CODEWITHSUNDEEP
c++parallel-processingcudagpu
hjfeldy
hjfeldy

Reputation: 9

CUDA program fails to execute correctly despite that deviceQuery test passed

I have just installed the nvidia CUDA toolkit on my fresh Ubuntu 20.04 installation. Nvcc compiles CUDA programs, and they run without crashing. However, none of the results are correct.

Here is the output of the test script (deviceQuery) that Nvidia provides:

./deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "NVIDIA GeForce GTX 770"
  CUDA Driver Version / Runtime Version          11.4 / 11.4
  CUDA Capability Major/Minor version number:    3.0
  Total amount of global memory:                 1997 MBytes (2093875200 bytes)
  (008) Multiprocessors, (192) CUDA Cores/MP:    1536 CUDA Cores
  GPU Max Clock rate:                            1110 MHz (1.11 GHz)
  Memory Clock rate:                             3505 Mhz
  Memory Bus Width:                              256-bit
  L2 Cache Size:                                 524288 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
  Maximum Layered 1D Texture Size, (num) layers  1D=(16384), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(16384, 16384), 2048 layers
  Total amount of constant memory:               65536 bytes
  Total amount of shared memory per block:       49152 bytes
  Total shared memory per multiprocessor:        49152 bytes
  Total number of registers available per block: 65536
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  2048
  Maximum number of threads per block:           1024
  Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
  Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
  Maximum memory pitch:                          2147483647 bytes
  Texture alignment:                             512 bytes
  Concurrent copy and kernel execution:          Yes with 1 copy engine(s)
  Run time limit on kernels:                     Yes
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Disabled
  Device supports Unified Addressing (UVA):      Yes
  Device supports Managed Memory:                Yes
  Device supports Compute Preemption:            No
  Supports Cooperative Kernel Launch:            No
  Supports MultiDevice Co-op Kernel Launch:      No
  Device PCI Domain ID / Bus ID / location ID:   0 / 2 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 11.4, CUDA Runtime Version = 11.4, NumDevs = 1 
Result = PASS

And here is the very simple vector addition program I am trying to run:

#include <cuda_runtime.h>

#include <iostream>
#include <cuda.h>
using namespace std;

int *a, *b;  // host data
int *c;  // results

__global__ void vecAdd(int *A,int *B,int *C)
{
   int i = blockIdx.x * blockDim.x + threadIdx.x;
   C[i] = A[i] + B[i];
}

int main(int argc,char **argv)
{
   printf("Begin \n");
   int n=1000000;
   int nBytes = n*sizeof(int);
   int block_size, block_no;
   a = (int *)malloc(nBytes);
   b = (int *)malloc(nBytes);
   c = (int *)malloc(nBytes);
   int *a_d,*b_d,*c_d;
   block_size=1000;
   block_no = n/block_size;
   for(int i=0;i<n;i++) {
      a[i]=i;
      b[i]=i;
   }

   printf("Allocating device memory on host..\n");
   cudaMalloc((void **)&a_d,n*sizeof(int));
   cudaMalloc((void **)&b_d,n*sizeof(int));

   printf("Copying to device..\n");
   cudaMemcpy(a_d,a,n*sizeof(int),cudaMemcpyHostToDevice);
   cudaMemcpy(b_d,b,n*sizeof(int),cudaMemcpyHostToDevice);

   printf("Doing GPU Vector add\n");
   vecAdd<<<block_no,block_size>>>(a_d,b_d,c_d);

   cudaMemcpy(c,c_d,n*sizeof(int),cudaMemcpyDeviceToHost);
   for(int i = 0; i < 10; i++) {
        std::cout << a[i] << " + " << b[i] << " = " << c[i] << std::endl;
   }
   cudaFree(a_d);
   cudaFree(b_d);
   cudaFree(c_d);
   free(a);
   free(b);
   free(c);
   return 0;
}

And last but not least, here is it's faulty output:


Begin
Allocating device memory on host..
Copying to device..
Doing GPU Vector add
0 + 0 = 0
1 + 1 = 0
2 + 2 = 0
3 + 3 = 0
4 + 4 = 0
5 + 5 = 0
6 + 6 = 0
7 + 7 = 0
8 + 8 = 0
9 + 9 = 0

Any help is greatly appreciated.

Upvotes: 0

Views: 393

Answers (1)

alter_igel
alter_igel

Reputation: 7202

You never allocate storage for c on the device. Try adding

cudaMalloc((void **)&c_d,n*sizeof(int));

before you call the CUDA kernel.

Upvotes: 3

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