this is my first attempt at a CUDA program. This is what it's supposed to do:
Receive 1D Pixel array from host memory
Each Pixel is processed by one thread: it is thread-safe because only "val" is read and only "newval" is updated. Wait for sync.
Each Pixel is processed by one thread: copy "newval" to "val."
Write this array back to host memory.
Repeat 2-4 for several different frames.
What happens, however, is that only a couple of variables, out of about 32000, in the new arrays seem to have decent values at all; the rest are zero.
I've removed the calculations for brevity.
__global__ void kernel(Pixel *array, float dt)
{
const unsigned int tid = threadIdx.x;
Pixel *point = array + tid;
//DO A BUNCH OF CALCULATIONS ON PIXEL KIND OF LIKE THIS
point->newval = point->val + foo;
}
__global__ void copykernel(Pixel *array)
{
const unsigned int tid = threadIdx.x;
Pixel *point = array + tid;
//COPY THE NEWVALS OVER TO THE OLD VALS IN PREPARATION FOR THE NEXT FRAME
point->val = point->newval;
}
extern "C" bool runIt(const int argc, const char **argv, Pixel *inarray, Pixel **outarrays, int arraysize, int numframes, float dt)
{
int memsize = arraysize*sizeof(Pixel);
int i=0;
Pixel *array;
cudaMalloc((void **) &array, memsize);
cudaMemcpy(array, inarray, memsize, cudaMemcpyHostToDevice);
int numthreads = arraysize;
dim3 grid(1,1,1);
dim3 threads(numthreads,1,1);
for(i=0;i<numframes;i++)
{
kernel<<<grid, threads>>>((Pixel *) array, dt);
cudaThreadSynchronize();
copykernel<<<grid, threads>>>((Pixel *) array);
cudaThreadSynchronize();
cudaMemcpy(array, outarrays[i], memsize, cudaMemcpyDeviceToHost);
}
cudaFree(array);
return true;
}
I have a suspicion that I'm setting up the parameters for the device incorrectly, or else I'm getting one of the device-specific keywords wrong or forgetting a crucial step. Does anything jump out at you?
I don't think you can run that many threads, and if you can, its not a good idea. Try setting the number of threads to 256 (16x16 for 2D), then choosing gridsize based on your input size.
dim3 threads(256,1,1);
dim3 grid(arraysize/threads.x,1,1); //Careful of integer division, this is just for example
Also your second copy is incorrect. You need to switch array and out_arrays
cudaMemcpy(outarrays[i], array, memsize, cudaMemcpyDeviceToHost);
Related
I have written a CUDA kernel and when I copy of an array of shorts to device memory and then pass it to the kernel it doesn't work. The simplified code below expresses my issue.
KernelCaller()
{
const int size = 1;
short hostArray[size]{41};
short* devPointer;
cudaMalloc((void**)&devicePointer, size * sizeof(short));
cudaMemcpy(devPointer, hostArray, size * sizeof(short), cudaMemcpyHostToDevice);
cudaKernel<<<1,1>>>(devPointer);
}
__global__
void cudaKernel(short* arr)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
short val = arr[idx];
}
At this point the value of val is 1063714857 and what I want it to be is 41.
I assume the issue is 41 in hex is 0x29 and the value I have is 0x3F670029 so it looks like it read too many bytes cause the 0x29 is at the beginning. When I switch to an array of floats it works perfectly, but I was trying to save memory. Does CUDA not allow an array of shorts?
I have implemented your code and getting the output as expected.
Here's the code
#include<stdio.h>
__global__ void cudaKernel(short* arr)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
short val = arr[idx];
# if __CUDA_ARCH__>=200
printf("Inside kernel %d\n",val);
#endif
arr[idx] = val;
}
int main()
{
const int size = 1;
short hostArray[size]{41};
printf("Before kernel call %d\n",hostArray[0]);
short *devPointer;
cudaMalloc((void**)&devPointer, size * sizeof(short));
cudaMemcpy(devPointer, hostArray, size * sizeof(short), cudaMemcpyHostToDevice);
cudaKernel<<<1,1>>>(devPointer);
cudaMemcpy(hostArray, devPointer, size * sizeof(short), cudaMemcpyDeviceToHost);
printf("After kernel call %d\n",hostArray[0]);
cudaFree(devPointer);
return 0;
}
And the output is
Before kernel call 41
Inside kernel 41
After kernel call 41
So, yes we can pass array of shorts into a CUDA kernel.
I am returning a two-dimensional structure after computation on a kernel, from device to host.
HANDLE_ERROR(cudaMemcpy(Pixel,Pixel_gpu,img_wd*img_ht*sizeof(pixel),cudaMemcpyDeviceToHost));
Pixel is declared on host, Pixel_gpu is allocated on device, as below:
**Pixel_gpu;
HANDLE_ERROR(cudaMalloc(&Pixel_gpu,img_wd*img_ht*sizeof(pixel)));
pixel **Pixel = (pixel**)malloc((img_ht)*sizeof(pixel*));
for(int i=0;i<(img_ht);i++)
Pixel[i]=(pixel*)malloc((img_wd)*sizeof(pixel));
Using this I end up getting illegal memory access error.
Trying a similar memory alignment for result, doesn't help either.
pixel *Pixel_res = (pixel*)malloc(img_wd*img_ht*sizeof(pixel));
HANDLE_ERROR(cudaMemcpy(Pixel_res,Pixel_gpu,img_wd*img_ht*sizeof(pixel),cudaMemcpyDeviceToHost));
Kernel launching:
cudaDeviceProp prop;
HANDLE_ERROR(cudaGetDeviceProperties(&prop, 0));
int thread_block=sqrt(prop.maxThreadsPerBlock);
dim3 DimGrid(ceil(img_wd/thread_block),ceil(img_ht/thread_block),1);
dim3 DimBlock(sqrt(prop.maxThreadsPerBlock),sqrt(prop.maxThreadsPerBlock),1);
//allocating gpu memory
pixel **Pixel_tmp_gpu, **Pixel_gpu;
HANDLE_ERROR(cudaMalloc(&Pixel_tmp_gpu,img_wd*img_ht*sizeof(pixel)));
HANDLE_ERROR(cudaMalloc(&Pixel_gpu,img_wd*img_ht*sizeof(pixel)));
float **kernel0_gpu, **kernel1_gpu;
HANDLE_ERROR(cudaMalloc(&kernel0_gpu,k*1*sizeof(float)));
HANDLE_ERROR(cudaMalloc(&kernel1_gpu,1*k*sizeof(float)));
cout<<"memory allocated"<<endl;
//copying needed data
HANDLE_ERROR(cudaMemcpy(Pixel_tmp_gpu,Pixel_tmp,img_wd*img_ht*sizeof(pixel),cudaMemcpyHostToDevice));
HANDLE_ERROR(cudaMemcpy(Pixel_gpu,Pixel,img_wd*img_ht*sizeof(pixel),cudaMemcpyHostToDevice));
HANDLE_ERROR(cudaMemcpy(kernel0_gpu,kernel0,k*1*sizeof(float),cudaMemcpyHostToDevice));
HANDLE_ERROR(cudaMemcpy(kernel1_gpu,kernel1,1*k*sizeof(float),cudaMemcpyHostToDevice));
cout<<"memory transfers done"<<endl;
vertical_conv<<<DimGrid,DimBlock>>>(Pixel_gpu, Pixel_tmp_gpu,img_wd, img_ht,kernel0_gpu,k);
time_t vertical_convolution=time(NULL);
cout<<" vertical_convolution time: "<<double(vertical_convolution - reading_file)<<"sec"<<endl;
horizontal_conv<<<DimGrid,DimBlock>>>(Pixel_tmp_gpu, Pixel_gpu, img_wd, img_ht, kernel1_gpu, k);
time_t horizontal_convolution=time(NULL);
cout<<" horizontal convolution time:" <<double(horizontal_convolution-vertical_convolution)<<" sec"<<endl;
pixel *Pixel_res = (pixel*)malloc(img_wd*img_ht*sizeof(pixel));
HANDLE_ERROR(cudaMemcpy(Pixel_res,Pixel_gpu,img_wd*img_ht*sizeof(pixel),cudaMemcpyDeviceToHost));
The functions used:
struct pixel //to store RGB values
{
unsigned char r;
unsigned char g;
unsigned char b;
};
static void HandleError( cudaError_t err, const char *file, int line ) {
if (err != cudaSuccess) {
cout<<cudaGetErrorString(err)<<" in "<< file <<" at line "<< line<<endl;
}
}
#define HANDLE_ERROR( err ) (HandleError( err, __FILE__, __LINE__ ))
__device__ void padding(pixel** Pixel_val, int x_coord, int y_coord, int img_width, int img_height, pixel Px) //padding the image,depending on pixel coordinates, can be replaced by reflect for better result //currently zero padding
{
if(x_coord<img_width && y_coord<img_height && x_coord>=0 && y_coord>=0)
Px=Pixel_val[y_coord][x_coord];
}
The vertical convolution:
__global__ void vertical_conv(pixel** Pixel_in, pixel** Pixel_out,int img_wd, int img_ht, float** kernel, int k)
{
float tmp_r, tmp_g, tmp_b;
pixel pix_val;
pix_val.r=0;pix_val.g=0;pix_val.b=0;
int row=blockIdx.y*blockDim.y + threadIdx.y;
int col = blockIdx.x*blockDim.x + threadIdx.x;
if(row<img_ht && col<img_wd){
tmp_r=0, tmp_g=0, tmp_b=0;
for(int l=0;l<k;l++)
{
padding(Pixel_in, col, row+l-(k-1)/2, img_wd, img_ht, pix_val);
tmp_r+=pix_val.r * kernel[l][0];
tmp_b+=pix_val.b * kernel[l][0];
tmp_g+=pix_val.g * kernel[l][0];
}
Pixel_out[row][col].r=tmp_r;
Pixel_out[row][col].g=tmp_g;
Pixel_out[row][col].b=tmp_b;
}
}
The horizontal convolution:
__global__ void horizontal_conv(pixel** Pixel_in, pixel** Pixel_out, int img_wd, int img_ht, float** kernel, int k)
{
float tmp_r, tmp_b, tmp_g;
pixel pix_val;
pix_val.r=0;pix_val.g=0;pix_val.b=0;
//horizontal convolution
int row=blockIdx.y*blockDim.y + threadIdx.y;
int col = blockIdx.x*blockDim.x + threadIdx.x;
tmp_r=0, tmp_g=0, tmp_b=0;
if(row<img_ht && col<img_wd)
{
for(int l=0; l<k;l++)
{
padding(Pixel_in, col+l-(k-1)/2, row, img_wd, img_ht, pix_val);
tmp_r+=pix_val.r * kernel[0][l];
tmp_g+=pix_val.g * kernel[0][l];
tmp_b+=pix_val.b * kernel[0][l];
}
Pixel_out[row][col].r=tmp_r;
Pixel_out[row][col].g=tmp_g;
Pixel_out[row][col].b=tmp_b;
}
}
Can someone help me know what could be wrong here?
The Pixel_gpu is be one contiguous memory block, consisting of w*h elements of type pixel. Its size is
sizeOfDeviceMemory = img_wd * img_ht * sizeof(pixel)
On contrast to that, Pixel on the CPU side is an "array of pointers": The Pixel pointer points to h elements of type pixel*. Its size is
sizeOfHostMemory = img_ht * sizeof(pixel*)
Clearly, these sizes are different, and trying to write sizeOfDeviceMemory bytes to this pointer causes an illegal access.
Usually, you should allocate your memory on the host as one contiguous block as well:
pixel* Pixel = (pixel*)malloc(img_wd * img_ht * sizeof(pixel));
Then you can copy the memory to this pointer using the cudaMemcpy call that you already have.
If having a pixel* on the host is not OK for you, and you urgently need a pixel** (for example, to pass it to some other function), then you can create an "array of pointers" like you had before, but not allocate new memory for each row, but instead, let each pointer point to one "row" of the single, contiguous pixel block.
I am trying to use dynamic parallelism in cuda. I am in a situation such that parent kernel has a variable that needs to be passed to child for further computation. I have gone through the resources in web
here
and it mentions that local variables cannot be passed to the child kernal and has mentioned the ways to pass variables and I have tried to pass the pass the variable as
#include <stdio.h>
#include <cuda.h>
__global__ void square(float *a, int N)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if(N==10)
{
a[idx] = a[idx] * a[idx];
}
}
// Kernel that executes on the CUDA device
__global__ void first(float *arr, int N)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
int n=N; // this value of n can be changed locally and need to be passed
printf("%d\n",n);
cudaMalloc((void **) &n, sizeof(int));
square <<< 1, N >>> (arr, n);
}
// main routine that executes on the host
int main(void)
{
float *a_h, *a_d; // Pointer to host & device arrays
const int N = 10; // Number of elements in arrays
size_t size = N * sizeof(float);
a_h = (float *)malloc(size); // Allocate array on host
cudaMalloc((void **) &a_d, size); // Allocate array on device
// Initialize host array and copy it to CUDA device
for (int i=0; i<N; i++) a_h[i] = (float)i;
cudaMemcpy(a_d, a_h, size, cudaMemcpyHostToDevice);
// Do calculation on device:
first <<< 1, 1 >>> (a_d, N);
//cudaThreadSynchronize();
// Retrieve result from device and store it in host array
cudaMemcpy(a_h, a_d, sizeof(float)*N, cudaMemcpyDeviceToHost);
// Print results
for (int i=0; i<N; i++) printf("%d %f\n", i, a_h[i]);
// Cleanup
free(a_h); cudaFree(a_d);
}
and the value of parent to child kernel is not passed . how can I pass the value of local variable. Is there any way to do so?
This operation is not appropriate:
int n=N; // this value of n can be changed locally and need to be passed
cudaMalloc((void **) &n, sizeof(int)); // illegal
It is not appropriate in host code, nor in device code. n is an int variable. You are not supposed to assign a pointer to it. When you attempt to do so in a 64-bit environment, you are attempting to write a 64-bit pointer on top of a 32-bit int quantity. It will not work.
It's not clear why you would need it anyway. n is an integer parameter presumably specifying the size of your arr array of float. You don't need to allocate anything on top of it.
If you had run this code with cuda-memcheck, you could easily discover that error. You can also do proper cuda error checking in device code in exactly the same fashion as you do it in host code.
When I comment out that cudaMalloc line in the first kernel, your code runs correctly for me.
I found some difficulty when I try to access a global array from function that's executed from device:
float globTemp[3][3] = "some value in here";
__device__ float* globTemp_d;
__global__ void compute(int *a, int w)
{
int x = threadIdx.x + blockDim.x * blockIdx.x;
int y = threadIdx.y + blockDim.y * blockIdx.y;
int i = y*w+x;
if(x<3 && y<3)
a[i] = 1+globTemp_d[i];
}
int hostFunc(){
float *a_d;
cudaMalloc((void**)&a_d, 3*3*sizeof(int));
cudaMalloc((void**)&globTemp_d, 3*3*sizeof(int));
cudaMemcpy(globTemp_d,globTemp, 3*3*sizeof(float), cudaMemcpyHostToDevice);
compute<<<1,1>>>(a_d,3);
cudaMemcpy(a,a_d, 3*3*sizeof(float), cudaMemcpyDeviceToHost);
}
However, I get seg fault when i try to access globTemp_d[i]. Am I doing something wrong in here?
There are a variety of problems with your code:
Your grid is a 1D grid of 1D threadblocks (in fact you are launching a single block of 1 thread) but your kernel is written as if it were expecting a 2D threadblock structure (using .x and .y built-in variables). A single thread won't get the work done certainly, and a 1D threadblock won't work with your kernel code.
__device__ variables are not accessed with cudaMalloc and cudaMemcpy. We use a different set of API calls like cudaMemcpyToSymbol.
You're not doing any cuda error checking which is always recommended when you're having difficulty. You should do cuda error checking on both API calls and kernel calls.
You're mixing float variables (a_d ) with int variables in the kernel parameters (int *a) so I don't think this code would compile without at least a warning. And that can lead to strange behavior of course if you ignore it.
This is the closest I could come to your code while fixing all the errors:
#include <stdio.h>
__device__ float* globTemp_d;
__global__ void compute(float *a, int w)
{
int x = threadIdx.x + blockDim.x * blockIdx.x;
int y = threadIdx.y + blockDim.y * blockIdx.y;
int i = (y*w)+x;
if((x<3) && (y<3))
a[i] = 1.0f+globTemp_d[i];
}
int main(){
float *a_d, *d_globTemp;
float globTemp[3][3] = {0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.6f, 0.7f, 0.8f, 0.9f};
float a[(3*3)];
dim3 threads(3,3);
dim3 blocks(1);
cudaMalloc((void**)&a_d, 3*3*sizeof(float));
cudaMalloc((void**)&d_globTemp, 3*3*sizeof(float));
cudaMemcpy(d_globTemp,globTemp, 3*3*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpyToSymbol(globTemp_d, &d_globTemp, sizeof(float *));
compute<<<blocks,threads>>>(a_d,3);
cudaMemcpy(a,a_d, 3*3*sizeof(float), cudaMemcpyDeviceToHost);
printf("results:\n");
for (int i = 0; i<(3*3); i++)
printf("a[%d] = %f\n", i, a[i]);
return 0;
}
This code can be simplified by dispensing with the __device__ variable and just passing d_globTemp as a parameter to the kernel, and using it in place of references to globTemp_d. However I did not make that simplification.
I really do not understand why the output for the below code is not a and b.
#include<cutil.h>
#include<iostream>
__global__ void p(unsigned char **a){
unsigned char temp[2];
temp[0] = 'a';
temp[1] = 'b';
a[0] = temp;
}
void main(){
unsigned char **a ;
cudaMalloc((void**)&a, sizeof(unsigned char*));
p<<<1,1>>>(a);
unsigned char **c;
unsigned char b[2];
cudaMemcpy(c, a, sizeof(unsigned char *), cudaMemcpyDeviceToHost);
cudaMemcpy(b, c[0], 2*sizeof(unsigned char), cudaMemcpyDeviceToHost);
for( int i=0 ; i < 2; i++){
printf("%c\n", b[i]);
}
getchar();
}
what is wrong with my logic?
Let's leave out CUDA for now. Let's just make a function that writes data to a user-provided array. The user passes the array via a pointer:
void fill_me_up(int * dst)
{
// We sure hope that `dst` points to a large enough area of memory!
dst[0] = 28;
dst[1] = 75;
}
Now, what you're doing with the local variable doesn't make sense, because you want to use the address of a local variable, which becomes invalid after you leave the function scope. The next best thing you could do is memcpy(), or some equivalent C++ algorithm:
void fill_me_up_again(int * dst)
{
int temp[] = { 28, 75 };
memcpy((void *)dst, (const void *)temp, sizeof(temp));
}
OK, now on to calling that function: We first must provide the target memory, and then pass a pointer:
int main()
{
int my_memory[2]; // here's our memory -- automatic local storage
fill_me_up(my_memory); // OK, array decays to pointer-to-beginning
fill_me_up(&my_memory[0]); // A bit more explicit
int * your_memory = malloc(sizeof(int) * 2); // more memory, this time dynamic
fill_me_up_again(your_memory);
/* ... */
free(your_memory);
}
(In C++ you would probably have uses new int[2] and delete your_memory instead, but by using C malloc() the connection to CUDA hopefully becomes clear.)
When you're moving fill_me_up to the CUDA device, you have to give it a device pointer rather than a host pointer, so you have to set that one up first and afterwards copy the results back out, but that's about the only change.