How to Run CUDA C or C++ on Jupyter (Google Colab)

 %% 
 cu 
 #include 
 <stdio.h> 
  
 #include 
 <stdlib.h> 
  
 __global__ 
 void 
 add 
 ( 
 int 
 * 
 a 
 , 
 int 
 * 
 b 
 , 
 int 
 * 
 c 
 ) 
 { 
 * 
 c 
 = 
 * 
 a 
 + 
 * 
 b 
 ; 
 } 
 int 
 main 
 () 
 { 
 int 
 a 
 , 
 b 
 , 
 c 
 ; 
 // host copies of variables a, b & c 
 int 
 * 
 d_a 
 , 
 * 
 d_b 
 , 
 * 
 d_c 
 ; 
 // device copies of variables a, b & c 
 int 
 size 
 = 
 sizeof 
 ( 
 int 
 ); 
 // Allocate space for device copies of a, b, c 
 cudaMalloc 
 (( 
 void 
 ** 
 ) 
 & 
 d_a 
 , 
 size 
 ); 
 cudaMalloc 
 (( 
 void 
 ** 
 ) 
 & 
 d_b 
 , 
 size 
 ); 
 cudaMalloc 
 (( 
 void 
 ** 
 ) 
 & 
 d_c 
 , 
 size 
 ); 
 // Setup input values  
 c 
 = 
 0 
 ; 
 a 
 = 
 3 
 ; 
 b 
 = 
 5 
 ; 
 // Copy inputs to device 
 cudaMemcpy 
 ( 
 d_a 
 , 
 & 
 a 
 , 
 size 
 , 
 cudaMemcpyHostToDevice 
 ); 
 cudaMemcpy 
 ( 
 d_b 
 , 
 & 
 b 
 , 
 size 
 , 
 cudaMemcpyHostToDevice 
 ); 
 // Launch add() kernel on GPU 
 add 
 <<< 
 1 
 , 
 1 
 >>> 
 ( 
 d_a 
 , 
 d_b 
 , 
 d_c 
 ); 
 // Copy result back to host 
 cudaError 
 err 
 = 
 cudaMemcpy 
 ( 
 & 
 c 
 , 
 d_c 
 , 
 size 
 , 
 cudaMemcpyDeviceToHost 
 ); 
 if 
 ( 
 err 
 != 
 cudaSuccess 
 ) 
 { 
 printf 
 ( 
 "CUDA error copying to Host: %s 
 \n 
 " 
 , 
 cudaGetErrorString 
 ( 
 err 
 )); 
 } 
 printf 
 ( 
 "result is %d 
 \n 
 " 
 , 
 c 
 ); 
 // Cleanup 
 cudaFree 
 ( 
 d_a 
 ); 
 cudaFree 
 ( 
 d_b 
 ); 
 cudaFree 
 ( 
 d_c 
 ); 
 return 
 0 
 ; 
 }