您好我有以下CUDA C代码：
kernel.cu：
/******************************************************************************
*cr
******************************************************************************/
#include <stdio.h>
#define TILE_SIZE 16
#define BLOCK_SIZE 512
/****************************************************************/
// Kernel for matrix multiplication:
// A: m x n matrix
// B: n x k matrix
// C = A x B: m x k matrix
__global__ void mysgemm(int m, int n, int k, const double *A, const double *B, double* C) {
__shared__ float ds_A[TILE_SIZE][TILE_SIZE];
__shared__ float ds_B[TILE_SIZE][TILE_SIZE];
int bx = blockIdx.x;
int by = blockIdx.y;
int tx = threadIdx.x;
int ty = threadIdx.y;
int row = (by*TILE_SIZE+ty);//%m;
int col = (bx*TILE_SIZE+tx);//%n;
float pvalue = 0;
for(int i=0;i<(k-1)/TILE_SIZE+1;++i)
{
if((i*TILE_SIZE +tx < k) && (row < m))
ds_A[ty][tx] = A[row*k+i*TILE_SIZE+tx];
else ds_A[ty][tx] = 0;
if((i*TILE_SIZE+ty < k) && (col < n))
ds_B[ty][tx] = B[(i*TILE_SIZE+ty)*n+col]; // Load data into shared memory
else ds_B[ty][tx] = 0;
__syncthreads();
if(row < m && col < n)
{
for(int j=0;j<TILE_SIZE;++j)
{
//if(j < k)
pvalue += ds_A[ty][j]*ds_B[j][tx];
}
}
__syncthreads();
}
if(row < m && col < n)
C[row*n+col] = pvalue;
}
/****************************************************************/
/****************************************************************/
// Kernel to multiply each element in A by the corresponding element in B and store
// the result to the corresponding element in C. All vectors should be of length m
__global__ void elem_mul(int m, const double *A, const double *B, double* C)
{
int bx = blockIdx.x;
int tx = threadIdx.x;
int i = tx+bx*blockDim.x;
if(i < m)
C[i] = A[i]*B[i];
}
/****************************************************************/
/****************************************************************/
// Kernel for parallel sum
__global__ void reduction(double *out, double *in, unsigned size)
{
__shared__ float partialSum[2*BLOCK_SIZE];
unsigned int t = threadIdx.x;
unsigned int start = 2*blockIdx.x*blockDim.x;
if(start + t >= size)
partialSum[t] = 0;
else partialSum[t] = in[start+t];
if(start + blockDim.x+t>= size)
partialSum[blockDim.x+t] = 0;
else partialSum[blockDim.x+t] = in[start + blockDim.x+t];
for(unsigned int stride = 1; stride <=blockDim.x; stride*=2)
{
__syncthreads();
if(t % stride ==0)
partialSum[2*t]+=partialSum[2*t+stride];
}
__syncthreads();
out[blockIdx.x] = partialSum[0];
}
/****************************************************************/
/****************************************************************/
// Uses several kernels to compute the inner product of A and B
void inner_product(double *out, int m, const double *A, const double* B, double* temp)
{
dim3 dimGrid((m-1)/BLOCK_SIZE+1,(m-1)/BLOCK_SIZE+1,1);
dim3 dimBlock(BLOCK_SIZE,BLOCK_SIZE,1);
elem_mul<<<dimGrid,dimBlock>>>(m,A,B,temp);
reduction<<<dimGrid,dimBlock>>>(out,temp,m);
}
/****************************************************************/
// Kernel to multiply each element in the matrix out in the following manner:
// out(i,j) = in(i) - in(j)
__global__ void fill(int m, const double *in, double *out)
{
int bx = blockIdx.x;
int by = blockIdx.y;
int tx = threadIdx.x;
int ty = threadIdx.y;
int i = tx+bx*blockDim.x;
int j = ty+by*blockDim.y;
if((i < m) && (j < m))
out[i*m+j] = in[i]-in[j];
}
// Kernel to fill the matrix out with the formula out(i,j) = exp(-omega*T(i.j))
__global__ void fill_E(int m, double coeff, double *in, double *out)
{
int bx = blockIdx.x;
int tx = threadIdx.x;
int i = tx+bx*blockDim.x;
if(i < m)
out[i] = exp(-coeff * in[i]);
}
// Kernel for scalar multiplication for an mxk matirx and a coefficient coeff
__global__ void scal_mul(int m, int k, double coeff, double *in, double *out)
{
int bx = blockIdx.x;
int tx = threadIdx.x;
int i = tx+bx*blockDim.x;
if(i < m*k)
out[i] = coeff * in[i];
}
// Kernel for scalar multiplication for an mxk matirx and a coefficient coeff
__global__ void scal_add(int m, int k, double coeff, double *in, double *out)
{
int bx = blockIdx.x;
int tx = threadIdx.x;
int i = tx+bx*blockDim.x;
if(i < m*k)
out[i] = coeff + in[i];
}
/****************************************************************/
// Kernel to update vector p2
__global__ void update_p2(int m, double coeff, double *in, double *out)
{
int bx = blockIdx.x;
int tx = threadIdx.x;
int i = tx+bx*blockDim.x;
if(i < m)
out[i] = coeff/in[i];
}
/****************************************************************/
/****************************************************************/
// Kernel to update matrix p
__global__ void update_p(int m, double* p2, double *denom, double *num, double *out)
{
int bx = blockIdx.x;
int tx = threadIdx.x;
int i = tx+bx*blockDim.x;
// loop through columns j
for(int j=0; j<m; ++j)
{
if(i == j)
out[i*m + j] = p2[i];
else if(i < m)
out[i*m + j] = num[i*m+j]/denom[i];
}
}
/****************************************************************/
/****************************************************************/
// Kernel to update the error, counter, and parameter variables
__global__ void update(int* counter, double* error, double *mu, double *mu_temp, double* alpha, double* alpha_temp, double* omega, double* omega_temp)
{
*counter = *counter + 1;
*error = (mu - mu_temp)*(mu - mu_temp) + (alpha-alpha_temp)*(alpha-alpha_temp) + (omega-omega_temp)*(omega-omega_temp);
mu = mu_temp;
alpha = alpha_temp;
omega = omega_temp;
}
/****************************************************************/
/****************************************************************/
// Kernel to assign old * coeff + inc to new
__global__ void assign(double* n, double* old, double coeff, double inc)
{
//*n = (*old)*coeff + inc;
*n = 5.0;
}
/****************************************************************/
/******************************************************************************************************/
// Function to calibrate the 1-D Hawke's process. Does so via an iterative procedure. Variables:
// int size: length of the Time-series vectors. Also the number of rows and columns in input matrices
// double mu: One of three parameters calibrated
// double alpha: One of three parameters calibrated
// double omega: One of three parameters calibrated
// double* A: A matrix filled out and used to calibrate
// double* T: A distance matrix T(i,j) = Times[i]-Times[j]
// double* Delta: A dissimilarity matrix Delta(i,j) = 1 if i > j, 0 otherwise
// double* E: A matrix filled out and used to calibrate--E(i,j) = exp(-omega*T(i,j))
// double* p: A probability matrix of cross excitations
// double* p2: A vector of self-excitation probabilities
// double* ones: A (size x 1) vector of 1's used in inner products and identity transformations
// double* Times: A (size x 1) vector of time series data to be calibrated
// int MAX_ITER: The maximum number of iterations allowed in the calibration
// double* TOL: The error tolerance or accuracy allowed in the calibration
// double* temp_1: A (size x 1) temporary vector used in intermediate calculations
// double* temp_2: A temporary matrix used in intermediate calculations
// double* temp_3: A temporary scalar used in intermediate calculations
/******************************************************************************************************/
void calibrate(int size, double *mu, double *mu_t, double *alpha, double *alpha_t, double *omega, double *omega_t, double *A, double *T, double *Delta, double *E, double *p, double *p2, double *D, double* ones, double *Times, int *ctr, double *err, double* temp_1, double* temp_2, double* temp_3)
{
//1) (a) Perform inner product to start initial values of mu, alpha, and omega
inner_product(temp_3, size, Times, ones, temp_1); // Inner product of Time series
dim3 dimGrid(1,1,1);
dim3 dimBlock(1,1,1);
//assign<<<dimGrid,dimBlock>>>(mu_t,temp_3,1.1,0); // Assign mu_t to be temp_3*(1/size) (the average)
//assign<<<dimGrid,dimBlock>>>(alpha_t,temp_3,1.1,0); // Assign mu_t to be temp_3*(1/size) (the average)
//assign<<<dimGrid,dimBlock>>>(omega_t,temp_3,1.1,0); // Assign mu_t to be temp_3*(1/size) (the average)
/*
//1) (b) Fill out matrix T of time differences
dim3 dimGrid((size-1)/BLOCK_SIZE+1,(size-1)/BLOCK_SIZE+1,1);
dim3 dimBlock(BLOCK_SIZE,BLOCK_SIZE,1);
fill<<<dimGrid,dimBlock>>>(size, Times, T);
// 2) Fill out matrix E
dim3 dimGrid((size-1)/BLOCK_SIZE+1,(size-1)/BLOCK_SIZE+1,1);
dim3 dimBlock(BLOCK_SIZE,BLOCK_SIZE,1);
fill_E<<<dimGrid,dimBlock>>>(size, omega, T, E);
// 3) Update matrix A
dim3 dimGrid((size-1)/BLOCK_SIZE+1,(size-1)/BLOCK_SIZE+1,1);
dim3 dimBlock(BLOCK_SIZE,BLOCK_SIZE,1);
scal_mult<<<dimGrid,dimBlock>>>(size,size, alpha, delta, A);
scal_mult<<<dimGrid,dimBlock>>>(size,size, omega, A, A);
dim3 dimGrid((n-1)/TILE_SIZE+1,(m-1)/TILE_SIZE+1,1);
dim3 dimBlock(TILE_SIZE,TILE_SIZE,1);
mysgemm<<<dimGrid,dimBlock>>>(size,size,size,A,E,A)
// 4) Update matrix D
mysgemm<<<dimGrid,dimBlock>>>(size,size,1,A,ones,D);
scal_add<<<dimGrid,dimBlock>>>(size,size, mu, D, D);
// 5) Update matrix p and vector p2
update_p2<<<dimGrid,dimBlock>>>(size,mu, D, p2);
update_p<<<dimGrid,dimBlock>>>(size,p2, D, A, p);
// 6) Update parameters mu, alpha, omega
inner_product(mu_t, size, p2, ones, temp_1);
mu_t /=Times[size-1];
reduction<<<dimGrid,dimBlock>>>(alpha_t,p,size*size);
alpha_t/= size;
// Treat T and p as very long vectors and calculate the inner product
inner_product(omega_t, size*size, T, p, temp_2);
omega_t = alpha_t/omega_t;
*/
// 7) Update error
dim3 g(100,100,1);
dim3 b(100,100,1);
//update<<<g,b>>>(ctr,err,mu,mu_t,alpha,alpha_t,omega,omega_t);
cudaError_t error = cudaGetLastError();
if(error != cudaSuccess)
{
printf("CUDA error: %s\n",cudaGetErrorString(error));
exit(-1);
}
}
以下文件启动包含所有内核调用的主机代码。 main.cu（我还没有使用support.h）：
/******************************************************************************
*cr
*cr
******************************************************************************/
#include <stdio.h>
#include <stdlib.h>
#include "kernel.cu"
#include "support.h"
int main (int argc, char *argv[])
{
Timer timer;
cudaError_t cuda_ret;
// Initialize host variables ----------------------------------------------
printf("\nSetting up the problem...\n"); fflush(stdout);
startTime(&timer);
double* A_h, *T_h, *Delta_h, *E_h, *p_h, *p2_h, *D_h, *Times_h, *ones_h;
double* A_d, *T_d, *Delta_d, *E_d, *p_d, *p2_d, *D_d, *Times_d, *ones_d, *temp_1, *temp_2, *temp_3;
double* mu_h, *alpha_h, *omega_h; // hawkes parameters on host
double* mu_d, *alpha_d, *omega_d; // hawkes parameters on device
double* mu_t_d, *alpha_t_d, *omega_t_d; // hawkes temporary parameters on device
double* err_h, *err_d; // Iterative variables for hohst and device
int* ctr_h, *ctr_d;
int N;
unsigned int mat_size, vec_size;
// Import data
FILE *fp;
char str[60];
unsigned int count=0;
double d;
/* opening file for reading */
fp = fopen("AAPL_data.txt","r");
if(fp == NULL) {
perror("Error opening file");
return(-1);
}
while(fgets (str, 60, fp)!=NULL)
++count;
// Stick with a limited subset of the data for now to avoid using too much host memory
N = 1000;
fclose(fp);
printf("Count is %u \n",count);
mat_size = N*N;
vec_size = N;
dim3 dim_grid, dim_block;
// Fill matrices with 0's
A_h = (double*) malloc( sizeof(double)*mat_size );
for (unsigned int i=0; i < mat_size; ++i) { A_h[i] = 0; }
T_h = (double*) malloc( sizeof(double)*mat_size );
for (unsigned int i=0; i < mat_size; ++i) { T_h[i] = 0; }
Delta_h = (double*) malloc( sizeof(double)*mat_size );
for (unsigned int i=0; i < mat_size; ++i) { Delta_h[i] = 0; }
E_h = (double*) malloc( sizeof(double)*mat_size );
for (unsigned int i=0; i < mat_size; ++i) { E_h[i] = 0; }
p_h = (double*) malloc( sizeof(double)*mat_size );
for (unsigned int i=0; i < mat_size; ++i) { p_h[i] = 0; }
// Fill vectors with 0's, except the 1's vector
p2_h = (double*) malloc( sizeof(double)*vec_size );
for (unsigned int i=0; i < vec_size; ++i) { p2_h[i] = 0; }
Times_h = (double*) malloc( sizeof(double)*vec_size );
for (unsigned int i=0; i < vec_size; ++i) { Times_h[i] = 0; }
D_h = (double*) malloc( sizeof(double)*vec_size );
for (unsigned int i=0; i < vec_size; ++i) { D_h[i] = 0; }
ones_h = (double*) malloc( sizeof(double)*vec_size );
for (unsigned int i=0; i < vec_size; ++i) { ones_h[i] = 0; }
// Start constants as zero
mu_h = (double*) malloc( sizeof(double));
alpha_h = (double*) malloc( sizeof(double));
omega_h = (double*) malloc( sizeof(double));
err_h = (double*) malloc( sizeof(double));
ctr_h = (int*) malloc( sizeof(int));
*mu_h = 0;
*alpha_h = 0;
*omega_h = 0;
*err_h = 0;
*ctr_h = 0;
// Import data
count=0;
/* opening file for reading */
fp = fopen("AAPL_data.txt","r");
if(fp == NULL) {
perror("Error opening file");
return(-1);
}
while(fgets (str, 60, fp)!=NULL)
{
sscanf(str, "%lf", &d);
if(count < vec_size)
Times_h[count] = d;
++count;
}
fclose(fp);
/*printf("TIMES VECTOR: \n");
for (unsigned int i=0; i < vec_size; ++i)
{
printf("TIMES_H[ %u ] is ",i);
printf("%f \n", Times_h[i]);
}*/
printf("Count is %u \n",count);
stopTime(&timer); printf("%f s\n", elapsedTime(timer));
// Allocate device variables ----------------------------------------------
printf("Allocating device variables..."); fflush(stdout);
startTime(&timer);
cudaMalloc((void**) &A_d, mat_size*sizeof(double)); // Create device variable for matrix A
cudaMalloc((void**) &T_d, mat_size*sizeof(double)); // Create device variable for matrix T
cudaMalloc((void**) &Delta_d, mat_size*sizeof(double)); // Create device variable for matrix Delta
cudaMalloc((void**) &E_d, mat_size*sizeof(double)); // Create device variable for matrix E
cudaMalloc((void**) &p_d, mat_size*sizeof(double)); // Create device variable for matrix p
cudaMalloc((void**) &p2_d, vec_size*sizeof(double)); // Create device variable for vector p2
cudaMalloc((void**) &D_d, vec_size*sizeof(double)); // Create device variable for vector D
cudaMalloc((void**) &Times_d, vec_size*sizeof(double)); // Create device variable for vector Times
cudaMalloc((void**) &ones_d, vec_size*sizeof(double)); // Create device variable for vector ones
// Parameters and intermediate parameters
cudaMalloc((void**) &mu_d, sizeof(double)); // Create device variable for constant mu
cudaMalloc((void**) &alpha_d, sizeof(double)); // Create device variable for constant alpha
cudaMalloc((void**) &omega_d, sizeof(double)); // Create device variable for constant omega
cudaMalloc((void**) &mu_t_d, sizeof(double)); // Create device variable for constant mu
cudaMalloc((void**) &alpha_t_d, sizeof(double)); // Create device variable for constant alpha
cudaMalloc((void**) &omega_t_d, sizeof(double)); // Create device variable for constant omega
// Temporary variables
cudaMalloc((void**) &temp_1, vec_size*sizeof(double)); // Create device variable for constant omega
cudaMalloc((void**) &temp_2, mat_size*sizeof(double)); // Create device variable for constant omega
cudaMalloc((void**) &temp_3, sizeof(double)); // Create device variable for constant omega
// Iteration variables
cudaMalloc((void**) &err_d, sizeof(double)); // Create device variable for iterative counters
cudaMalloc((void**) &ctr_d, sizeof(int));
cudaDeviceSynchronize();
stopTime(&timer); printf("%f s\n", elapsedTime(timer));
// Copy host variables to device ------------------------------------------
printf("Copying data from host to device..."); fflush(stdout);
startTime(&timer);
cudaMemcpy(A_d,A_h,mat_size*sizeof(double), cudaMemcpyHostToDevice); // Copy from host var to device var
cudaMemcpy(T_d,T_h,mat_size*sizeof(double), cudaMemcpyHostToDevice); // Copy from host var to device var
cudaMemcpy(Delta_d,Delta_h,mat_size*sizeof(double), cudaMemcpyHostToDevice); // Copy from host var to device var
cudaMemcpy(E_d,E_h,mat_size*sizeof(double), cudaMemcpyHostToDevice); // Copy from host var to device var
cudaMemcpy(p_d,p_h,mat_size*sizeof(double), cudaMemcpyHostToDevice); // Copy from host var to device var
cudaMemcpy(p2_d,p2_h,vec_size*sizeof(double), cudaMemcpyHostToDevice); // Copy from host var to device var
cudaMemcpy(D_d,D_h,vec_size*sizeof(double), cudaMemcpyHostToDevice); // Copy from host var to device var
cudaMemcpy(ones_d,ones_h,vec_size*sizeof(double), cudaMemcpyHostToDevice); // Copy from host var to device var
cudaMemcpy(Times_d,Times_h,vec_size*sizeof(double), cudaMemcpyHostToDevice); // Copy from host var to device var
// Parameters and intermediate parameters
cudaMemcpy(mu_d,mu_h,sizeof(double), cudaMemcpyHostToDevice); // Copy from host var to device var
cudaMemcpy(alpha_d,alpha_h,sizeof(double), cudaMemcpyHostToDevice); // Copy from host var to device var
cudaMemcpy(omega_d,omega_h,sizeof(double), cudaMemcpyHostToDevice); // Copy from host var to device var
cudaMemcpy(mu_t_d,mu_h,sizeof(double), cudaMemcpyHostToDevice); // Copy from host var to device var
cudaMemcpy(alpha_t_d,alpha_h,sizeof(double), cudaMemcpyHostToDevice); // Copy from host var to device var
cudaMemcpy(omega_t_d,omega_h,sizeof(double), cudaMemcpyHostToDevice); // Copy from host var to device var
// Temporary variables
cudaMemcpy(temp_1,D_h,vec_size*sizeof(double), cudaMemcpyHostToDevice); // Copy from host var to device var
cudaMemcpy(temp_2,A_h,mat_size*sizeof(double), cudaMemcpyHostToDevice); // Copy from host var to device var
cudaMemcpy(temp_3,mu_h,sizeof(double), cudaMemcpyHostToDevice); // Copy from host var to device var
// Iteration variables
cudaMemcpy(err_d,err_h,sizeof(double), cudaMemcpyHostToDevice); // Copy from host var to device var
cudaMemcpy(ctr_d,ctr_h,sizeof(int), cudaMemcpyHostToDevice); // Copy from host var to device var
cudaDeviceSynchronize();
stopTime(&timer); printf("%f s\n", elapsedTime(timer));
// Launch kernel using standard sgemm interface ---------------------------
printf("Launching kernel..."); fflush(stdout);
startTime(&timer);
int MAX_ITER = 100;
double TOL = .001;
//while(ctr_h < MAX_ITER && err_h < TOL)
//{
calibrate(vec_size,mu_d, mu_t_d, alpha_d, alpha_t_d, omega_d, omega_t_d, A_d, T_d, Delta_d, E_d, p_d,
p2_d, D_d, ones_d, Times_d, ctr_d, err_d, temp_1, temp_2, temp_3);
// cudaMemcpy(err_h,err_d,sizeof(double), cudaMemcpyDeviceToHost); // Copy from device var to host var
// cudaMemcpy(ctr_h,ctr_d,sizeof(int), cudaMemcpyDeviceToHost); // Copy from device var to host var
//}
cuda_ret = cudaDeviceSynchronize();
if(cuda_ret != cudaSuccess) FATAL("Unable to launch kernel");
stopTime(&timer); printf("%f s\n", elapsedTime(timer));
// Copy device variables from host ----------------------------------------
printf("Copying data from device to host...\n"); fflush(stdout);
startTime(&timer);
cudaMemcpy(mu_h,mu_d,sizeof(double), cudaMemcpyDeviceToHost); // Copy from device var to host var
cudaMemcpy(alpha_h,alpha_d,sizeof(double), cudaMemcpyDeviceToHost); // Copy from device var to host var
cudaMemcpy(omega_h,omega_d,sizeof(double), cudaMemcpyDeviceToHost); // Copy from device var to host var
printf("mu is %f: \n",*mu_h);
printf("alpha is %f: \n",*alpha_h);
printf("omega is %f: \n",*omega_h);
cudaDeviceSynchronize();
stopTime(&timer); printf("%f s\n", elapsedTime(timer));
// Free memory ------------------------------------------------------------
free(A_h);
free(T_h);
free(Delta_h);
free(E_h);
free(p_h);
free(p2_h);
free(D_h);
free(ones_h);
free(Times_h);
free(mu_h);
free(alpha_h);
free(omega_h);
cudaFree(A_d);
cudaFree(T_d);
cudaFree(Delta_d);
cudaFree(E_d);
cudaFree(p_d);
cudaFree(p2_d);
cudaFree(D_d);
cudaFree(ones_d);
cudaFree(Times_d);
cudaFree(mu_d);
cudaFree(alpha_d);
cudaFree(omega_d);
return 0;
}
我收到错误CUDA错误：来自kernel.cu末尾的cudaGetLastError（）调用的无效配置参数。因为除了调用两个内核的inner_product主机函数之外，所有内容都被注释掉了，我认为问题来自那里：
/****************************************************************/
// Uses several kernels to compute the inner product of A and B
void inner_product(double *out, int m, const double *A, const double* B, double* temp)
{
dim3 dimGrid((m-1)/BLOCK_SIZE+1,(m-1)/BLOCK_SIZE+1,1);
dim3 dimBlock(BLOCK_SIZE,BLOCK_SIZE,1);
elem_mul<<<dimGrid,dimBlock>>>(m,A,B,temp);
reduction<<<dimGrid,dimBlock>>>(out,temp,m);
}
/****************************************************************/
由于传递的m是1000而BLOCK_SIZE是512，这应该产生dimGrid（1,1,1）和dimBlock（512,512,1），但这会产生上述错误。即使我将其更改为dimBlock（256,256,1），我也得到了同样的错误。我非常确定此设备允许的块大小最多为1024个线程。
答案 0 :(得分：2)
你的区块尺寸dimBlock(512,512)很大！每个线程块可以启动最大线程数，具体取决于你的gpu的计算架构。
有多种方法可以找出最大块尺寸。 一种快速的方法是使用cuda sdk样本deviceQuery程序。这列出了启用cuda的gpus的所有信息，例如最大块尺寸。 或者您使用cuda occupancy calculator并尝试输入您的内核参数。在您的示例中，将出现错误。 第三种方法是阅读cuda programming guide，您可以在其中找到所需的所有信息。
答案 1 :(得分：-1)
这可能有助于动态定义您要使用的线程数。
struct cudaDeviceProp properties;
cudaGetDeviceProperties(&properties, device);
cout<<"using "<<properties.multiProcessorCount<<" multiprocessors"<<endl;
cout<<"max threads per processor: "<<properties.maxThreadsPerMultiProcessor<<endl<<endl;
您可以使用以下任何配置：
其中块的容量不超过properties.maxThreadsPerMultiProcessor