CUDA by example/chapter 4

Normal single threaded programming

#define N   10

void add( int *a, int *b, int *c ) {
    int tid = 0;    // this is CPU zero, so we start at zero
    while (tid < N) {
        c[tid] = a[tid] + b[tid];
        tid += 1;   // we have one CPU, so we increment by one
    }
}

int main( void ) {
    int a[N], b[N], c[N];

    // fill the arrays 'a' and 'b' on the CPU
    for (int i=0; i<N; i++) {
        a[i] = -i;
        b[i] = i * i;
    }

    add( a, b, c );

    // display the results
    for (int i=0; i<N; i++) {
        printf( "%d + %d = %d\n", a[i], b[i], c[i] );
    }

    return 0;
}

Basic multithreaded application

CPU 1	CPU 2
void add( int *a, int *b, int *c ) { int tid = 0; while (tid < N) { c[tid] = a[tid] + b[tid] ; tid += 2; } }	void add( int *a, int *b, int *c ) { int tid = 1; while (tid < N) { c[tid] = a[tid] + b[tid] ; tid += 2; } }

CUDA threaded example

The __global__ tells the compiler nvcc compiler that the add subroutine should run on the device.

#define N   10

__global__ void add( int *a, int *b, int *c ) {
    int tid = blockIdx.x;    // this thread handles the data at its thread id
    if (tid < N)
        c[tid] = a[tid] + b[tid];
}

int main( void ) {
    int a[N], b[N], c[N];
    int *dev_a, *dev_b, *dev_c;

    // allocate the memory on the GPU
    cudaMalloc( (void**)&dev_a, N * sizeof(int) );
    cudaMalloc( (void**)&dev_b, N * sizeof(int) );
    cudaMalloc( (void**)&dev_c, N * sizeof(int) );

    // fill the arrays 'a' and 'b' on the CPU
    for (int i=0; i<N; i++) {
        a[i] = -i;
        b[i] = i * i;
    }

    // copy the arrays 'a' and 'b' to the GPU
    cudaMemcpy( dev_a, a, N * sizeof(int),
                              cudaMemcpyHostToDevice );
    cudaMemcpy( dev_b, b, N * sizeof(int),
                              cudaMemcpyHostToDevice );

    add<<<N,1>>>( dev_a, dev_b, dev_c );

    // copy the array 'c' back from the GPU to the CPU
    cudaMemcpy( c, dev_c, N * sizeof(int),
                              cudaMemcpyDeviceToHost );

    // display the results
    for (int i=0; i<N; i++) {
        printf( "%d + %d = %d\n", a[i], b[i], c[i] );
    }

    // free the memory allocated on the GPU
    cudaFree( dev_a );
    cudaFree( dev_b );
    cudaFree( dev_c );

    return 0;
}

Memory on the host and memory on the device

As a generel rule: Host pointers can access memory from host code, and device pointers can access memory from device code.

• You can pass pointers allocated with cudaMalloc( ) to functions that execute on the device.
• You can use pointers allocated with cudaMalloc() to read or write memory from code that executes on the device.
• You can pass pointers allocated with cudaMalloc( ) to functions that execute on the host.
• You cannot use pointers allocated with cudaMalloc() to read or write memory from code that executes on the host.