1. CUDA Programming continued ITCS 4145/5145 Nov 24, 2010 © Barry Wilkinson CUDA-3

2. 2 Error Reporting continued CUDA SDK toolkit has some “safety check routines: cutilSafeCall(... ); // check for error return codes: cutilCheckMsg(... ); // check for failure messages: Example cutilSafeCall( cudaMalloc(… ) ); // allocate GPU memory myKernel >>( … ) ; // execute kernel cutilCheckMsg("myKernel failed\n"); cutilSafeCall( cudaMemcpy(…);// copy results back cutilSafeCall(cudaFree( … ); // free memory Need details of these routines!

3. 3 Error Reporting continued Book by Sanders and Kandrot* uses a macro called HANDLE_ERROR() to surround CUDA calls, e.g.: HANDLE_ERROR( cudaMalloc( … )); HANDLE_ERROR detects that call has returned an error code, prints an associated error message, and exist the application with an EXIT_FAILURE code: static void HandleError( cudaError_t err, const char *file, int line ) { if (err != cudaSuccess) { printf( "%s in %s at line %d\n", cudaGetErrorString( err ), file, line ); exit( EXIT_FAILURE ); } #define HANDLE_ERROR( err ) (HandleError( err, __FILE__, __LINE__ )) * “CUDA By Example An Introduction to General-Purpose GPU Programming” by Jason Sanders and Edward Kandrot, Addison-Wesley, Upper Saddle River, NJ, 2011

4. 4 Timing Execution CUDA SDK timer int timer =0; cutCreateTimer (& timer); cutStartTimer (timer);... cutStopTimer (timer); cutGetTimerValue (timer); cutDeleteTimer (timer); Avoid including time of first kernel launch which will be more timing consuming that subsequent launches because of initialization Use events instead of above for asynchronous functions Need details of these routines!

5. 5 Timing If program uses synchronous cudaMemcpy, can use clock(): #include … start = clock(); cudaMemcpy …// kernel call cudaMemcpy stop = clock(); printf("GPU pi calculated in %f s.\n", (stop-start)/(float)CLOCKS_PER_SEC );

6. 6 Monte Carlo Computations Embarrassingly parallel computations that are attractive for GPUs. Use random numbers to make random selections that are then used in the computation. Many application areas: numerical integration, physical simulations, business models, finance, … Principle issue is how to generate (pseudo) random sequences. Cannot call rand() or any other C library function from within a CUDA kernel. * http://developer.download.nvidia.com/compute/cuda/3_2_prod/toolkit/docs/CURAND_Library.pdf

7. 7 Generating random numbers Possible solutions: 1.Call rand() in the CPU code and copy the random numbers across to the GPU (not the best way) 2.Use the NVIDIA CUDA CURAND library* 3.Hand-code the rand() function in kernel. Common random number generator formula is: x i+1 = (a * x i + c) mod m. Good values for a, c, and m are a = 16807, c = 0, and m = 2 31 - 1 (a prime number). Will need to use long ints because of the size of numbers. * http://developer.download.nvidia.com/compute/cuda/3_2_prod/toolkit/docs/CURAND_Library.pdf

8. Questions