【CUDA并行编程之八】Cuda实现Kmeans算法

本文主要介绍如何使用CUDA并行计算框架编程实现机器学习中的Kmeans算法，Kmeans算法的详细介绍在这里，本文重点在并行实现的过程。

当然还是简单的回顾一下kmeans算法的串行过程：

伪代码：

创建k个点作为起始质心(经常是随机选择)
当任意一个点的簇分配结果发生改变时
    对数据集中的每个数据点
        对每个质心
            计算质心与数据点之间的距离
        将数据点分配到距其最近的簇
    对每一个簇，计算簇中所有点的均值并将均值作为质心

我们可以观察到有两个部分可以并行优化：

①line03-04：将每个数据点到多个质心的距离计算进行并行化

②line05：将数据点到某个执行的距离计算进行并行化

KMEANS类：

class KMEANS
{
private:
    int numClusters;
    int numCoords;
    int numObjs;
    int *membership;//[numObjs]
    char *filename;
    float **objects;//[numObjs][numCoords] data objects
    float **clusters;//[numClusters][unmCoords] cluster center
    float threshold;
    int loop_iterations;

public:
    KMEANS(int k);
    void file_read(char *fn);
    void file_write();
    void cuda_kmeans();
    inline int nextPowerOfTwo(int n);
    void free_memory();
    virtual ~KMEANS();
};//KMEANS

成员变量：

numClusters：中心点的个数

numCoords：每个数据点的维度

numObjs：数据点的个数

membership：每个数据点所属类别的数组，维度为numObjs

filename：读入的文件名

objects：所有数据点，维度为[numObjs][numCoords]

clusters：中心点数据，维度为[numObjs][numCoords]

threshold：控制循环次数的一个域值

loop_iterations：循环的迭代次数

成员函数：

KMEANS(int k)：含参构造函数。初始化成员变量

file_read(char *fn)：读入文件数据并初始化object以及membership变量

file_write()：将计算结果写回到结果文件中去

cuda_kmeans()：kmeans计算的入口函数

nextPowerOfTwo(int n)：它计算大于等于输入参数n的第一个2的幂次数。

free_memory()：释放内存空间

~KMEANS()：析构函数

并行的代码主要三个函数：

find_nearest_cluster(...)

compute_delta(...)

euclid_dist_2(...)

首先看一下函数euclid_dist_2(...)：

__host__ __device__ inline static
float euclid_dist_2(int numCoords,int numObjs,int numClusters,float *objects,float *clusters,int objectId,int clusterId)
{
    int i;
    float ans = 0;
    for( i=0;i<numCoords;i++ )
    {
        ans += ( objects[numObjs * i + objectId] - clusters[numClusters*i + clusterId] ) *
               ( objects[numObjs * i + objectId] - clusters[numClusters*i + clusterId] ) ;
    }
    return ans;
}

这段代码实际上就是并行的计算向量objects[objectId]和clusters[clusterId]之间的距离，即第objectId个数据点到第clusterId个中心点的距离。

再看一下函数compute_delta(...)：

/*
* numIntermediates:The actual number of intermediates
* numIntermediates2:The next power of two
*/
__global__ static void compute_delta(int *deviceIntermediates,int numIntermediates, int numIntermediates2)
{
    extern __shared__ unsigned int intermediates[];

    intermediates[threadIdx.x] = (threadIdx.x < numIntermediates) ? deviceIntermediates[threadIdx.x] : 0 ;
    __syncthreads();

    //numIntermediates2 *must* be a power of two!
    for(unsigned int s = numIntermediates2 /2 ; s > 0 ; s>>=1)
    {
        if(threadIdx.x < s)
        {
            intermediates[threadIdx.x] += intermediates[threadIdx.x + s];
        }
        __syncthreads();
    }
    if(threadIdx.x == 0)
    {
        deviceIntermediates[0] = intermediates[0];
    }
}

这段代码的意义就是将一个线程块中每个线程的对应的intermediates的数据求和最后放到deviceIntermediates[0]中去然后拷贝回主存块中去。这个问题的更好的解释在这里，实际上就是一个数组求和的问题，应用在这里求得的是有改变的membership中所有数据的和，即改变了簇的点的个数。

最后再看函数finid_nearest_cluster(...)：

/*
* objects:[numCoords][numObjs]
* deviceClusters:[numCoords][numClusters]
* membership:[numObjs]
*/
__global__ static void find_nearest_cluster(int numCoords,int numObjs,int numClusters,float *objects, float *deviceClusters,int *membership ,int *intermediates)
{
    extern __shared__ char sharedMemory[];
    unsigned char *membershipChanged = (unsigned char *)sharedMemory;
    float *clusters = deviceClusters;

    membershipChanged[threadIdx.x] = 0;

    int objectId = blockDim.x * blockIdx.x + threadIdx.x;
    if( objectId < numObjs )
    {
        int index;
        float dist,min_dist;
        /*find the cluster id that has min distance to object*/
        index = 0;
        min_dist = euclid_dist_2(numCoords,numObjs,numClusters,objects,clusters,objectId,0);

        for(int i=0;i<numClusters;i++)
        {
            dist = euclid_dist_2(numCoords,numObjs,numClusters,objects,clusters,objectId,i) ;
            /* no need square root */
            if( dist < min_dist )
            {
                min_dist = dist;
                index = i;
            }
        }

        if( membership[objectId]!=index )
        {
            membershipChanged[threadIdx.x] = 1;
        }
        //assign the membership to object objectId
        membership[objectId] = index;

        __syncthreads(); //for membershipChanged[]

#if 1
        //blockDim.x *must* be a power of two!
        for(unsigned int s = blockDim.x / 2; s > 0 ;s>>=1)
        {
            if(threadIdx.x < s)
            {
                membershipChanged[threadIdx.x] += membershipChanged[threadIdx.x + s];//calculate all changed values and save result to membershipChanged[0]
            }
            __syncthreads();
        }
        if(threadIdx.x == 0)
        {
            intermediates[blockIdx.x] = membershipChanged[0];
        }
#endif
    }
}//find_nearest_cluster

这个函数计算的就是第objectId个数据点到numClusters个中心点的距离，然后根据情况比较更新membership。

这三个函数将所有能够并行的地方都进行了并行，实现了整体算法的并行化~

在此呈上全部代码：

kmeans.h:

#ifndef _H_KMEANS
#define _H_KMEANS

#include <assert.h>

#define malloc2D(name, xDim, yDim, type) do {               \
    name = (type **)malloc(xDim * sizeof(type *));          \
    assert(name != NULL);                                   \
    name[0] = (type *)malloc(xDim * yDim * sizeof(type));   \
    assert(name[0] != NULL);                                \
    for (size_t i = 1; i < xDim; i++)                       \
        name[i] = name[i-1] + yDim;                         \
} while (0)


double  wtime(void);

#endif

wtime.cu:

#include <sys/time.h>
#include <stdio.h>
#include <stdlib.h>

double wtime(void)
{
    double          now_time;
    struct timeval  etstart;
    struct timezone tzp;

    if (gettimeofday(&etstart, &tzp) == -1)
        perror("Error: calling gettimeofday() not successful.\n");

    now_time = ((double)etstart.tv_sec) +              /* in seconds */
               ((double)etstart.tv_usec) / 1000000.0;  /* in microseconds */
    return now_time;
}

cuda_kmeans.cu：

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <sys/types.h>
#include <sys/stat.h>
#include <unistd.h>
#include <iostream>
#include <cassert>

#include "kmeans.h"

using namespace std;

const int MAX_CHAR_PER_LINE = 1024;

class KMEANS
{
private:
    int numClusters;
    int numCoords;
    int numObjs;
    int *membership;//[numObjs]
    char *filename;
    float **objects;//[numObjs][numCoords] data objects
    float **clusters;//[numClusters][unmCoords] cluster center
    float threshold;
    int loop_iterations;

public:
    KMEANS(int k);
    void file_read(char *fn);
    void file_write();
    void cuda_kmeans();
    inline int nextPowerOfTwo(int n);
    void free_memory();
    virtual ~KMEANS();
};

KMEANS::~KMEANS()
{
    free(membership);
    free(clusters[0]);
    free(clusters);
    free(objects[0]);
    free(objects);
}

KMEANS::KMEANS(int k)
{
    threshold = 0.001;
    numObjs = 0;
    numCoords = 0;
    numClusters = k;
    filename = NULL;
    loop_iterations = 0;
}

void KMEANS::file_write()
{
    FILE *fptr;
    char outFileName[1024];

    //output:the coordinates of the cluster centres
    sprintf(outFileName,"%s.cluster_centres",filename);
    printf("Writingcoordinates of K=%d cluster centers to file \"%s\"\n",numClusters,outFileName);
    fptr = fopen(outFileName,"w");
    for(int i=0;i<numClusters;i++)
    {
        fprintf(fptr,"%d ",i)   ;
        for(int j=0;j<numCoords;j++)
            fprintf(fptr,"%f ",clusters[i][j]);
        fprintf(fptr,"\n");
    }
    fclose(fptr);

    //output:the closest cluster centre to each of the data points
    sprintf(outFileName,"%s.membership",filename);
    printf("writing membership of N=%d data objects to file \"%s\" \n",numObjs,outFileName);
    fptr = fopen(outFileName,"w");
    for(int i=0;i<numObjs;i++)
    {
        fprintf(fptr,"%d %d\n",i,membership[i]) ;
    }
    fclose(fptr);
}

inline int KMEANS::nextPowerOfTwo(int n)
{
    n--;
    n = n >> 1 | n;
    n = n >> 2 | n;
    n = n >> 4 | n;
    n = n >> 8 | n;
    n = n >> 16 | n;
    //n = n >> 32 | n; // for 64-bit ints
    return ++n;
}

__host__ __device__ inline static
float euclid_dist_2(int numCoords,int numObjs,int numClusters,float *objects,float *clusters,int objectId,int clusterId)
{
    int i;
    float ans = 0;
    for( i=0;i<numCoords;i++ )
    {
        ans += ( objects[numObjs * i + objectId] - clusters[numClusters*i + clusterId] ) *
               ( objects[numObjs * i + objectId] - clusters[numClusters*i + clusterId] ) ;
    }
    return ans;
}

/*
* numIntermediates:The actual number of intermediates
* numIntermediates2:The next power of two
*/
__global__ static void compute_delta(int *deviceIntermediates,int numIntermediates, int numIntermediates2)
{
    extern __shared__ unsigned int intermediates[];

    intermediates[threadIdx.x] = (threadIdx.x < numIntermediates) ? deviceIntermediates[threadIdx.x] : 0 ;
    __syncthreads();

    //numIntermediates2 *must* be a power of two!
    for(unsigned int s = numIntermediates2 /2 ; s > 0 ; s>>=1)
    {
        if(threadIdx.x < s)
        {
            intermediates[threadIdx.x] += intermediates[threadIdx.x + s];
        }
        __syncthreads();
    }
    if(threadIdx.x == 0)
    {
        deviceIntermediates[0] = intermediates[0];
    }
}

/*
* objects:[numCoords][numObjs]
* deviceClusters:[numCoords][numClusters]
* membership:[numObjs]
*/
__global__ static void find_nearest_cluster(int numCoords,int numObjs,int numClusters,float *objects, float *deviceClusters,int *membership ,int *intermediates)
{
    extern __shared__ char sharedMemory[];
    unsigned char *membershipChanged = (unsigned char *)sharedMemory;
    float *clusters = deviceClusters;

    membershipChanged[threadIdx.x] = 0;

    int objectId = blockDim.x * blockIdx.x + threadIdx.x;
    if( objectId < numObjs )
    {
        int index;
        float dist,min_dist;
        /*find the cluster id that has min distance to object*/
        index = 0;
        min_dist = euclid_dist_2(numCoords,numObjs,numClusters,objects,clusters,objectId,0);

        for(int i=0;i<numClusters;i++)
        {
            dist = euclid_dist_2(numCoords,numObjs,numClusters,objects,clusters,objectId,i) ;
            /* no need square root */
            if( dist < min_dist )
            {
                min_dist = dist;
                index = i;
            }
        }

        if( membership[objectId]!=index )
        {
            membershipChanged[threadIdx.x] = 1;
        }
        //assign the membership to object objectId
        membership[objectId] = index;

        __syncthreads(); //for membershipChanged[]

#if 1
        //blockDim.x *must* be a power of two!
        for(unsigned int s = blockDim.x / 2; s > 0 ;s>>=1)
        {
            if(threadIdx.x < s)
            {
                membershipChanged[threadIdx.x] += membershipChanged[threadIdx.x + s];//calculate all changed values and save result to membershipChanged[0]
            }
            __syncthreads();
        }
        if(threadIdx.x == 0)
        {
            intermediates[blockIdx.x] = membershipChanged[0];
        }
#endif
    }
}//find_nearest_cluster

void KMEANS::cuda_kmeans()
{
    int index,loop = 0;
    int *newClusterSize;//[numClusters]:no.objects assigned in each new cluster
    float delta; //% of objects changes their clusters
    float **dimObjects;//[numCoords][numObjs]
    float **dimClusters;
    float **newClusters;//[numCoords][numClusters]

    float *deviceObjects; //[numCoords][numObjs]
    float *deviceClusters; //[numCoords][numclusters]
    int *deviceMembership;
    int *deviceIntermediates;

    //Copy objects given in [numObjs][numCoords] layout to new [numCoords][numObjs] layout
    malloc2D(dimObjects,numCoords,numObjs,float);
    for(int i=0;i<numCoords;i++)
    {
        for(int j=0;j<numObjs;j++)
        {
            dimObjects[i][j] = objects[j][i];
        }
    }
    //pick first numClusters elements of objects[] as initial cluster centers
    malloc2D(dimClusters, numCoords, numClusters,float);
    for(int i=0;i<numCoords;i++)
    {
        for(int j=0;j<numClusters;j++)
        {
            dimClusters[i][j] = dimObjects[i][j];
        }
    }
    newClusterSize = new int[numClusters];
    assert(newClusterSize!=NULL);
    malloc2D(newClusters,numCoords,numClusters,float);
    memset(newClusters[0],0,numCoords * numClusters * sizeof(float) );

    //To support reduction,numThreadsPerClusterBlock *must* be a power of two, and it *must* be no larger than the number of bits that will fit into an unsigned char ,the type used to keep track of membership changes in the kernel.
    const unsigned int numThreadsPerClusterBlock = 32;
    const unsigned int numClusterBlocks = (numObjs + numThreadsPerClusterBlock -1)/numThreadsPerClusterBlock;
    const unsigned int numReductionThreads = nextPowerOfTwo(numClusterBlocks);

    const unsigned int clusterBlockSharedDataSize = numThreadsPerClusterBlock * sizeof(unsigned char);

    const unsigned int reductionBlockSharedDataSize = numReductionThreads * sizeof(unsigned int);

    cudaMalloc(&deviceObjects,numObjs*numCoords*sizeof(float));
    cudaMalloc(&deviceClusters,numClusters*numCoords*sizeof(float));
    cudaMalloc(&deviceMembership,numObjs*sizeof(int));
    cudaMalloc(&deviceIntermediates,numReductionThreads*sizeof(unsigned int));

    cudaMemcpy(deviceObjects,dimObjects[0],numObjs*numCoords*sizeof(float),cudaMemcpyHostToDevice);
    cudaMemcpy(deviceMembership,membership,numObjs*sizeof(int),cudaMemcpyHostToDevice);

    do
    {
        cudaMemcpy(deviceClusters,dimClusters[0],numClusters*numCoords*sizeof(float),cudaMemcpyHostToDevice);

        find_nearest_cluster<<<numClusterBlocks,numThreadsPerClusterBlock,clusterBlockSharedDataSize>>>(numCoords,numObjs,numClusters,deviceObjects,deviceClusters,deviceMembership,deviceIntermediates);

        cudaDeviceSynchronize();

        compute_delta<<<1,numReductionThreads,reductionBlockSharedDataSize>>>(deviceIntermediates,numClusterBlocks,numReductionThreads);

        cudaDeviceSynchronize();

        int d;
        cudaMemcpy(&d,deviceIntermediates,sizeof(int),cudaMemcpyDeviceToHost);
        delta = (float)d;

        cudaMemcpy(membership,deviceMembership,numObjs*sizeof(int),cudaMemcpyDeviceToHost);

        for(int i=0;i<numObjs;i++)
        {
            //find the array index of nestest
            index = membership[i];
            //update new cluster centers:sum of objects located within
            newClusterSize[index]++;
            for(int j=0;j<numCoords;j++)
            {
                newClusters[j][index] += objects[i][j];
            }
        }
        //average the sum and replace old cluster centers with newClusters
        for(int i=0;i<numClusters;i++)
        {
            for(int j=0;j<numCoords;j++)
            {
                if(newClusterSize[i] > 0)
                    dimClusters[j][i] = newClusters[j][i]/newClusterSize[i];
                newClusters[j][i] = 0.0;//set back to 0
            }
            newClusterSize[i] = 0 ; //set back to 0
        }
        delta /= numObjs;
    }while( delta > threshold && loop++ < 500 );

    loop_iterations = loop + 1;

    malloc2D(clusters,numClusters,numCoords,float);
    for(int i=0;i<numClusters;i++)
    {
        for(int j=0;j<numCoords;j++)
        {
            clusters[i][j] = dimClusters[j][i];
        }
    }

    cudaFree(deviceObjects) ;
    cudaFree(deviceClusters);
    cudaFree(deviceMembership);
    cudaFree(deviceMembership);

    free(dimObjects[0]);
    free(dimObjects);
    free(dimClusters[0]);
    free(dimClusters);
    free(newClusters[0]);
    free(newClusters);
    free(newClusterSize);
}

void KMEANS::file_read(char *fn)
{

    FILE *infile;
    char *line = new char[MAX_CHAR_PER_LINE];
    int lineLen = MAX_CHAR_PER_LINE;

    filename = fn;
    infile = fopen(filename,"r");
    assert(infile!=NULL);
    /*find the number of objects*/
    while( fgets(line,lineLen,infile) )
    {
        numObjs++;
    }

    /*find the dimension of each object*/
    rewind(infile);
    while( fgets(line,lineLen,infile)!=NULL )
    {
        if( strtok(line," \t\n")!=0 )
        {
            while( strtok(NULL," \t\n") )
                numCoords++;
            break;
        }
    }

    /*allocate space for object[][] and read all objcet*/
    rewind(infile);
    objects = new float*[numObjs];
    for(int i=0;i<numObjs;i++)
    {
        objects[i] = new float[numCoords];
    }
    int i=0;
    /*read all object*/
    while( fgets(line,lineLen,infile)!=NULL )
    {
        if( strtok(line," \t\n") ==NULL ) continue;
        for(int j=0;j<numCoords;j++)
        {
            objects[i][j] = atof( strtok(NULL," ,\t\n") )   ;
        }
        i++;
    }

    /* membership: the cluster id for each data object */
    membership = new int[numObjs];
    assert(membership!=NULL);
    for(int i=0;i<numObjs;i++)
        membership[i] = -1;

}

int main(int argc,char *argv[])
{
    KMEANS kmeans(atoi(argv[1]));
    kmeans.file_read(argv[2]);
    kmeans.cuda_kmeans();
    kmeans.file_write();
    return 0;
}

makefile：

target:
    nvcc cuda_kmeans.cu
    ./a.out  4 ./Image_data/color100.txt

所有代码和文件数据在这里：http://yunpan.cn/cKBZMPAJ8tcAs（提取码：9476）

运行代码：

kmeans的cuda实现代码相对复杂，在阅读的过程中可能会有困难，有问题请留言~

Author：忆之独秀

Email：leaguenew@qq.com

注明出处：http://blog.csdn.net/lavorange/article/details/41942323