Using CPU RAM and Computing with the GPU

Accelerated Computing CUDA CUDA Programming and Performance
1

Hello

I comput primes numbers with cuda :

__global__  void PrimeNumberV2( bool *tab, unsigned long long Nstart,  unsigned long long Ndelta)
{
	
	unsigned long long index = blockIdx.x * blockDim.x + threadIdx.x;
	while (index <= Ndelta)
	{	
		unsigned long long N = (index + Nstart)*2+1;
		unsigned long long c = 0;
		for (unsigned long long i = 1; i < (index + Nstart); i++)
		{
			c = i * 2 + 1;
			if (N%c == 0)
			{
				tab[index] = false;
				break;
			}
		}
		//index = Ndelta + 1;
		index +=blockDim.x * gridDim.x;
	}
}

I use Unified Memory :

#include "cuda_runtime.h"
#include "device_launch_parameters.h"

#include <stdio.h>
#include <iostream>
#include <chrono>	//Keep track of time
#include <fstream>	
using namespace std::chrono;
std::ofstream fileOut_Benchmark;
__device__ __managed__  bool  *Tab;

Code CPU host :

unsigned long long Udiff = (end - start)/2;
		//bool *Tab = NULL;
		cudaMallocManaged(&Tab, Udiff * sizeof(bool));
		cudaDeviceSynchronize();
		initTab(Tab, Udiff);
		cudaDeviceSynchronize();
		int NbThreadPerBlock = 1024;
		int NbBlockPerGrid = 512;
		PrimeNumberV2 << <NbBlockPerGrid, NbThreadPerBlock >> > (Tab, startN, Udiff);
		cudaDeviceSynchronize();
		count = countTab(Tab, Udiff, startN, mode);
		cudaFree(Tab);

Problem, a size of Tab is limited at 600 000 elements and I want have Tab with 20 000 000 000 elements.

I propose to use a RAM of my PC (32 Go) for to stock Tab, but how do I do that?

2

Pascal and Volta GPUs on CUDA 9 and Linux support oversubscription of GPU physical memory when using managed memory.

You appear to be running on windows. This feature (oversubscription) is not available on Windows on CUDA 9.

3

You can get a factor 8 increase in memory efficiency by switching to a bitwise sieve. But you would need atomicOr operations in global memory to reliably set (or clear) bits in the sieve. for 20 000 000 000 elements you need 2.5 GB of GPU memory when using a bitwise sieve. Most mid range GPUs should support this.

If you are still having trouble with memory, you can segment your sieve. Each run on the GPU covers a specific range of numbers, then you copy the results segment by segment into your host (CPU) memory.

You appear to be doing trial division against all odd numbers. There might be more efficient approaches ;)