makeDefaultLibrary() else {
    print("Failed to create default metal shader library")
    exit(1)
}

// Encoder for GPU commands
let computeCommandEncoder = commandBuffer.makeComputeCommandEncoder()

// hardcoded to 16 for now (recommendation: read about threadExecutionWidth)
var threadsPerGroup = MTLSize(width:16, height:16, depth:1)
var numThreadgroups = MTLSizeMake(texture.width / threadsPerGroup.width,
                                  texture.height / threadsPerGroup.height,
                                  1);

// b. set up a compute pipeline with Sigmoid function and add it to encoder
let countBlackProgram = defaultLibrary.makeFunction(name: "countBlack")
let computePipelineState = try device.makeComputePipelineState(function: countBlackProgram!)
computeCommandEncoder.setComputePipelineState(computePipelineState)


// set the input texture for the countBlack() function, e.g. inArray
// atIndex: 0 here corresponds to texture(0) in the countBlack() function
computeCommandEncoder.setTexture(texture, index: 0)

// create the output vector for the countBlack() function, e.g. counter
// atIndex: 1 here corresponds to buffer(0) in the Sigmoid function
var counterBuffer = device.makeBuffer(length: MemoryLayout<UInt32>.size,
                                        options: .storageModeShared)
computeCommandEncoder.setBuffer(counterBuffer, offset: 0, index: 0)

computeCommandEncoder.dispatchThreadgroups(numThreadgroups, threadsPerThreadgroup: threadsPerGroup)

computeCommandEncoder.endEncoding()
commandBuffer.commit()
commandBuffer.waitUntilCompleted()

// a. Get GPU data
// outVectorBuffer.contents() returns UnsafeMutablePointer roughly equivalent to char* in C
var data = NSData(bytesNoCopy: counterBuffer.contents(),
                  length: MemoryLayout<UInt32>.size,
                  freeWhenDone: false)
// b. prepare Swift array large enough to receive data from GPU
var finalResultArray = [UInt32](repeating: 0, count: 1)

// c. get data from GPU into Swift array
data.getBytes(&finalResultArray, length: MemoryLayout<UInt>.size)

print("Found \(finalResultArray[0]) non-white pixels")

// d. YOU'RE ALL SET!

此外，在Shaders.metal：

#include <metal_stdlib>
using namespace metal;

kernel void
countBlack(texture2d<float, access::read> inArray [[texture(0)]],
           volatile device uint *counter [[buffer(0)]],
           uint2 gid [[thread_position_in_grid]]) {

    // Atomic as we need to sync between threadgroups
    device atomic_uint *atomicBuffer = (device atomic_uint *)counter;
    float3 inColor = inArray.read(gid).rgb;
    if(inColor.r != 1.0 || inColor.g != 1.0 || inColor.b != 1.0) {
        atomic_fetch_add_explicit(atomicBuffer, 1, memory_order_relaxed);
    }
}

我用这个问题来了解一些关于 Metal 和数据并行计算的知识，所以大部分代码都被用作在线文章和编辑的样板。请花时间访问下面提到的资源以获取更多示例。此外，针对这个特定问题的代码几乎是硬编码的，但是您在调整它时应该不会遇到很多麻烦。

资料来源：

http://flexmonkey.blogspot.com.ar/2016/05/histogram-equalisation-with-metal.html

http://metalbyexample.com/introduction-to-compute/

http://memkite.com/blog/2014/12/15/data-parallel-programming-with-metal-and-swift-for-iphoneipad-gpu/