Multiple kernel calls in CUDA
Question
I'm trying to call the same kernel on CUDA (with one different input parameter) more times, but it executes only the first one and doesn't follow with other kernel calls. Assume the inputs arrays are new_value0=[123.814935276; 234; 100; 166; 203.0866414; 383; 186; 338; 173.0984233] new_value0=[123.814935276; 234; 100; 166; 203.0866414; 383; 186; 338; 173.0984233] new_value0=[123.814935276; 234; 100; 166; 203.0866414; 383; 186; 338; 173.0984233] and new_value1=[186.221113; 391; 64; 235; 195.7454998; 275; 218; 121; 118.0333872] new_value1=[186.221113; 391; 64; 235; 195.7454998; 275; 218; 121; 118.0333872] new_value1=[186.221113; 391; 64; 235; 195.7454998; 275; 218; 121; 118.0333872] part of output is:
entra
entra
entra
334
549
524
alpha1.000000
alpha1.000000
alpha1.000000
in 2 idx-j 0-0 Value 123.814934 - m=334 - k=0
mlx -1618.175171
in 1 idx-j 0-1 Value 234.000000 - m=334 k=1
mlx -571.983032
in 1 idx-j 0-2 Value 100.000000 - m=334 k=2
mlx -208.243652
in 1 idx-j 1-0 Value 166.000000 - m=549 k=3
mlx 477.821777
in 2 idx-j 1-1 Value 203.086639 - m=549 - k=4
mlx -2448.556396
in 1 idx-j 1-2 Value 383.000000 - m=549 k=5
mlx -549.565674
in 1 idx-j 2-0 Value 186.000000 - m=524 k=6
mlx 239.955444
in 1 idx-j 2-1 Value 338.000000 - m=524 k=7
mlx 1873.975708
in 2 idx-j 2-2 Value 173.098419 - m=524 - k=8
mlx -835.600220
mlx =-835.600220
bs = -835.600220 .
esci
esci
esci
It is from the first kernel call.
This is the kernel:
__global__ void calculateMLpa( int N, float *bs, float *Value, float alphaxixj, float tauxi, const int sz, int dim, int *m){
int idx = blockIdx.x * blockDim.x + threadIdx.x;
printf("entra\n");
if(idx<N){
bs[idx]=0;
int i,k=0;
float mlx = 0;
float v;
float alphaxi;
m[idx]=0;
int state[9];
int p, j, t;
int cont=0;
if(idx==0){
m[idx]=Value[idx+1]+Value[idx+2];
}
else if(idx==1){
m[idx]=Value[idx+2]+Value[idx+4];
}else{
m[idx]=Value[idx+4]+Value[idx+5];
}
printf("%d \n",m[idx]);
alphaxi = alphaxixj * (((float) sz) - 1.0);
alphaxi = alphaxixj;
printf("alpha%f \n",alphaxi);
if(idx==0){
for(i=0;i<sz;i++){
for (j = 0; j < sz; j++) {
// xi!=xj
if (i!=j){
if(j==0) {
k=i*3;
}
else if(j==1){
k=i*3+1;
}
else if(j==2) {
k=i*3+2;
}
mlx = mlx + lgamma(alphaxixj + Value[k]) - lgamma(alphaxixj);
printf("in 1 idx-j %d-%d Value %f - m=%d k=%d \n",i,j,Value[k],m[i],k);
printf(" mlx %f \n",mlx);
//k++;
}
// xi
else {
if(j==0) {
k=i*3;
}
else if(j==1){
k=i*3+1;
}
else if(j==2) {
k=i*3+2;
}
mlx = mlx + lgamma(alphaxi) - lgamma(alphaxi + m[i]);
mlx = mlx + lgamma(alphaxi + m[i] + 1.0)+ (alphaxi + 1.0) * log(tauxi);
mlx = mlx - lgamma(alphaxi + 1.0)- (alphaxi + m[i] + 1.0) * log(tauxi + Value[k]);
printf("in 2 idx-j %d-%d Value %f - m=%d - k=%d \n",i,j,Value[k],m[i],k);
printf(" mlx %f \n",mlx);
//k++;
}
}
}
printf("mlx =%f \n",mlx);
bs[idx]=mlx;
printf("bs = %f .\n",bs[idx]);
}
}
printf("esci\n");
}
Here is the code:
int main (void){
printf("START");
FILE *pf;
const int N=9;
char fName[2083];
char *parents[3]={"0","1","2"};
char *traject[9]={"0-0","0-1","0-2","1-0","1-1","1-2","2-0","2-1","2-2"};
size_t parents_len;
size_t traject_len;
parents_len=sizeof(char)/sizeof(parents[0]);
traject_len=sizeof(char)/sizeof(traject[0]);
//possibile malloc
//pointer host to memory
char **parents_dev;
char **traject_dev;
//allocate on device
cudaMalloc((void **)&parents_dev,sizeof(char**)*parents_len);
cudaMalloc((void **)&traject_dev,sizeof(char**)*traject_len);
//host to Device
cudaMemcpy(parents_dev,parents,sizeof(char**)*parents_len,cudaMemcpyHostToDevice);
cudaMemcpy(traject_dev,traject,sizeof(char**)*traject_len,cudaMemcpyHostToDevice);
//Loop start
int file,Epoca;
float *bs;
float *bs_dev;
int file_size0=28;
int file_size1=55;
int file_size3=109;
//size_t size = N * sizeof(float);
bs=(float *)malloc(N * sizeof(float));
cudaMalloc((void **)&bs_dev, N * sizeof(float));
float *new_value0,*new_value0_dev;
new_value0=(float *)malloc(file_size0*N/3);
cudaMalloc((void **)&new_value0_dev, N * file_size0/3);
//
float *new_value1,*new_value1_dev;
new_value1=(float *)malloc(file_size0*N/3);
cudaMalloc((void **)&new_value1_dev, N * file_size0/3);
//
float *new_value2,*new_value2_dev;
new_value2=(float *)malloc(file_size0*N/3);
cudaMalloc((void **)&new_value2_dev, N * file_size0/3);
//
//one parent 1,2
float *new_value00,*new_value00_dev;
new_value00=(float *)malloc(file_size1*N/6);
cudaMalloc((void **)&new_value00_dev, N * file_size1/6);
//
float *new_value01,*new_value01_dev;
new_value01=(float *)malloc(file_size1*N/6);
cudaMalloc((void **)&new_value01_dev, N * file_size1/6);
//
float *new_value10,*new_value10_dev;
new_value10=(float *)malloc(file_size1*N/6);
cudaMalloc((void **)&new_value10_dev, N * file_size1/6);
//
float *new_value11,*new_value11_dev;
new_value11=(float *)malloc(file_size1*N/6);
cudaMalloc((void **)&new_value11_dev, N * file_size1/6);
//
float *new_value20,*new_value20_dev;
new_value20=(float *)malloc(file_size1*N/6);
cudaMalloc((void **)&new_value20_dev, N * file_size1/6);
//
float *new_value21,*new_value21_dev;
new_value21=(float *)malloc(file_size1*N/6);
cudaMalloc((void **)&new_value21_dev, N * file_size1/6);
//
//double parent
float *new_value000,*new_value000_dev;
new_value000=(float *)malloc(file_size3*N/12);
cudaMalloc((void **)&new_value000_dev, N * file_size3/12);
//
float *new_value001,*new_value001_dev;
new_value001=(float *)malloc(file_size3*N/12);
cudaMalloc((void **)&new_value001_dev, N * file_size3/12);
//
float *new_value010,*new_value010_dev;
new_value010=(float *)malloc(file_size3*N/12);
cudaMalloc((void **)&new_value010_dev, N * file_size3/12);
//
float *new_value011,*new_value011_dev;
new_value011=(float *)malloc(file_size3*N/12);
cudaMalloc((void **)&new_value011_dev, N * file_size3/12);
//
float *new_value100,*new_value100_dev;
new_value100=(float *)malloc(file_size3*N/12);
cudaMalloc((void **)&new_value100_dev, N * file_size3/12);
//
float *new_value101,*new_value101_dev;
new_value101=(float *)malloc(file_size3*N/12);
cudaMalloc((void **)&new_value101_dev, N * file_size3/12);
//
float *new_value110,*new_value110_dev;
new_value110=(float *)malloc(file_size3*N/12);
cudaMalloc((void **)&new_value110_dev, N * file_size3/12);
//
float *new_value111,*new_value111_dev;
new_value111=(float *)malloc(file_size3*N/12);
cudaMalloc((void **)&new_value111_dev, N * file_size3/12);
//
float *new_value200,*new_value200_dev;
new_value200=(float *)malloc(file_size3*N/12);
cudaMalloc((void **)&new_value200_dev, N * file_size3/12);
//
float *new_value201,*new_value201_dev;
new_value201=(float *)malloc(file_size3*N/12);
cudaMalloc((void **)&new_value201_dev, N * file_size3/12);
//
float *new_value210,*new_value210_dev;
new_value210=(float *)malloc(file_size3*N/12);
cudaMalloc((void **)&new_value210_dev, N * file_size3/12);
//
float *new_value211,*new_value211_dev;
new_value211=(float *)malloc(file_size3*N/12);
cudaMalloc((void **)&new_value211_dev, N * file_size3/12);
//int file;
for(file=0;file<4;file++){
int f, i, j, file_size=0, kk=0;
//file IO
sprintf(fName, "//home//user//prova%d.csv",file);
pf=fopen(fName,"r");
char *X;
char *PaX;
int Time;
char *pa;
char *xixj;
float val;
char buffer[BUFSIZ], *ptr;
if (pf)
{
/*
* Read each line from the file.
*/
while(fgets(buffer, sizeof buffer, pf)){
file_size++;
}
fclose(pf);
}
//variabile per kernel
float *Value, *Value_dev;
Value=(float *)malloc(file_size*N);
cudaMalloc((void **)&Value_dev, N * file_size);
//
pf=fopen(fName,"r");
if(pf)
{
printf("\nnumero righe file %d = %d\n",file,file_size);
char *state[file_size];
while(fgets(buffer, sizeof buffer, pf))
{
//printf("start csv \n");
char *token;
char *ptr = buffer;
const char end[2]=",";//fgets(buffer, sizeof buffer, pf);
token = strtok(ptr, end);
f=0;
/* walk through other tokens */
while( token != NULL )
{
if(f==0){
X=token;
// printf( "X %s\n", token );
}else if(f==1){
PaX=token;
// printf( "PaX %s\n", token );
}
else if(f==2){
Time=strtod(token,NULL);
// printf( "Time %f \n", token );
}
else if(f==3){
pa=token;
// printf( "pa %s \n", token );
}
else if(f==4){
xixj=(token);
// printf( "xixj %s \n", token );
}
else{
Value[kk]=strtod(&token[1], NULL);
// printf("Value %f \n", Value[kk]);
kk++;
}
token = strtok(NULL, end);
f++;
}
}
//
//insert in variable
if (file==0){
for (i=0;i<(file_size0-1)/3;++i){
new_value0[i]=Value[i+1];
cudaMemcpy(new_value0_dev,new_value0,N*sizeof(file_size0), cudaMemcpyHostToDevice);
new_value1[i]=Value[i + 1+((file_size0-1)/3)];
cudaMemcpy(new_value1_dev,new_value1,N*sizeof(file_size0), cudaMemcpyHostToDevice);
new_value2[i]=Value[i + (1+ 2*(file_size0-1)/3)];
cudaMemcpy(new_value2_dev,new_value2,N*sizeof(file_size0), cudaMemcpyHostToDevice);
// printf(" new_value- %d - %f - %f - %f \n",i,new_value0[i],new_value1[i],new_value2[i]);
}
}else if(file==1 || file==2){
for (i=0; i<(file_size1-1)/6;++i)
{
new_value00[i]=Value[i+1];
cudaMemcpy(new_value00_dev,new_value00,N*sizeof(file_size0), cudaMemcpyHostToDevice);
new_value01[i]=Value[i+ ((file_size0-1)/3)+1];
cudaMemcpy(new_value01_dev,new_value01,N*sizeof(file_size1), cudaMemcpyHostToDevice);
new_value10[i]=Value[i+ (2*(file_size1-1)/6)+1];
cudaMemcpy(new_value10_dev,new_value10,N*sizeof(file_size1), cudaMemcpyHostToDevice);
new_value11[i]=Value[i+ (3*(file_size1-1)/6)+1];
cudaMemcpy(new_value11_dev,new_value11,N*sizeof(file_size1), cudaMemcpyHostToDevice);
new_value20[i]=Value[i+ (4*(file_size1-1)/6)+1];
cudaMemcpy(new_value20_dev,new_value20,N*sizeof(file_size1), cudaMemcpyHostToDevice);
new_value21[i]=Value[i+ (5*(file_size1-1)/6)+1];
cudaMemcpy(new_value21_dev,new_value21,N*sizeof(file_size1), cudaMemcpyHostToDevice);
// printf(" new_value- %d - %f - %f - %f - %f - %f - %f \n",i,new_value00[i],new_value01[i],new_value10[i],new_value11[i],new_value20[i],new_value21[i]);
}
}else{
for (i=0; i<(file_size3-1)/12;++i)
{
new_value000[i]=Value[i+1];
cudaMemcpy(new_value000_dev,new_value000,N*sizeof(file_size3), cudaMemcpyHostToDevice);
new_value001[i]=Value[i+ ((file_size3-1)/12)+1];
cudaMemcpy(new_value001_dev,new_value001,N*sizeof(file_size3), cudaMemcpyHostToDevice);
new_value010[i]=Value[i+ (2*(file_size3-1)/12)+1];
cudaMemcpy(new_value010_dev,new_value010,N*sizeof(file_size3), cudaMemcpyHostToDevice);
new_value011[i]=Value[i+ (3*(file_size3-1)/12)+1];
cudaMemcpy(new_value011_dev,new_value011,N*sizeof(file_size3), cudaMemcpyHostToDevice);
new_value100[i]=Value[i+ (4*(file_size3-1)/12)+1];
cudaMemcpy(new_value100_dev,new_value100,N*sizeof(file_size3), cudaMemcpyHostToDevice);
new_value101[i]=Value[i+ (5*(file_size3-1)/12)+1];
cudaMemcpy(new_value101_dev,new_value101,N*sizeof(file_size3), cudaMemcpyHostToDevice);
new_value110[i]=Value[i+ (6*(file_size3-1)/12)+1];
cudaMemcpy(new_value110_dev,new_value110,N*sizeof(file_size3), cudaMemcpyHostToDevice);
new_value111[i]=Value[i+ (7*(file_size3-1)/12)+1];
cudaMemcpy(new_value111_dev,new_value111,N*sizeof(file_size3), cudaMemcpyHostToDevice);
new_value200[i]=Value[i+ (8*(file_size3-1)/12)+1];
cudaMemcpy(new_value200_dev,new_value200,N*sizeof(file_size3), cudaMemcpyHostToDevice);
new_value201[i]=Value[i+ (9*(file_size3-1)/12)+1];
cudaMemcpy(new_value201_dev,new_value201,N*sizeof(file_size3), cudaMemcpyHostToDevice);
new_value210[i]=Value[i+ (10*(file_size3-1)/12)+1];
cudaMemcpy(new_value210_dev,new_value210,N*sizeof(file_size3), cudaMemcpyHostToDevice);
new_value211[i]=Value[i+ (11*(file_size3-1)/12)+1];
cudaMemcpy(new_value211_dev,new_value211,N*sizeof(file_size3), cudaMemcpyHostToDevice);
// printf(" new_value- %d - %f - %f - %f - %f - %f - %f - %f - %f - %f - %f - %f - %f \n",i,new_value000[i],new_value001[i],new_value010[i],new_value011[i],new_value100[i],new_value101[i],new_value110[i],new_value111[i],new_value200[i],new_value201[i],new_value210[i],new_value211[i]);
}
}
}
}
//cudaMemcpy(Value_dev,Value,N*sizeof(file_size), cudaMemcpyHostToDevice);
//variable of kernel
//no parent
//START computation
printf("\nPRE KERNEL\n");
const int sz=(sizeof(parents)/sizeof(*(parents)));
const int dim=(sizeof(traject)/sizeof(*(traject)));
printf("%d - %d \n",sz, dim);
//chiamata kernel
int block_size = 3;
int n_blocks =1 ;
int *m, *m_dev;
m=(int *)malloc(sz*N);
cudaMalloc((void **)&m_dev, N * sz);
float *trns_dev;
cudaMalloc((void **)&trns_dev, N * dim);
int i;
for(i=0;i<(file_size0-1)/3;i++){
printf(" new_value- %d - %f - %f - %f \n",i,new_value0[i],new_value1[i],new_value2[i]);
}
printf("\n");
for(i=0;i<(file_size1-1)/6;i++){
printf(" new_value- %d - %f - %f - %f - %f - %f - %f \n",i,new_value00[i],new_value01[i],new_value10[i],new_value11[i],new_value20[i],new_value21[i]);
}
printf("\n");
for(i=0;i<(file_size3-1)/12;i++){
printf(" new_value- %d - %f - %f - %f - %f - %f - %f - %f - %f - %f - %f - %f - %f \n",i,new_value000[i],new_value001[i],new_value010[i],new_value011[i],new_value100[i],new_value101[i],new_value110[i],new_value111[i],new_value200[i],new_value201[i],new_value210[i],new_value211[i]);
}
for(Epoca=0; Epoca<3; Epoca++){
bs=0;
float bf=0;
cudaMalloc((void **)&bf, N * sz);
cudaMemcpy(bs_dev,bs,N*sizeof(float), cudaMemcpyHostToDevice);
if(Epoca==0){
calculateMLpa<<<n_blocks, block_size >>>(N,bs_dev,new_value0_dev,1.0,0.1,sz,dim,m_dev);
cudaDeviceSynchronize();
cudaMemcpy(bs,bs_dev,N*sizeof(float), cudaMemcpyDeviceToHost);
cudaMemcpy(m,m_dev,N*sizeof(float), cudaMemcpyDeviceToHost);
bf =+ bs[0];
printf("score= %f m0 = %d, m1 = %d, m2 = %d \n\n", bf, m[0], m[1], m[2]);
calculateMLpa<<<n_blocks, block_size >>>(N,bs_dev,new_value00_dev,1.0,0.1,sz,dim,m_dev);
cudaDeviceSynchronize();
cudaMemcpy(bs,bs_dev,N*sizeof(float), cudaMemcpyDeviceToHost);
cudaMemcpy(m,m_dev,N*sizeof(float), cudaMemcpyDeviceToHost);
bf =+ bs[0];
printf("score= %f \n", bf);
}
printf("score %d= %f \n",Epoca, bf);
}
free(bs_dev);
}
I think that I can parallelize this with stream but I have never used it before. I have watched this for start.
1 answers
solution1
0 ACCPTED 2015-11-06 13:26:22
It sounds like you should use parallel CUDA streams .
An interesting option:
CUDA 7 introduces a new option, the per-thread default stream, that has two effects. First, it gives each host thread its own default stream. This means that commands issued to the default stream by different host threads can run concurrently.
Also noteworthy:
As described by the CUDA C Programming Guide, asynchronous commands return control to the calling host thread before the device has finished the requested task (they are non-blocking). These commands are:
Kernel launches; Memory copies between two addresses to the same device memory; Memory copies from host to device of a memory block of 64 KB or less; Memory copies performed by functions with the Async suffix; Memory set function calls.
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