Compute Unified Device Architecture, aka Compute Unified Device Architecture, is an actively used programming language created in 2007. CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia. It allows software developers and software engineers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing – an approach termed GPGPU (General-Purpose computing on Graphics Processing Units). The CUDA platform is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements, for the execution of compute kernels. Read more on Wikipedia...

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Example code from the Hello World Collection:

// Hello world in CUDA

#include <stdio.h>
 
const int N = 16; 
const int blocksize = 16; 
 
__global__ 
void hello(char *a, int *b) 
{
	a[threadIdx.x] += b[threadIdx.x];
}
 
int main()
{
	char a[N] = "Hello \0\0\0\0\0\0";
	int b[N] = {15, 10, 6, 0, -11, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
 
	char *ad;
	int *bd;
	const int csize = N*sizeof(char);
	const int isize = N*sizeof(int);
 
	printf("%s", a);
 
	cudaMalloc( (void**)&ad, csize ); 
	cudaMalloc( (void**)&bd, isize ); 
	cudaMemcpy( ad, a, csize, cudaMemcpyHostToDevice ); 
	cudaMemcpy( bd, b, isize, cudaMemcpyHostToDevice ); 
	
	dim3 dimBlock( blocksize, 1 );
	dim3 dimGrid( 1, 1 );
	hello<<<dimGrid, dimBlock>>>(ad, bd);
	cudaMemcpy( a, ad, csize, cudaMemcpyDeviceToHost ); 
	cudaFree( ad );
	cudaFree( bd );
	
	printf("%s\n", a);
	return EXIT_SUCCESS;
}

Example code from Linguist:

#include <stdio.h>
#include <cuda_runtime.h>

/**
 * CUDA Kernel Device code
 *
 * Computes the vector addition of A and B into C. The 3 vectors have the same
 * number of elements numElements.
 */
__global__ void
vectorAdd(const float *A, const float *B, float *C, int numElements)
{
    int i = blockDim.x * blockIdx.x + threadIdx.x;

    if (i < numElements)
    {
        C[i] = A[i] + B[i];
    }
}

/**
 * Host main routine
 */
int
main(void)
{
    // Error code to check return values for CUDA calls
    cudaError_t err = cudaSuccess;

    // Launch the Vector Add CUDA Kernel
    int threadsPerBlock = 256;
    int blocksPerGrid =(numElements + threadsPerBlock - 1) / threadsPerBlock;
    vectorAdd<<<blocksPerGrid, threadsPerBlock>>>(d_A, d_B, d_C, numElements);
    err = cudaGetLastError();

    if (err != cudaSuccess)
    {
        fprintf(stderr, "Failed to launch vectorAdd kernel (error code %s)!\n", cudaGetErrorString(err));
        exit(EXIT_FAILURE);
    }

    // Reset the device and exit
    err = cudaDeviceReset();

    return 0;
}

Example code from Wikipedia:

import numpy
from pycublas import CUBLASMatrix
A = CUBLASMatrix( numpy.mat([[1,2,3]],[[4,5,6]],numpy.float32) )
B = CUBLASMatrix( numpy.mat([[2,3]],[4,5],[[6,7]],numpy.float32) )
C = A*B
print C.np_mat()

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Last updated December 4th, 2019

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