Cuda kernel to compute squares of integers in an array

Question

I am learning some basic CUDA programming. I am trying to initialize an array on the Host with host_a[i] = i. This array consists of N = 128 integers. I am launching a kernel with 1 block and 128 threads per block, in which I want to square the integer at index i.

My questions are:

1. How do I come to know whether the kernel gets launched or not? Can I use printf within the kernel?

2. The expected output for my program is a space-separated list of squares of integers -

1 4 9 16 ... .

What's wrong with my code, since it outputs 1 2 3 4 5 ...

Code:

#include <iostream>
#include <numeric>
#include <stdlib.h>
#include <cuda.h>

const int N = 128;

__global__ void f(int *dev_a) {
    unsigned int tid = threadIdx.x;

    if(tid < N) {
        dev_a[tid] = tid * tid;
    }
}

int main(void) {

    int host_a[N];
    int *dev_a;
    cudaMalloc((void**)&dev_a, N * sizeof(int));
    for(int i = 0 ; i < N ; i++) {
        host_a[i] = i;
    }
    cudaMemcpy(dev_a, host_a, N * sizeof(int), cudaMemcpyHostToDevice);
    f<<<1, N>>>(dev_a);

    cudaMemcpy(host_a, dev_a, N * sizeof(int), cudaMemcpyDeviceToHost);

    for(int i = 0 ; i < N ; i++) {
        printf("%d ", host_a[i]);
    }
}

Answer 1

How do I come to know whether the kernel gets launched or not? Can I use printf within the kernel?

You can use printf in device code (as long as you #include <stdio.h>) on any compute capability 2.0 or higher GPU. Since CUDA 7 and CUDA 7.5 only support those types of GPUs, if you are using CUDA 7 or CUDA 7.5 (successfully) then you can use printf in device code.

What's wrong with my code?

As identified in the comments, there is nothing "wrong" with your code, if run on a properly set up machine. To address your previous question "How do I come to know whether the kernel gets launched or not?", the best approach in my opinion is to use proper cuda error checking, which has numerous benefits besides just telling you whether your kernel launched or not. In this case it would also give a clue as to the failure being an improper CUDA setup on your machine. You can also run CUDA codes with cuda-memcheck as a quick test as to whether any runtime errors are occurring.