CUDA：固定内存零复制问题

Question

I tried the code in this link Is CUDA pinned memory zero-copy? The one who asked claims the program worked fine for him But does not work the same way on mine the values does not change if I manipulate them in the kernel.

Basically my problem is, my GPU memory is not enough but I want to do calculations which require more memory. I my program to use RAM memory, or host memory and be able to use CUDA for calculations. The program in the link seemed to solve my problem but the code does not give output as shown by the guy.

Any help or any working example on Zero copy memory would be useful.

Thank you

__global__ void testPinnedMemory(double * mem)
{
double currentValue = mem[threadIdx.x];
printf("Thread id: %d, memory content: %f\n", threadIdx.x, currentValue);
mem[threadIdx.x] = currentValue+10;
}

void test() 
{
const size_t THREADS = 8;
double * pinnedHostPtr;
cudaHostAlloc((void **)&pinnedHostPtr, THREADS, cudaHostAllocDefault);

//set memory values
for (size_t i = 0; i < THREADS; ++i)
    pinnedHostPtr[i] = i;

//call kernel
dim3 threadsPerBlock(THREADS);
dim3 numBlocks(1);
testPinnedMemory<<< numBlocks, threadsPerBlock>>>(pinnedHostPtr);

//read output
printf("Data after kernel execution: ");
for (int i = 0; i < THREADS; ++i)
    printf("%f ", pinnedHostPtr[i]);    
printf("\n");
}

Answer 1

First of all, to allocate ZeroCopy memory, you have to specify cudaHostAllocMapped flag as an argument to cudaHostAlloc .

cudaHostAlloc((void **)&pinnedHostPtr, THREADS * sizeof(double), cudaHostAllocMapped);

Still the pinnedHostPointer will be used to access the mapped memory from the host side only. To access the same memory from device, you have to get the device side pointer to the memory like this:

double* dPtr;
cudaHostGetDevicePointer(&dPtr, pinnedHostPtr, 0);

Pass this pointer as kernel argument.

testPinnedMemory<<< numBlocks, threadsPerBlock>>>(dPtr);

Also, you have to synchronize the kernel execution with the host to read the updated values. Just add cudaDeviceSynchronize after the kernel call.

The code in the linked question is working, because the person who asked the question is running the code on a 64 bit OS with a GPU of Compute Capability 2.0 and TCC enabled. This configuration automatically enables the Unified Virtual Addressing feature of the GPU in which the device sees host + device memory as a single large memory instead of separate ones and host pointers allocated using cudaHostAlloc can be passed directly to the kernel.

In your case, the final code will look like this:

#include <cstdio>

__global__ void testPinnedMemory(double * mem)
{
    double currentValue = mem[threadIdx.x];
    printf("Thread id: %d, memory content: %f\n", threadIdx.x, currentValue);
    mem[threadIdx.x] = currentValue+10;
}

int main() 
{
    const size_t THREADS = 8;
    double * pinnedHostPtr;
    cudaHostAlloc((void **)&pinnedHostPtr, THREADS * sizeof(double), cudaHostAllocMapped);

    //set memory values
    for (size_t i = 0; i < THREADS; ++i)
        pinnedHostPtr[i] = i;

    double* dPtr;
    cudaHostGetDevicePointer(&dPtr, pinnedHostPtr, 0);

    //call kernel
    dim3 threadsPerBlock(THREADS);
    dim3 numBlocks(1);
    testPinnedMemory<<< numBlocks, threadsPerBlock>>>(dPtr);
    cudaDeviceSynchronize();

    //read output
    printf("Data after kernel execution: ");
    for (int i = 0; i < THREADS; ++i)
        printf("%f ", pinnedHostPtr[i]);    
    printf("\n");

    return 0;
}