CUDA Programming continued

ITCS 4145/5145 Nov 24, 2010 © Barry Wilkinson

CUDA-3

2

Error Reportingcontinued

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<<<nblocks,nthreads>>>( … ) ; // execute kernel

cutilCheckMsg("myKernel failed\n");

cutilSafeCall( cudaMemcpy(…); // copy results back

cutilSafeCall(cudaFree( … ); // free memory

Need details of these routines!

3

Error Reportingcontinued

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

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 initializationUse events instead of above for asynchronous functions

Need details of these routines!

5

Timing

If program uses synchronous cudaMemcpy, can use clock():

#include <time.h>…start = clock();cudaMemcpy

… // kernel callcudaMemcpystop = clock();printf("GPU pi calculated in %f s.\n",

(stop-start)/(float)CLOCKS_PER_SEC);

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

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:

xi+1 = (a * xi + c) mod m. Good values for a, c, and m are a = 16807, c = 0, and m = 231 -

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

Questions