与 metal swift 并行计算数组值的总和
Compute sum of array values in parallel with metal swift
我正在尝试计算与金属并行的大型数组的总和 swift。
有没有大神的办法？
我的思路是把数组分成子数组，并行计算一个子数组的和，然后并行计算完成后计算子数组的和。
例如，如果我有
array = [a0,....an]
我将数组分成子数组：
array_1 = [a_0,...a_i],
array_2 = [a_i+1,...a_2i],
....
array_n/i = [a_n-1, ... a_n]
这个数组的总和是并行计算的，我得到
sum_1, sum_2, sum_3, ... sum_n/1
最后只计算子和的总和。
我创建了 运行 我的金属着色器的应用程序，但有些事情我不太明白。
var array:[[Float]] = [[1,2,3], [4,5,6], [7,8,9]]
// get device
let device: MTLDevice! = MTLCreateSystemDefaultDevice()
// get library
let defaultLibrary:MTLLibrary! = device.newDefaultLibrary()
// queue
let commandQueue:MTLCommandQueue! = device.newCommandQueue()
// function
let kernerFunction: MTLFunction! = defaultLibrary.newFunctionWithName("calculateSum")
// pipeline with function
let pipelineState: MTLComputePipelineState! = try device.newComputePipelineStateWithFunction(kernerFunction)
// buffer for function
let commandBuffer:MTLCommandBuffer! = commandQueue.commandBuffer()
// encode function
let commandEncoder:MTLComputeCommandEncoder = commandBuffer.computeCommandEncoder()
// add function to encode
commandEncoder.setComputePipelineState(pipelineState)
// options
let resourceOption = MTLResourceOptions()
let arrayBiteLength = array.count * array[0].count * sizeofValue(array[0][0])
let arrayBuffer = device.newBufferWithBytes(&array, length: arrayBiteLength, options: resourceOption)
commandEncoder.setBuffer(arrayBuffer, offset: 0, atIndex: 0)
var result:[Float] = [0,0,0]
let resultBiteLenght = sizeofValue(result[0])
let resultBuffer = device.newBufferWithBytes(&result, length: resultBiteLenght, options: resourceOption)
commandEncoder.setBuffer(resultBuffer, offset: 0, atIndex: 1)
let threadGroupSize = MTLSize(width: 1, height: 1, depth: 1)
let threadGroups = MTLSize(width: (array.count), height: 1, depth: 1)
commandEncoder.dispatchThreadgroups(threadGroups, threadsPerThreadgroup: threadGroupSize)
commandEncoder.endEncoding()
commandBuffer.commit()
commandBuffer.waitUntilCompleted()
let data = NSData(bytesNoCopy: resultBuffer.contents(), length: sizeof(Float), freeWhenDone: false)
data.getBytes(&result, length: result.count * sizeof(Float))
print(result)
是我的Swift代码，
我的着色器是：
kernel void calculateSum(const device float *inFloat [[buffer(0)]],
device float *result [[buffer(1)]],
uint id [[ thread_position_in_grid ]]) {
float * f = inFloat[id];
float sum = 0;
for (int i = 0 ; i < 3 ; ++i) {
sum = sum + f[i];
}
result = sum;
}
我不知道如何定义inFloat是array of array。
我不知道什么是 threadGroupSize 和 threadGroups。
我不知道着色器属性中的 device 和 uint 是什么。
这是正确的方法吗？
我花时间用 Metal 创建了这个问题的完整示例。解释在评论里：
let count = 10_000_000
let elementsPerSum = 10_000
// Data type, has to be the same as in the shader
typealias DataType = CInt
let device = MTLCreateSystemDefaultDevice()!
let library = self.library(device: device)
let parsum = library.makeFunction(name: "parsum")!
let pipeline = try! device.makeComputePipelineState(function: parsum)
// Our data, randomly generated:
var data = (0..<count).map{ _ in DataType(arc4random_uniform(100)) }
var dataCount = CUnsignedInt(count)
var elementsPerSumC = CUnsignedInt(elementsPerSum)
// Number of individual results = count / elementsPerSum (rounded up):
let resultsCount = (count + elementsPerSum - 1) / elementsPerSum
// Our data in a buffer (copied):
let dataBuffer = device.makeBuffer(bytes: &data, length: MemoryLayout<DataType>.stride * count, options: [])!
// A buffer for individual results (zero initialized)
let resultsBuffer = device.makeBuffer(length: MemoryLayout<DataType>.stride * resultsCount, options: [])!
// Our results in convenient form to compute the actual result later:
let pointer = resultsBuffer.contents().bindMemory(to: DataType.self, capacity: resultsCount)
let results = UnsafeBufferPointer<DataType>(start: pointer, count: resultsCount)
let queue = device.makeCommandQueue()!
let cmds = queue.makeCommandBuffer()!
let encoder = cmds.makeComputeCommandEncoder()!
encoder.setComputePipelineState(pipeline)
encoder.setBuffer(dataBuffer, offset: 0, index: 0)
encoder.setBytes(&dataCount, length: MemoryLayout<CUnsignedInt>.size, index: 1)
encoder.setBuffer(resultsBuffer, offset: 0, index: 2)
encoder.setBytes(&elementsPerSumC, length: MemoryLayout<CUnsignedInt>.size, index: 3)
// We have to calculate the sum `resultCount` times => amount of threadgroups is `resultsCount` / `threadExecutionWidth` (rounded up) because each threadgroup will process `threadExecutionWidth` threads
let threadgroupsPerGrid = MTLSize(width: (resultsCount + pipeline.threadExecutionWidth - 1) / pipeline.threadExecutionWidth, height: 1, depth: 1)
// Here we set that each threadgroup should process `threadExecutionWidth` threads, the only important thing for performance is that this number is a multiple of `threadExecutionWidth` (here 1 times)
let threadsPerThreadgroup = MTLSize(width: pipeline.threadExecutionWidth, height: 1, depth: 1)
encoder.dispatchThreadgroups(threadgroupsPerGrid, threadsPerThreadgroup: threadsPerThreadgroup)
encoder.endEncoding()
var start, end : UInt64
var result : DataType = 0
start = mach_absolute_time()
cmds.commit()
cmds.waitUntilCompleted()
for elem in results {
result += elem
}
end = mach_absolute_time()
print("Metal result: \(result), time: \(Double(end - start) / Double(NSEC_PER_SEC))")
result = 0
start = mach_absolute_time()
data.withUnsafeBufferPointer { buffer in
for elem in buffer {
result += elem
}
}
end = mach_absolute_time()
print("CPU result: \(result), time: \(Double(end - start) / Double(NSEC_PER_SEC))")
我用 Mac 对其进行了测试，但它应该在 iOS 上运行良好。
输出：
Metal result: 494936505, time: 0.024611456
CPU result: 494936505, time: 0.163341018
Metal 版本大约快 7 倍。我敢肯定，如果您实施分而治之的分而治之之类的方法或其他方法，您可以获得更快的速度。
我已经 运行 使用了该应用程序。在 gt 740（384 核）与 i7-4790 上，具有多线程向量和实现，这是我的数据：
Metal lap time: 19.959092
cpu MT lap time: 4.353881
这是 cpu 的 5/1 比率，
所以除非你有一个强大的 gpu 使用着色器是不值得的。
我一直在带有 igpu intel hd 4000 的 i7-3610qm 上测试相同的代码，令人惊讶的是金属的结果要好得多：2/1
已编辑：在调整线程参数后，我终于提高了 gpu 性能，现在高达 16xcpu
令人讨厌的是，接受的答案缺少为其编写的内核。 The source is here，但这里是完整的程序和着色器，可以 运行 作为 swift 命令行应用程序。
/*
* Command line Metal Compute Shader for data processing
*/
import Metal
import Foundation
//------------------------------------------------------------------------------
let count = 10_000_000
let elementsPerSum = 10_000
//------------------------------------------------------------------------------
typealias DataType = CInt // Data type, has to be the same as in the shader
//------------------------------------------------------------------------------
let device = MTLCreateSystemDefaultDevice()!
let library = device.makeDefaultLibrary()!
let parsum = library.makeFunction(name: "parsum")!
let pipeline = try! device.makeComputePipelineState(function: parsum)
//------------------------------------------------------------------------------
// Our data, randomly generated:
var data = (0..<count).map{ _ in DataType(arc4random_uniform(100)) }
var dataCount = CUnsignedInt(count)
var elementsPerSumC = CUnsignedInt(elementsPerSum)
// Number of individual results = count / elementsPerSum (rounded up):
let resultsCount = (count + elementsPerSum - 1) / elementsPerSum
//------------------------------------------------------------------------------
// Our data in a buffer (copied):
let dataBuffer = device.makeBuffer(bytes: &data, length: MemoryLayout<DataType>.stride * count, options: [])!
// A buffer for individual results (zero initialized)
let resultsBuffer = device.makeBuffer(length: MemoryLayout<DataType>.stride * resultsCount, options: [])!
// Our results in convenient form to compute the actual result later:
let pointer = resultsBuffer.contents().bindMemory(to: DataType.self, capacity: resultsCount)
let results = UnsafeBufferPointer<DataType>(start: pointer, count: resultsCount)
//------------------------------------------------------------------------------
let queue = device.makeCommandQueue()!
let cmds = queue.makeCommandBuffer()!
let encoder = cmds.makeComputeCommandEncoder()!
//------------------------------------------------------------------------------
encoder.setComputePipelineState(pipeline)
encoder.setBuffer(dataBuffer, offset: 0, index: 0)
encoder.setBytes(&dataCount, length: MemoryLayout<CUnsignedInt>.size, index: 1)
encoder.setBuffer(resultsBuffer, offset: 0, index: 2)
encoder.setBytes(&elementsPerSumC, length: MemoryLayout<CUnsignedInt>.size, index: 3)
//------------------------------------------------------------------------------
// We have to calculate the sum `resultCount` times => amount of threadgroups is `resultsCount` / `threadExecutionWidth` (rounded up) because each threadgroup will process `threadExecutionWidth` threads
let threadgroupsPerGrid = MTLSize(width: (resultsCount + pipeline.threadExecutionWidth - 1) / pipeline.threadExecutionWidth, height: 1, depth: 1)
// Here we set that each threadgroup should process `threadExecutionWidth` threads, the only important thing for performance is that this number is a multiple of `threadExecutionWidth` (here 1 times)
let threadsPerThreadgroup = MTLSize(width: pipeline.threadExecutionWidth, height: 1, depth: 1)
//------------------------------------------------------------------------------
encoder.dispatchThreadgroups(threadgroupsPerGrid, threadsPerThreadgroup: threadsPerThreadgroup)
encoder.endEncoding()
//------------------------------------------------------------------------------
var start, end : UInt64
var result : DataType = 0
//------------------------------------------------------------------------------
start = mach_absolute_time()
cmds.commit()
cmds.waitUntilCompleted()
for elem in results {
result += elem
}
end = mach_absolute_time()
//------------------------------------------------------------------------------
print("Metal result: \(result), time: \(Double(end - start) / Double(NSEC_PER_SEC))")
//------------------------------------------------------------------------------
result = 0
start = mach_absolute_time()
data.withUnsafeBufferPointer { buffer in
for elem in buffer {
result += elem
}
}
end = mach_absolute_time()
print("CPU result: \(result), time: \(Double(end - start) / Double(NSEC_PER_SEC))")
//------------------------------------------------------------------------------
#include <metal_stdlib>
using namespace metal;
typedef unsigned int uint;
typedef int DataType;
kernel void parsum(const device DataType* data [[ buffer(0) ]],
const device uint& dataLength [[ buffer(1) ]],
device DataType* sums [[ buffer(2) ]],
const device uint& elementsPerSum [[ buffer(3) ]],
const uint tgPos [[ threadgroup_position_in_grid ]],
const uint tPerTg [[ threads_per_threadgroup ]],
const uint tPos [[ thread_position_in_threadgroup ]]) {
uint resultIndex = tgPos * tPerTg + tPos;
uint dataIndex = resultIndex * elementsPerSum; // Where the summation should begin
uint endIndex = dataIndex + elementsPerSum < dataLength ? dataIndex + elementsPerSum : dataLength; // The index where summation should end
for (; dataIndex < endIndex; dataIndex++)
sums[resultIndex] += data[dataIndex];
}
我正在尝试计算与金属并行的大型数组的总和 swift。
有没有大神的办法？
我的思路是把数组分成子数组，并行计算一个子数组的和，然后并行计算完成后计算子数组的和。
例如，如果我有
array = [a0,....an]
我将数组分成子数组：
array_1 = [a_0,...a_i],
array_2 = [a_i+1,...a_2i],
....
array_n/i = [a_n-1, ... a_n]
这个数组的总和是并行计算的，我得到
sum_1, sum_2, sum_3, ... sum_n/1
最后只计算子和的总和。
我创建了 运行 我的金属着色器的应用程序，但有些事情我不太明白。
var array:[[Float]] = [[1,2,3], [4,5,6], [7,8,9]]
// get device
let device: MTLDevice! = MTLCreateSystemDefaultDevice()
// get library
let defaultLibrary:MTLLibrary! = device.newDefaultLibrary()
// queue
let commandQueue:MTLCommandQueue! = device.newCommandQueue()
// function
let kernerFunction: MTLFunction! = defaultLibrary.newFunctionWithName("calculateSum")
// pipeline with function
let pipelineState: MTLComputePipelineState! = try device.newComputePipelineStateWithFunction(kernerFunction)
// buffer for function
let commandBuffer:MTLCommandBuffer! = commandQueue.commandBuffer()
// encode function
let commandEncoder:MTLComputeCommandEncoder = commandBuffer.computeCommandEncoder()
// add function to encode
commandEncoder.setComputePipelineState(pipelineState)
// options
let resourceOption = MTLResourceOptions()
let arrayBiteLength = array.count * array[0].count * sizeofValue(array[0][0])
let arrayBuffer = device.newBufferWithBytes(&array, length: arrayBiteLength, options: resourceOption)
commandEncoder.setBuffer(arrayBuffer, offset: 0, atIndex: 0)
var result:[Float] = [0,0,0]
let resultBiteLenght = sizeofValue(result[0])
let resultBuffer = device.newBufferWithBytes(&result, length: resultBiteLenght, options: resourceOption)
commandEncoder.setBuffer(resultBuffer, offset: 0, atIndex: 1)
let threadGroupSize = MTLSize(width: 1, height: 1, depth: 1)
let threadGroups = MTLSize(width: (array.count), height: 1, depth: 1)
commandEncoder.dispatchThreadgroups(threadGroups, threadsPerThreadgroup: threadGroupSize)
commandEncoder.endEncoding()
commandBuffer.commit()
commandBuffer.waitUntilCompleted()
let data = NSData(bytesNoCopy: resultBuffer.contents(), length: sizeof(Float), freeWhenDone: false)
data.getBytes(&result, length: result.count * sizeof(Float))
print(result)
是我的Swift代码，
我的着色器是：
kernel void calculateSum(const device float *inFloat [[buffer(0)]],
device float *result [[buffer(1)]],
uint id [[ thread_position_in_grid ]]) {
float * f = inFloat[id];
float sum = 0;
for (int i = 0 ; i < 3 ; ++i) {
sum = sum + f[i];
}
result = sum;
}
我不知道如何定义inFloat是array of array。 我不知道什么是 threadGroupSize 和 threadGroups。 我不知道着色器属性中的 device 和 uint 是什么。
这是正确的方法吗？
我花时间用 Metal 创建了这个问题的完整示例。解释在评论里：
let count = 10_000_000
let elementsPerSum = 10_000
// Data type, has to be the same as in the shader
typealias DataType = CInt
let device = MTLCreateSystemDefaultDevice()!
let library = self.library(device: device)
let parsum = library.makeFunction(name: "parsum")!
let pipeline = try! device.makeComputePipelineState(function: parsum)
// Our data, randomly generated:
var data = (0..<count).map{ _ in DataType(arc4random_uniform(100)) }
var dataCount = CUnsignedInt(count)
var elementsPerSumC = CUnsignedInt(elementsPerSum)
// Number of individual results = count / elementsPerSum (rounded up):
let resultsCount = (count + elementsPerSum - 1) / elementsPerSum
// Our data in a buffer (copied):
let dataBuffer = device.makeBuffer(bytes: &data, length: MemoryLayout<DataType>.stride * count, options: [])!
// A buffer for individual results (zero initialized)
let resultsBuffer = device.makeBuffer(length: MemoryLayout<DataType>.stride * resultsCount, options: [])!
// Our results in convenient form to compute the actual result later:
let pointer = resultsBuffer.contents().bindMemory(to: DataType.self, capacity: resultsCount)
let results = UnsafeBufferPointer<DataType>(start: pointer, count: resultsCount)
let queue = device.makeCommandQueue()!
let cmds = queue.makeCommandBuffer()!
let encoder = cmds.makeComputeCommandEncoder()!
encoder.setComputePipelineState(pipeline)
encoder.setBuffer(dataBuffer, offset: 0, index: 0)
encoder.setBytes(&dataCount, length: MemoryLayout<CUnsignedInt>.size, index: 1)
encoder.setBuffer(resultsBuffer, offset: 0, index: 2)
encoder.setBytes(&elementsPerSumC, length: MemoryLayout<CUnsignedInt>.size, index: 3)
// We have to calculate the sum `resultCount` times => amount of threadgroups is `resultsCount` / `threadExecutionWidth` (rounded up) because each threadgroup will process `threadExecutionWidth` threads
let threadgroupsPerGrid = MTLSize(width: (resultsCount + pipeline.threadExecutionWidth - 1) / pipeline.threadExecutionWidth, height: 1, depth: 1)
// Here we set that each threadgroup should process `threadExecutionWidth` threads, the only important thing for performance is that this number is a multiple of `threadExecutionWidth` (here 1 times)
let threadsPerThreadgroup = MTLSize(width: pipeline.threadExecutionWidth, height: 1, depth: 1)
encoder.dispatchThreadgroups(threadgroupsPerGrid, threadsPerThreadgroup: threadsPerThreadgroup)
encoder.endEncoding()
var start, end : UInt64
var result : DataType = 0
start = mach_absolute_time()
cmds.commit()
cmds.waitUntilCompleted()
for elem in results {
result += elem
}
end = mach_absolute_time()
print("Metal result: \(result), time: \(Double(end - start) / Double(NSEC_PER_SEC))")
result = 0
start = mach_absolute_time()
data.withUnsafeBufferPointer { buffer in
for elem in buffer {
result += elem
}
}
end = mach_absolute_time()
print("CPU result: \(result), time: \(Double(end - start) / Double(NSEC_PER_SEC))")
我用 Mac 对其进行了测试，但它应该在 iOS 上运行良好。
输出：
Metal result: 494936505, time: 0.024611456
CPU result: 494936505, time: 0.163341018
Metal 版本大约快 7 倍。我敢肯定，如果您实施分而治之的分而治之之类的方法或其他方法，您可以获得更快的速度。
我已经 运行 使用了该应用程序。在 gt 740（384 核）与 i7-4790 上，具有多线程向量和实现，这是我的数据：
Metal lap time: 19.959092
cpu MT lap time: 4.353881
这是 cpu 的 5/1 比率， 所以除非你有一个强大的 gpu 使用着色器是不值得的。
我一直在带有 igpu intel hd 4000 的 i7-3610qm 上测试相同的代码，令人惊讶的是金属的结果要好得多：2/1
已编辑：在调整线程参数后，我终于提高了 gpu 性能，现在高达 16xcpu
令人讨厌的是，接受的答案缺少为其编写的内核。 The source is here，但这里是完整的程序和着色器，可以 运行 作为 swift 命令行应用程序。
/*
* Command line Metal Compute Shader for data processing
*/
import Metal
import Foundation
//------------------------------------------------------------------------------
let count = 10_000_000
let elementsPerSum = 10_000
//------------------------------------------------------------------------------
typealias DataType = CInt // Data type, has to be the same as in the shader
//------------------------------------------------------------------------------
let device = MTLCreateSystemDefaultDevice()!
let library = device.makeDefaultLibrary()!
let parsum = library.makeFunction(name: "parsum")!
let pipeline = try! device.makeComputePipelineState(function: parsum)
//------------------------------------------------------------------------------
// Our data, randomly generated:
var data = (0..<count).map{ _ in DataType(arc4random_uniform(100)) }
var dataCount = CUnsignedInt(count)
var elementsPerSumC = CUnsignedInt(elementsPerSum)
// Number of individual results = count / elementsPerSum (rounded up):
let resultsCount = (count + elementsPerSum - 1) / elementsPerSum
//------------------------------------------------------------------------------
// Our data in a buffer (copied):
let dataBuffer = device.makeBuffer(bytes: &data, length: MemoryLayout<DataType>.stride * count, options: [])!
// A buffer for individual results (zero initialized)
let resultsBuffer = device.makeBuffer(length: MemoryLayout<DataType>.stride * resultsCount, options: [])!
// Our results in convenient form to compute the actual result later:
let pointer = resultsBuffer.contents().bindMemory(to: DataType.self, capacity: resultsCount)
let results = UnsafeBufferPointer<DataType>(start: pointer, count: resultsCount)
//------------------------------------------------------------------------------
let queue = device.makeCommandQueue()!
let cmds = queue.makeCommandBuffer()!
let encoder = cmds.makeComputeCommandEncoder()!
//------------------------------------------------------------------------------
encoder.setComputePipelineState(pipeline)
encoder.setBuffer(dataBuffer, offset: 0, index: 0)
encoder.setBytes(&dataCount, length: MemoryLayout<CUnsignedInt>.size, index: 1)
encoder.setBuffer(resultsBuffer, offset: 0, index: 2)
encoder.setBytes(&elementsPerSumC, length: MemoryLayout<CUnsignedInt>.size, index: 3)
//------------------------------------------------------------------------------
// We have to calculate the sum `resultCount` times => amount of threadgroups is `resultsCount` / `threadExecutionWidth` (rounded up) because each threadgroup will process `threadExecutionWidth` threads
let threadgroupsPerGrid = MTLSize(width: (resultsCount + pipeline.threadExecutionWidth - 1) / pipeline.threadExecutionWidth, height: 1, depth: 1)
// Here we set that each threadgroup should process `threadExecutionWidth` threads, the only important thing for performance is that this number is a multiple of `threadExecutionWidth` (here 1 times)
let threadsPerThreadgroup = MTLSize(width: pipeline.threadExecutionWidth, height: 1, depth: 1)
//------------------------------------------------------------------------------
encoder.dispatchThreadgroups(threadgroupsPerGrid, threadsPerThreadgroup: threadsPerThreadgroup)
encoder.endEncoding()
//------------------------------------------------------------------------------
var start, end : UInt64
var result : DataType = 0
//------------------------------------------------------------------------------
start = mach_absolute_time()
cmds.commit()
cmds.waitUntilCompleted()
for elem in results {
result += elem
}
end = mach_absolute_time()
//------------------------------------------------------------------------------
print("Metal result: \(result), time: \(Double(end - start) / Double(NSEC_PER_SEC))")
//------------------------------------------------------------------------------
result = 0
start = mach_absolute_time()
data.withUnsafeBufferPointer { buffer in
for elem in buffer {
result += elem
}
}
end = mach_absolute_time()
print("CPU result: \(result), time: \(Double(end - start) / Double(NSEC_PER_SEC))")
//------------------------------------------------------------------------------
#include <metal_stdlib>
using namespace metal;
typedef unsigned int uint;
typedef int DataType;
kernel void parsum(const device DataType* data [[ buffer(0) ]],
const device uint& dataLength [[ buffer(1) ]],
device DataType* sums [[ buffer(2) ]],
const device uint& elementsPerSum [[ buffer(3) ]],
const uint tgPos [[ threadgroup_position_in_grid ]],
const uint tPerTg [[ threads_per_threadgroup ]],
const uint tPos [[ thread_position_in_threadgroup ]]) {
uint resultIndex = tgPos * tPerTg + tPos;
uint dataIndex = resultIndex * elementsPerSum; // Where the summation should begin
uint endIndex = dataIndex + elementsPerSum < dataLength ? dataIndex + elementsPerSum : dataLength; // The index where summation should end
for (; dataIndex < endIndex; dataIndex++)
sums[resultIndex] += data[dataIndex];
}