【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];
}
}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 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
#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
#include
#include
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;
}
#include
#include
#include
#include
#include
#include
#include
#include
#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> 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 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 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,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 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
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://pan.baidu.com/s/1jIdslYQ
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：[email protected]
注明出处：http://blog.csdn.net/lavorange/article/details/41942323