# [solved] Why can't I compute primes past 85 million? (sieve of eratosthenes)

Why can’t I compute primes past 85 million?

85 million bytes represents roughly 0.8 GB. I have a GTX 780 (3 GB), so shouldn’t my code be able to compute at least primes up to 2 billion or 3 billion?

With my current code, I’m hitting a wall and not being able to compute prime numbers past 85 million.
If I try to do so, my system freezes when I run the program, and a few seconds later, it gets unfrozen, with a crashed program. Error message:

``````CUDA error at prime.cu:129 code=6(cudaErrorLaunchTimeout) "cudaDeviceSynchronize()"
``````

This is my code, prime.cu:

``````#include <stdio.h>
#include <helper_cuda.h> // checkCudaErrors() - NVIDIA_CUDA-6.0_Samples/common/inc
// #include <cuda.h>
// #include <cuda_runtime_api.h>
// #include <cuda_runtime.h>

typedef unsigned long long int uint64_t;

/******************************************************************************
* kernel that initializes the 1st couple of values in the primes array.
******************************************************************************/
__global__ static void sieveInitCUDA(char* primes)
{
primes[0] = 1; // value of 1 means the number is NOT prime
primes[1] = 1; // numbers "0" and "1" are not prime numbers
}

/******************************************************************************
* kernel for sieving the even numbers starting at 4.
******************************************************************************/
__global__ static void sieveEvenNumbersCUDA(char* primes, uint64_t max)
{
uint64_t index = blockIdx.x * blockDim.x + threadIdx.x + threadIdx.x + 4;
if (index < max)
primes[index] = 1;
}

/******************************************************************************
* kernel for finding prime numbers using the sieve of eratosthenes
* - primes: an array of bools. initially all numbers are set to "0".
*           A "0" value means that the number at that index is prime.
* - max: the max size of the primes array
* - maxRoot: the sqrt of max (the other input). we don't wanna make all threads
*   compute this over and over again, so it's being passed in
******************************************************************************/
__global__ static void sieveOfEratosthenesCUDA(char *primes, uint64_t max,
const uint64_t maxRoot)
{
// get the starting index, sieve only odds starting at 3
// 3,5,7,9,11,13...
/* int index = blockIdx.x * blockDim.x + threadIdx.x + threadIdx.x + 3; */

// apparently the following indexing usage is faster than the one above. Hmm
int index = blockIdx.x * blockDim.x + threadIdx.x + 3;

// make sure index won't go out of bounds, also don't start the execution
// on numbers that are already composite
if (index < maxRoot && primes[index] == 0)
{
// mark off the composite numbers
for (int j = index * index; j < max; j += index)
{
primes[j] = 1;
}
}
}

/******************************************************************************
* checkDevice()
******************************************************************************/
__host__ int checkDevice()
{
// query the Device and decide on the block size
int devID = 0; // the default device ID
cudaError_t error;
error = cudaGetDevice(&devID);
if (error != cudaSuccess)
{
printf("CUDA Device not ready or not supported\n");
printf("%s: cudaGetDevice returned error code %d, line(%d)\n", __FILE__, error, __LINE__);
exit(EXIT_FAILURE);
}

error = cudaGetDeviceProperties(&deviceProp, devID);
if (deviceProp.computeMode == cudaComputeModeProhibited || error != cudaSuccess)
{
printf("CUDA device ComputeMode is prohibited or failed to getDeviceProperties\n");
return EXIT_FAILURE;
}

// Use a larger block size for Fermi and above (see compute capability)
return (deviceProp.major < 2) ? 16 : 32;
}

/******************************************************************************
* genPrimesOnDevice
* - inputs: limit - the largest prime that should be computed
*           primes - an array of size [limit], initialized to 0
******************************************************************************/
__host__ void genPrimesOnDevice(char* primes, uint64_t max)
{
int blockSize = checkDevice();
if (blockSize == EXIT_FAILURE)
return;

char* d_Primes = NULL;
int sizePrimes = sizeof(char) * max;
uint64_t maxRoot = sqrt(max);

// allocate the primes on the device and set them to 0
checkCudaErrors(cudaMalloc(&d_Primes, sizePrimes));
checkCudaErrors(cudaMemset(d_Primes, 0, sizePrimes));

// make sure that there are no errors...
checkCudaErrors(cudaPeekAtLastError());

// setup the execution configuration
dim3 dimBlock(blockSize);
dim3 dimGrid((maxRoot + dimBlock.x) / dimBlock.x);
dim3 dimGridEvens(((max + dimBlock.x) / dimBlock.x) / 2);

//////// debug
#ifdef DEBUG
printf("dimBlock(%d, %d, %d)\n", dimBlock.x, dimBlock.y, dimBlock.z);
printf("dimGrid(%d, %d, %d)\n", dimGrid.x, dimGrid.y, dimGrid.z);
printf("dimGridEvens(%d, %d, %d)\n", dimGridEvens.x, dimGridEvens.y, dimGridEvens.z);
#endif

// call the kernel
// NOTE: no need to synchronize after each kernel
// http://stackoverflow.com/a/11889641/2261947
sieveInitCUDA<<<1, 1>>>(d_Primes); // launch a single thread to initialize
sieveEvenNumbersCUDA<<<dimGridEvens, dimBlock>>>(d_Primes, max);
sieveOfEratosthenesCUDA<<<dimGrid, dimBlock>>>(d_Primes, max, maxRoot);

// check for kernel errors
checkCudaErrors(cudaPeekAtLastError());

// copy the results back
checkCudaErrors(cudaMemcpy(primes, d_Primes, sizePrimes, cudaMemcpyDeviceToHost));

// no memory leaks
checkCudaErrors(cudaFree(d_Primes));
}
``````

If you’d like to test the code:

``````int main()
{
int max = 85000000; // 85 million
char* primes = malloc(max);
// check that it allocated correctly...
memset(primes, 0, max);

genPrimesOnDevice(primes, max);

// if you wish to display results:
for (uint64_t i = 0; i < size; i++)
{
if (primes[i] == 0) // if the value is '0', then the number is prime
{
printf("%llu", i)
if ((i + 1) != size)
printf(", ");
}
}

free(primes);

}
``````

Ok, nevermind, I don’t know how to delete this thread, but someone has answered me on StackOverflow.
The problem is that I’m using X Window (linux) and I’m using the GPU for display as well.

http://stackoverflow.com/a/24794547/2261947

He recommended me to checkout this document - http://nvidia.custhelp.com/app/answers/detail/a_id/3029/~/using-cuda-and-x

The easiest way to do this is a hybrid cpu/gpu implementation like this really really simple version:

http://pastebin.com/UNMstwEC

modestly faster than an optimized -O3 CPU version (4.0 Ghz)