Out-of-Memory Issue caused array access in loop

I’m working on a simple prime sieve right now. The gist of it is for n<=2^32, it sieves all primes upto n using a segboolsieve() (defined in smallsieve.h), and for n>2^32, segboolsieve() returns an array P containing all primes less than sqrt(n).

In the latter case, the host rounds n up to the nearest multiple of 2^32 (for convenience sake), and splits the digits between 2^32 and n into segments of 2^32 digits a piece.

The kernel works on one segment at a time, dividing it into threads sub-segments, and each thread sieves their respective sub-segment. I initially allocated each segment in global memory before sieving it, but that caused kernel launch failures for a reason I have yet to understand. Instead, I just had each thread allocate memory for their respective sub-segment and work on it instead. Each thread is working with a 256kB sub-segment given threads = 1024.

I’m only sieving odd numbers, and for the sake of space-efficiency I’ve mapped each number to a bit in an array of 64-bit integers.

CORE.cu

#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include "smallsieve.h"
#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include <math.h>
#include <time.h>

typedef unsigned __int64 uint64;
typedef unsigned __int32 uint32;
const uint64 pow2_32 = 4294967296;
const uint32 threads = 1024;
const uint64 segsize = pow2_32 / threads;

///////////////////////////KERNELS START///////////////////////////
//Checks if x is prime; if bit corresponding to x is 0, then return true.
__device__ bool isPrime(uint64 *mark, uint64 x)
{
	return (mark[x / 128] & ((uint64)1 << ((x >> 1) & 63))) ? 0 : 1;
}

//Set the bit corresponding to x
__device__ void bitSet(uint64 *mark, uint64 x)
{
	mark[x / 128] |= ((uint64)1 << ((x >> 1) & 63));
}

__global__ void SieveBlock(uint32 *P, uint32 completed, uint32 plen)
{
	//Each thread sieves [(pow2_32 >> 1) / threads] elements of the current block
	uint64 mark[(segsize / 128) + 1];
	uint64 id, i, j, minb, min, max, prime, temp1, temp2;
	id = threadIdx.x;
	min = (completed*pow2_32) + (id*segsize) + 1;
	max = min + segsize - 2;

	for (i = 0;(P[i] * P[i] <= max) & (i<plen);i++)
	{
		prime = P[i];
		minb = ((min / prime) * prime) + P[i];
		if (~minb & 1)	minb += prime;
		for (j = minb;j <= max;j += (prime << 1))
		    bitSet(mark,j-(min-1));
	}
}
////////////////////////////KERNELS END////////////////////////////   

//Driver function
int main(uint32 argc, char* argv[])
{
	//Range: Data-type dependent
	uint64 n, m;
	printf("Enter n: ");
	scanf("%llu", &n);

	bool smallsieve = false;	//Use serial sieve for n<2^32
	if (n <= pow2_32)
	{
		smallsieve = true;
		printf("Rounded %llu to ", n);
		m = (uint64)sqrt(n);
		n = m * m;
		printf("%llu\n", n);
	}
	else if (n % pow2_32 > 0)	//If n>2^32 then round n to nearest multiple of 2^32
	{
		printf("Rounded %llu to ", n);
		n = ((n / pow2_32) + 1) * pow2_32;
		printf("%llu\n", n);
		m = (uint64)(sqrt(n));
	}

	uint32 plen = 0;
	uint32 *P = NULL;
	if (~n & 1) n--;
	if (~m & 1) m--;

	P = segboolsieve(n, m, plen, smallsieve);
	if (P == NULL)
	{
		printf("Memory Allocation Failure!\n");
		exit(0);
	}
	else
		printf("Last prime in utility sieve: %u @ index [%u]\n", P[plen - 1], plen - 1);

	if (smallsieve)
	{
		free(P);
		return 0;
	}

	uint32 chunkcount = (uint32)((n + 1) / pow2_32);		//No. of chunks
	uint32 completed = 1;
	printf("\n%u chunk(s) for [%llu->%llu]\n", chunkcount - 1, pow2_32 + 1, n);

	uint32 *dP;

	//Log execution time
	float GPU_TIME = 0.0;
	float temp_t;

	//CUDA Malloc
	cudaMalloc(&dP, (plen + 1) * (size));

	cudaEvent_t start, stop;
	cudaEventCreate(&start);
	cudaEventCreate(&stop);
	dim3 TPB(threads, 1, 1);

	cudaMemcpy(dP, P, plen * size, cudaMemcpyHostToDevice);

	while (completed < chunkcount)
	{
		cudaEventRecord(start);
		SieveBlock << <1, TPB >> > (dP, completed, plen);
		cudaEventRecord(stop);
		cudaEventSynchronize(stop);
		cudaEventElapsedTime(&temp_t, start, stop);

		GPU_TIME += temp_t;
		completed++;
	}

	free(P);
	cudaFree(dP);

	GPU_TIME /= 1000;
	printf("COMPUTE-PHASE GPU Time: %0.3f seconds\n", GPU_TIME);
	return 0;
}

smallsieve.h

#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <time.h>

typedef unsigned __int64 uint64;
typedef unsigned __int32 uint32;
const size_t size = sizeof(uint64);

//Utility function to calculate postive uint64-powers
uint64 power(uint64 val, uint64 exp)
{
	uint64 temp = val;
	for (uint64 i = 1; i < exp; i++) temp *= val;
	return temp;
}

//Utility function to approximate no. of primes between 1->n as n/ln(n)
uint32 trimSize(uint64 n)
{
	long double e = 2.7183;
	uint64 exp = 1;
	while (pow(e, exp) < n)
		exp++;
	return (uint32)(n / (exp - 2));
}

uint32 *segboolsieve(uint64 n, uint64 m, uint32 &plen, bool smallsieve)
{
	uint32 blocksize = (uint32)m >> 1;
	if (smallsieve)
		plen = trimSize(n);
	else
		plen = trimSize(m + 2);

	//Boolean array initialized to false
	bool *mark = (bool *)calloc(blocksize + 1, sizeof(bool));	//Represents [2,3,5,7,9,11,...,sqrt(n)]

	//Array to store primes b/w [2,m]
	uint32 *P = (uint32 *)calloc(plen, sizeof(uint32) + 1);
	if (mark == NULL || P == NULL) { printf("Memory Allocation Failed!\n"); exit(1); }
	uint32 i, j, k, offset;

	//Log execution time
	clock_t START_TIME, END_TIME;
	double  CPU_TIME1 = 0.0, CPU_TIME2 = 0.0;

	//Setup-Phase: Calculate all primes in the range [3,m]
	START_TIME = clock();
	for (i = 5, k = 1, offset = 2; i <= m; i += offset, offset = 6 - offset)	//i->[3,5,7,9...,sqrt(n)] | i corresponds to mark[(i-3)/2]
	{
		if (!mark[i >> 1])
		{
			if (i*i <= n)
			{
				for (j = i * i; j <= m; j += (i << 1))	//j->[i^2,n] | increments by 2*i
					mark[j >> 1] = true;
			}
			P[k++] = i;
		}
	}
	free(mark);
	P[0] = 3;
	plen = k;
	END_TIME = clock();
	CPU_TIME1 = ((double)(END_TIME - START_TIME)) / CLOCKS_PER_SEC;

	//If CUDA Sieve will be used
	if (!smallsieve)
	{
		printf("\nSETUP-PHASE CPU Time: %0.3f seconds\n", CPU_TIME1);
		return P;
	}

	//Calculate all primes in the range [m,n] in segmented blocks of size (m/2)
	//Doubled as a blocksize of X covers 2X digits
	uint32 min = (blocksize << 1) + 1;
	uint32 max = (blocksize << 2) - 1;
	//Blocks contain ranges [blocksize*k, blocksize*(k+1)]
	START_TIME = clock();
	while (min < n)
	{
		if (max > n) max = (uint32)n;
		mark = (bool *)calloc(blocksize + 1, sizeof(bool));
		for (i = 0;i < plen;i++)
		{
			//Find smallest odd-multiple of P[i] w/i range [min,max]
			uint32 minb = (uint32)(min / P[i]) * (P[i]) + P[i];
			if (~minb & 1) minb += P[i];
			//Mark odd-multiples of P[i] as composite
			for (j = minb; j <= max; j += 2 * P[i])
				mark[(j - min) >> 1] = true;
		}
		for (i = min; i <= max;i += 2)
		{
			if (!mark[(i - min) >> 1])
				P[k++] = i;
		}
		min += (blocksize << 1);
		max += (blocksize << 1);
		free(mark);
	}
	plen = k;
	END_TIME = clock();
	CPU_TIME2 += ((double)(END_TIME - START_TIME)) / CLOCKS_PER_SEC;

	printf("\nSETUP-PHASE CPU Time: %0.3f seconds\n", CPU_TIME1 + CPU_TIME2);
	return P;
}

Here’s the problem: to set the bit corresponding to a number, I need to perform a bitwise OR. For some reason, writing the result of this operation to the array generates the following report from CUDA-MEMCHECK:-

========= Program hit cudaErrorMemoryAllocation (error 2) due to "out of memory" on CUDA API call to cudaLaunchKernel.
=========     Saved host backtrace up to driver entry point at error
=========     Host Frame:C:\WINDOWS\system32\nvcuda.dll (cuD3D9UnmapVertexBuffer + 0x2e2c29) [0x2f0feb]
=========     Host Frame:C:\Users\games\source\repos\CUDABitsieve\x64\Release\CUDABitsieve.exe (cudaLaunchKernel + 0x1f9) [0x1839]
=========     Host Frame:C:\Users\games\source\repos\CUDABitsieve\x64\Release\CUDABitsieve.exe (SieveBlock + 0xda) [0xf3fa]
=========     Host Frame:C:\Users\games\source\repos\CUDABitsieve\x64\Release\CUDABitsieve.exe (main + 0x2bb) [0xfadb]
=========     Host Frame:C:\Users\games\source\repos\CUDABitsieve\x64\Release\CUDABitsieve.exe (__scrt_common_main_seh + 0x10c) [0x10184]
=========     Host Frame:C:\WINDOWS\System32\KERNEL32.DLL (BaseThreadInitThunk + 0x14) [0x181f4]
=========     Host Frame:C:\WINDOWS\SYSTEM32\ntdll.dll (RtlUserThreadStart + 0x21) [0x6a251]
=========
========= ERROR SUMMARY: 1 error

I reiterate, the mere act of writing the result to the array is causing an out-of-memory error. If I comment out the contents of the j-loop in the SieveBlock() kernel, the error disappears. Even substituting its contents with the following raises no errors:-

for (j = minb;j <= max;j += (prime << 1))
{
	temp1 = j - (min - 1);
	temp2 = mark[temp1 / 128] | 1; //Arbitrary operation involving an element of the array
}//SAFE

And assigning the result of some other operation to it also causes no errors:-

for (j = minb;j <= max;j += (prime << 1))
{
        temp = j - (min - 1);
	mark[temp / 128] = 5 * 10; //Result of arbitrary operation not involving array's elements written to array
}//SAFE

However writing the value to the array as shown below causes the same out-of-memory error to manifest:-

for (j = minb;j <= max;j += (prime << 1))
{
	temp1 = j - (min - 1);
	temp2 = mark[temp1 / 128] | 1; //Arbitrary operation involving an element of the array
	mark[temp1 / 128] = temp2; //Writing result back to array
}//ERROR

And simply printing an element of the array also causes the same error:-

for (j = minb;j <= max;j += (prime << 1))
{
	temp = j - (min - 1); //Note: j = 1,3,5,7,9,11,...so it's 100% within bounds
	printf("%llu\n", mark[1]); //WORKS
	printf("%llu\n", mark[163/128); //WORKS
	printf("%llu\n", mark[prime/128); //WORKS
	printf("%llu\n", mark[temp / 128]); //ERROR
}

As a relative newbie, I have no idea what exactly I’m not seeing that’s causing this problem.The logic is sound on paper, and my serial implementation works perfectly. So there must be something about CUDA that I don’t know that’s pertinent here.

Any suggestions and advice are very much appreciated. Cheers.

OS: Windows 10
IDE: VS2017 Community
CUDA: v10.0
CPU: i7 4790K
GPU: GTX 1070 Ti

Probably not “caused” but “triggered”, as far as the “OR” is concerned. I am not going to spend time to debug your code. I suspect that your local memory usage per thread is too high. Depending on what you do with the local memory (and whether this is a debug or release build) the compiler may be able to eliminate some or all of the code operating on local memory, and thus the local memory usage.

Recommendation: Don’t use local memory for tasks like this. Use global memory for your bitmap and let each thread operate on the appropriate portion of that bitmap.

  1. Commenting out a single line of code that writes to some sort of visible state and then drawing conclusions based on that is fraught with danger. If you are a “newbie” I’d just simply advise you not to do that. It is not a good code introspection technique. The reason is that when you remove “writing to an output”, the compiler will often then do dead-code removal. Dead-code removal can remove large chunks of your kernel code, so interpreting the behavior of whats left as some how meaningful or useful is often, not useful. A possible reason for the appearance/disappearance of the out-of-memory issue is because, when you stop writing to mark, not only are you removing the writes, but the compiler may omit the creation of mark altogether. This might matter (see item 3 below).

  2. I suspect your code has defects. Here is one example that is is not unique or specific to CUDA:

for (i = 0;(P[i] * P[i] <= max) & (i<plen);i++)

First of all, (i<plen) is comparing a uint64 type to a uint32 type. I wouldn’t suggest doing that. It may not be wrong, but it looks odd to me and opens doors I’d rather not have to think about as a programmer. Second, that single & in C and C++ is a bitwise and operation. Did you mean a boolean and, which is &&? Maybe that works somehow, but I don’t want to figure out the correctness of that.

  1. Let’s revisit the previous topic (from your last question). Specifically this:
uint64 mark[(segsize / 128) + 1];

That is asking for a local array (one for each thread) of size: (segsize/128+1)8 bytes. How much is that exactly? Well segsize is 4 billion/1024, so 4 million. 4 million/128 is about 32,000, but we have to multiply that by 8 bytes in each uint64 entry, so we end up at 4 million/16 = 250,000 bytes. Each thread needs that as a separate array. You have 1024 threads per threadblock, so that means you need 250 megabytes of memory per threadblock. How many threadblocks will you launch? Only one. Unfortunately, the CUDA runtime doesn’t calculate the memory requirements that way. Instead, it calculates the maximum thread-carrying capacity of your GPU. What GPU are you running on? Oh, you didn’t say. (So it’s good practice to identify things like that when asking for help.) Let’s pick an example. GTX 1080 has 28 SMs, each of which can handle 2048 threads. So the CUDA runtime wants to allocate enough space so that if you did have a “full complement” on your kernel launch, it could be handled. 28SMs2048 threads/SM*250,000bytes/thread = 14Gigabytes. GTX 1080 (for example) doesn’t have 14GB of memory. So that upper bound of 512kbyte/thread might not be a realistic limit, even though it is the actual hardware limit. There is also a runtime-imposed limit that is a function of the GPU you are running on, and it may effectively be lower. I suspect that is what you are running into (albeit there may be other unrelated defects as well, see item 2 for an example).

I’ve confirmed via -Xptxas=-v that each thread takes up no more than 262160 bytes (256kB) for their respective arrays, that should fall under the 512kB limit. The main reason I moved them to the thread rather than sticking to global memory as I did before was that the latter was causing kernel launch failures for some reason. I figured I’d move it to thread-side temporarily and run tests with smaller values of n until I could figure out what I was doing wrong there.

See Robert Crovella’s post for a detailed explanation of what I tersely (and therefore perhaps misleadingly) referred to as “local memory use per thread is too high”.

It would have been probably the best idea to identify that reason. From decades of experience I can report that one can learn a lot from root-causing one’s bugs. And knowledge gained in this fashion tends to stick in one’s brain particularly well. Research shows that memory retention is higher when associated with unpleasant events (that can be as simple as sticking one’s arm into a bucket of ice water).

Especially since you are only launching one threadblock, the idea of locating your array(s) in the logical global space, rather than the logical local space, makes a lot of sense. This would necessitate debugging whatever the kernel launch failures were, of course.

Of course, from a performance perspective, launching kernels with a configuration of <<<1,N>>> or <<<N,1>>> or <<<1,1>>> doesn’t make sense, and generally should be avoided, where performance is a concern.

I apologise for the lacking information on my part. My GPU is a GTX 1070Ti. As for your points,

I’ll keep that in mind. I’ve only recently become familiar with Debug and Release builds and the background optimization that compilers do; hopefully it doesn’t take too long to reform my debug-by-comment etiquette.

You are absolutely right, that’s a horrible oversight on my part. As for the bitwise AND, the kernel executed about 0.1s faster using it compared to using a boolean AND. I’d assume it’s working as intended, but without a way to even generate output there’s no way for me to confirm that. I’ll revert to && for now.

The 1070Ti has 19 SMs. Going by your math, that’s about 9GB. My GPU has 8GB. What does this mean for me? And considering the number of threads I’m launching and the size of mark in each, that’s about 256MB in total. The maximum possible size that P can take up (assuming n = 2^64) would be 1GB. That should fall under the card’s VRAM limit right? So then is the “theoretical most-demanding-case” of 9GB what’s causing the issue? Or the runtim-imposed limit as you mentioned?

Regardless, I’ll try reducing the number of threads and using more blocks (256 threads and 8 blocks is a start), with segsize and id adjusted accordingly, and update ASAP with the results.

UPDATE: It’s doing…something. It isn’t throwing errors anymore though. I’ve added a second loop to find and display the last prime in a thread’s segment using isPrime(), which seems to just be printing the last number in the segment period, but it’s a start.

Since 9 GB needed is greater than 8 GB provided, that means your kernel will not launch due to insufficient resources. Which is exactly what you are observing.

As Robert Crovella pointed out, the way these local memory allocations work is that the per-thread usage is multiplied with the product of (number of SMs) * (max number of threads per SM). In other words, local memory is allocated based on the total number of threads that could theoretically run, not how many you actually run.

And this would remain an issue even if I reduced the number of threads to 256 and used more blocks? I’ll roll-back to my global-memory version to see if the issue persists.

“number of SMs” and “max number of threads per SM” are properties of the GPU hardware. They don’t change based on your code. The one factor you can change is how much local memory you request per thread:

(local memory storage requirements) = (local memory your code requests per thread) * (number of SMs present in your GPU hardware) * (maximum number of threads allowed per SM by GPU hardware)

I’ve modified the code so it uses global memory instead:-

#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include "smallsieve.h"
#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include <math.h>
#include <time.h>

typedef unsigned __int64 uint64;
typedef unsigned __int32 uint32;
const uint64 pow2_32 = 4294967296;
const uint32 threads = 256;
const uint32 blocks = 8;
const uint64 segsize = pow2_32 / (threads*blocks);

///////////////////////////KERNELS START///////////////////////////
//Checks if x is prime; if bit corresponding to x is 0, then return true.
__device__ bool isPrime(uint64 *mark, uint64 x)
{
	return (mark[x / 128] & ((uint64)1 << ((x >> 1) & 63))) ? 0 : 1;
}

//Set the bit corresponding to x
__device__ void bitSet(uint64 *mark, uint64 x)
{
	mark[x / 128] |= ((uint64)1 << ((x >> 1) & 63));
}

__global__ void SieveBlock(uint32 *P, uint64 *mark, uint32 completed, uint32 plen)
{
	//Each thread sieves [(pow2_32 >> 1) / (threads*blocks)] elements of the current block
	uint64 global_min = completed * pow2_32 + 1;

	uint64 id, i, j, minb, min, max, prime, x, temp;

	id = blockIdx.x*blockDim.x + threadIdx.x;
	min = global_min + (id*segsize);
	max = min + segsize - 2;

	//printf("Kernel %llu handles ranges %llu->%llu\n", id, min - global_min + 1, max - global_min + 1);//works correctly

	for (i = 0;((uint64)P[i] * (uint64)P[i] <= max) && (i < plen);i++)
	{
		prime = P[i];
		minb = ((min / prime) * prime);
		if (minb < min) minb += prime;
		if (~minb & 1)	minb += prime;
		for (j = minb;j <= max;j += (prime << 1))
		{
			x = j - global_min + 1;
			temp = mark[x / 128];
			temp |= ((uint64)1 << ((x >> 1) & 63));
			mark[x / 128] = temp;
		}
		//bitSet(mark, j - global_min + 1);
	}

	//Print last found prime
	for (j = max; j >= min; j -= 2)
	{
		if (isPrime(mark, j - min + 1))
		{
			printf("Kernel %llu: %llu|%llu\n", id, j, max);
			break;
		}
	}
}
////////////////////////////KERNELS END////////////////////////////

//     SEGMENTED SIEVE
//	n		RAM	    Time
// E07	   552KB   0.026s
// E08	   620KB   0.206s
// E09	   704KB   1.895s
// E10	   668KB   20.02s 
// E11     904KB   205.2s

//PARALLEL SEGMENTED SIEVE
//	n		RAM	    Time
// E10	      
// E11      

//Stats logged via Visual Studio Performance Profiler on i7 4790K @4.00GHz w/ 16GB DDR3 RAM and GTX 1070Ti

//Driver function
int main(uint32 argc, char* argv[])
{
	//Range: Data-type dependent
	uint64 n, m;
	printf("Enter n: ");
	scanf("%llu", &n);

	bool smallsieve = false;	//Use serial sieve for n<2^32
	if (n <= pow2_32)
	{
		smallsieve = true;
		printf("Rounded %llu to ", n);
		m = (uint64)sqrt(n);
		n = m * m;
		printf("%llu\n", n);
	}
	else if (n % pow2_32 > 0)	//If n>2^32 then round n to nearest multiple of 2^32
	{
		printf("Rounded %llu to ", n);
		n = ((n / pow2_32) + 1) * pow2_32;
		printf("%llu\n", n);
		m = (uint64)(sqrt(n));
	}

	uint32 plen = 0;
	uint32 *P = NULL;
	if (~n & 1) n--;
	if (~m & 1) m--;

	P = segboolsieve(n, m, plen, smallsieve);
	if (P == NULL)
	{
		printf("Memory Allocation Failure!\n");
		exit(0);
	}
	else
		printf("Last prime in utility sieve: %u @ index [%u]\n", P[plen - 1], plen - 1);

	if (smallsieve)
	{
		free(P);
		return 0;
	}

	uint32 chunkcount = (uint32)((n + 1) / pow2_32);		//No. of chunks
	uint32 completed = 1;
	printf("\n%u chunk(s) for [%llu->%llu]\n", chunkcount - 1, pow2_32 + 1, n);

	uint32 *dP;
	uint64 *dmark;
	//uint64 mark[(pow2_32 >> 7) + 1];
	uint64 *mark = (uint64 *)calloc((pow2_32 >> 7), sizeof(uint64));

	//Log execution time
	float GPU_TIME = 0.0;
	float temp_t;

	//CUDA Malloc
	cudaMalloc(&dP, (plen + 1) * (size));
	cudaMalloc(&dmark, ((pow2_32 >> 7)) * (size));

	cudaEvent_t start, stop;
	cudaEventCreate(&start);
	cudaEventCreate(&stop);
	dim3 TPB(threads, 1, 1);
	dim3 BPG(blocks, 1, 1);

	cudaMemcpy(dP, P, plen * size, cudaMemcpyHostToDevice);

	while (completed < chunkcount)
	{
		cudaMemcpy(dmark, mark, (pow2_32 >> 7) * size, cudaMemcpyHostToDevice);
		cudaEventRecord(start);
		SieveBlock << <BPG, TPB >> > (dP, dmark, completed, plen);
		cudaEventRecord(stop);
		cudaEventSynchronize(stop);
		cudaEventElapsedTime(&temp_t, start, stop);

		GPU_TIME += temp_t;
		completed++;
	}

	free(P);
	free(mark);
	cudaFree(dP);
	cudaFree(dmark);

	GPU_TIME /= 1000;
	printf("COMPUTE-PHASE GPU Time: %0.3f seconds\n", GPU_TIME);
	return 0;
}

And the errors it used to throw prior to switching to local memory still exist:-

========= Program hit cudaErrorLaunchFailure (error 4) due to "unspecified launch failure" on CUDA API call to cudaEventSynchronize.
=========     Saved host backtrace up to driver entry point at error
=========     Host Frame:C:\WINDOWS\system32\nvcuda.dll (cuD3D9UnmapVertexBuffer + 0x2e2c29) [0x2f0feb]
=========     Host Frame:C:\Users\games\source\repos\CUDABitsieve\x64\Release\CUDABitsieve.exe (cudaEventSynchronize + 0x103) [0x14f3]
=========     Host Frame:C:\Users\games\source\repos\CUDABitsieve\x64\Release\CUDABitsieve.exe (main + 0x32a) [0xfb5a]
=========     Host Frame:C:\Users\games\source\repos\CUDABitsieve\x64\Release\CUDABitsieve.exe (__scrt_common_main_seh + 0x10c) [0x10204]
=========     Host Frame:C:\WINDOWS\System32\KERNEL32.DLL (BaseThreadInitThunk + 0x14) [0x181f4]
=========     Host Frame:C:\WINDOWS\SYSTEM32\ntdll.dll (RtlUserThreadStart + 0x21) [0x6a251]
=========
========= Program hit cudaErrorLaunchFailure (error 4) due to "unspecified launch failure" on CUDA API call to cudaEventElapsedTime.
=========     Saved host backtrace up to driver entry point at error
=========     Host Frame:C:\WINDOWS\system32\nvcuda.dll (cuD3D9UnmapVertexBuffer + 0x2e2c29) [0x2f0feb]
=========     Host Frame:C:\Users\games\source\repos\CUDABitsieve\x64\Release\CUDABitsieve.exe (cudaEventElapsedTime + 0x125) [0x1255]
=========     Host Frame:C:\Users\games\source\repos\CUDABitsieve\x64\Release\CUDABitsieve.exe (main + 0x33b) [0xfb6b]
=========     Host Frame:C:\Users\games\source\repos\CUDABitsieve\x64\Release\CUDABitsieve.exe (__scrt_common_main_seh + 0x10c) [0x10204]
=========     Host Frame:C:\WINDOWS\System32\KERNEL32.DLL (BaseThreadInitThunk + 0x14) [0x181f4]
=========     Host Frame:C:\WINDOWS\SYSTEM32\ntdll.dll (RtlUserThreadStart + 0x21) [0x6a251]
=========
========= Program hit cudaErrorLaunchFailure (error 4) due to "unspecified launch failure" on CUDA API call to cudaFree.
=========     Saved host backtrace up to driver entry point at error
=========     Host Frame:C:\WINDOWS\system32\nvcuda.dll (cuD3D9UnmapVertexBuffer + 0x2e2c29) [0x2f0feb]
=========     Host Frame:C:\Users\games\source\repos\CUDABitsieve\x64\Release\CUDABitsieve.exe (cudaFree + 0xfc) [0x161c]
=========     Host Frame:C:\Users\games\source\repos\CUDABitsieve\x64\Release\CUDABitsieve.exe (main + 0x374) [0xfba4]
=========     Host Frame:C:\Users\games\source\repos\CUDABitsieve\x64\Release\CUDABitsieve.exe (__scrt_common_main_seh + 0x10c) [0x10204]
=========     Host Frame:C:\WINDOWS\System32\KERNEL32.DLL (BaseThreadInitThunk + 0x14) [0x181f4]
=========     Host Frame:C:\WINDOWS\SYSTEM32\ntdll.dll (RtlUserThreadStart + 0x21) [0x6a251]
=========
========= Program hit cudaErrorLaunchFailure (error 4) due to "unspecified launch failure" on CUDA API call to cudaFree.
=========     Saved host backtrace up to driver entry point at error
=========     Host Frame:C:\WINDOWS\system32\nvcuda.dll (cuD3D9UnmapVertexBuffer + 0x2e2c29) [0x2f0feb]
=========     Host Frame:C:\Users\games\source\repos\CUDABitsieve\x64\Release\CUDABitsieve.exe (cudaFree + 0xfc) [0x161c]
=========     Host Frame:C:\Users\games\source\repos\CUDABitsieve\x64\Release\CUDABitsieve.exe (main + 0x37d) [0xfbad]
=========     Host Frame:C:\Users\games\source\repos\CUDABitsieve\x64\Release\CUDABitsieve.exe (__scrt_common_main_seh + 0x10c) [0x10204]
=========     Host Frame:C:\WINDOWS\System32\KERNEL32.DLL (BaseThreadInitThunk + 0x14) [0x181f4]
=========     Host Frame:C:\WINDOWS\SYSTEM32\ntdll.dll (RtlUserThreadStart + 0x21) [0x6a251]
=========
========= Program hit CUDA_ERROR_LAUNCH_FAILED (error 719) due to "unspecified launch failure" on CUDA API call to cuModuleUnload.
=========     Saved host backtrace up to driver entry point at error
=========     Host Frame:C:\WINDOWS\system32\nvcuda.dll (cuD3D9UnmapVertexBuffer + 0x1c11ec) [0x1cf5ae]
=========     Host Frame:C:\Users\games\source\repos\CUDABitsieve\x64\Release\CUDABitsieve.exe (cudart::module::unload + 0x115) [0x9e25]
=========     Host Frame:C:\Users\games\source\repos\CUDABitsieve\x64\Release\CUDABitsieve.exe (cudart::contextState::unloadAllModules + 0x1c6) [0xa456]
=========     Host Frame:C:\Users\games\source\repos\CUDABitsieve\x64\Release\CUDABitsieve.exe (cudart::contextStateManager::destroyAllContextStatesOnRuntimeUnload + 0x78) [0xb5f8]
=========     Host Frame:C:\Users\games\source\repos\CUDABitsieve\x64\Release\CUDABitsieve.exe (cudart::globalState::~globalState + 0x3f) [0x28df]
=========     Host Frame:C:\Users\games\source\repos\CUDABitsieve\x64\Release\CUDABitsieve.exe (cudart::set<cudart::globalModule * __ptr64>::rehash + 0x106) [0x7876]
=========     Host Frame:C:\WINDOWS\System32\ucrtbase.dll (execute_onexit_table + 0x156) [0x13e26]
=========     Host Frame:C:\WINDOWS\System32\ucrtbase.dll (execute_onexit_table + 0x7b) [0x13d4b]
=========     Host Frame:C:\WINDOWS\System32\ucrtbase.dll (execute_onexit_table + 0x34) [0x13d04]
=========     Host Frame:C:\WINDOWS\System32\ucrtbase.dll (exit + 0x13e) [0x2000e]
=========     Host Frame:C:\WINDOWS\System32\ucrtbase.dll (exit + 0xcb) [0x1ff9b]
=========     Host Frame:C:\WINDOWS\System32\ucrtbase.dll (exit + 0x6e) [0x1ff3e]
=========     Host Frame:C:\Users\games\source\repos\CUDABitsieve\x64\Release\CUDABitsieve.exe (__scrt_common_main_seh + 0x173) [0x1026b]
=========     Host Frame:C:\WINDOWS\System32\KERNEL32.DLL (BaseThreadInitThunk + 0x14) [0x181f4]
=========     Host Frame:C:\WINDOWS\SYSTEM32\ntdll.dll (RtlUserThreadStart + 0x21) [0x6a251]
=========
========= ERROR SUMMARY: 5 errors

I really don’t know where to go from here. The most global memory that this program would possibly need to use is about 1.25GB. I don’t know what’s causing the kernel to fail execution.

PS: Don’t know of this info will help, but one thing to note is that when executing the program directly via Visual Studio, around 4/10 times the kernel does execute, and manages to sieve arbitrarily with some primes.

Sample o/p:-

Kernel 0: 4297064423|4297064447
Kernel 1: 4299161575|4299161599
Kernel 2: 4301258727|4301258751
Kernel 3: 4303355879|4303355903
Kernel 4: 4305453031|4305453055
Kernel 5: 4307550183|4307550207
Kernel 6: 4309647335|4309647359
Kernel 7: 4311744487|4311744511
Kernel 8: 4313841639|4313841663
Kernel 9: 4315938791|4315938815
Kernel 10: 4318035943|4318035967
Kernel 11: 4320133095|4320133119
Kernel 12: 4322230247|4322230271
Kernel 13: 4324327399|4324327423
Kernel 14: 4326424551|4326424575
Kernel 15: 4328521703|4328521727
Kernel 16: 4330618855|4330618879
Kernel 17: 4332716007|4332716031
Kernel 18: 4334813159|4334813183
Kernel 19: 4336910311|4336910335
Kernel 20: 4339007463|4339007487
Kernel 21: 4341104615|4341104639
Kernel 22: 4343201767|4343201791
Kernel 23: 4345298919|4345298943
Kernel 24: 4347396071|4347396095
Kernel 25: 4349493223|4349493247
Kernel 26: 4351590375|4351590399
Kernel 27: 4353687527|4353687551
Kernel 28: 4355784679|4355784703
Kernel 29: 4357881831|4357881855
Kernel 30: 4359978983|4359979007
Kernel 31: 4362076135|4362076159
Kernel 32: 4364173287|4364173311
Kernel 33: 4366270439|4366270463
Kernel 34: 4368367591|4368367615
Kernel 35: 4370464743|4370464767
Kernel 36: 4372561895|4372561919
Kernel 37: 4374659047|4374659071
Kernel 38: 4376756199|4376756223
Kernel 39: 4378853351|4378853375
Kernel 40: 4380950503|4380950527
Kernel 41: 4383047655|4383047679
Kernel 42: 4385144807|4385144831
Kernel 43: 4387241959|4387241983
Kernel 44: 4389339111|4389339135
Kernel 45: 4391436263|4391436287
Kernel 46: 4393533415|4393533439
Kernel 47: 4395630567|4395630591
Kernel 48: 4397727719|4397727743
Kernel 49: 4399824871|4399824895
Kernel 50: 4401922023|4401922047
Kernel 51: 4404019175|4404019199
Kernel 52: 4406116327|4406116351
Kernel 53: 4408213479|4408213503
Kernel 54: 4410310631|4410310655
Kernel 55: 4412407783|4412407807
Kernel 56: 4414504935|4414504959
Kernel 57: 4416602087|4416602111
Kernel 58: 4418699239|4418699263
Kernel 59: 4420796391|4420796415
Kernel 60: 4422893543|4422893567
Kernel 61: 4424990695|4424990719
Kernel 62: 4427087847|4427087871
Kernel 63: 4429184999|4429185023
Kernel 64: 4431282151|4431282175
Kernel 65: 4433379303|4433379327
Kernel 66: 4435476455|4435476479
Kernel 67: 4437573607|4437573631
Kernel 68: 4439670759|4439670783
Kernel 69: 4441767911|4441767935
Kernel 70: 4443865063|4443865087
Kernel 71: 4445962215|4445962239
Kernel 72: 4448059367|4448059391
Kernel 73: 4450156519|4450156543
Kernel 74: 4452253671|4452253695
Kernel 75: 4454350823|4454350847
Kernel 76: 4456447975|4456447999
Kernel 77: 4458545127|4458545151
Kernel 78: 4460642279|4460642303
Kernel 79: 4462739431|4462739455
Kernel 80: 4464836583|4464836607
Kernel 81: 4466933735|4466933759
Kernel 82: 4469030887|4469030911
Kernel 83: 4471128039|4471128063
Kernel 84: 4473225191|4473225215
Kernel 85: 4475322343|4475322367
Kernel 86: 4477419495|4477419519
Kernel 87: 4479516647|4479516671
Kernel 88: 4481613799|4481613823
Kernel 89: 4483710951|4483710975
Kernel 90: 4485808103|4485808127
Kernel 91: 4487905255|4487905279
Kernel 92: 4490002407|4490002431
Kernel 93: 4492099559|4492099583
Kernel 94: 4494196711|4494196735
Kernel 95: 4496293863|4496293887
Kernel 96: 4498391015|4498391039
Kernel 97: 4500488167|4500488191
Kernel 98: 4502585319|4502585343
Kernel 99: 4504682471|4504682495
Kernel 100: 4506779623|4506779647
Kernel 101: 4508876775|4508876799
Kernel 102: 4510973927|4510973951
Kernel 103: 4513071079|4513071103
Kernel 104: 4515168231|4515168255
Kernel 105: 4517265383|4517265407
Kernel 106: 4519362535|4519362559
Kernel 107: 4521459687|4521459711
Kernel 108: 4523556839|4523556863
Kernel 109: 4525653991|4525654015
Kernel 110: 4527751143|4527751167
Kernel 111: 4529848295|4529848319
Kernel 112: 4531945447|4531945471
Kernel 113: 4534042599|4534042623
Kernel 114: 4536139751|4536139775
Kernel 115: 4538236903|4538236927
Kernel 116: 4540334055|4540334079
Kernel 117: 4542431207|4542431231
Kernel 118: 4544528359|4544528383
Kernel 119: 4546625511|4546625535
Kernel 120: 4548722663|4548722687
Kernel 121: 4550819815|4550819839
Kernel 122: 4552916967|4552916991
Kernel 123: 4555014119|4555014143
Kernel 124: 4557111271|4557111295
Kernel 125: 4559208423|4559208447
Kernel 126: 4561305575|4561305599
Kernel 127: 4563402727|4563402751
Kernel 128: 4565499879|4565499903
Kernel 129: 4567597031|4567597055
Kernel 130: 4569694183|4569694207
Kernel 131: 4571791335|4571791359
Kernel 132: 4573888487|4573888511
Kernel 133: 4575985639|4575985663
Kernel 134: 4578082791|4578082815
Kernel 135: 4580179943|4580179967
Kernel 136: 4582277095|4582277119
Kernel 137: 4584374247|4584374271
Kernel 138: 4586471399|4586471423
Kernel 139: 4588568551|4588568575
Kernel 140: 4590665703|4590665727
Kernel 141: 4592762855|4592762879
Kernel 142: 4594860007|4594860031
Kernel 143: 4596957159|4596957183
Kernel 144: 4599054311|4599054335
Kernel 145: 4601151463|4601151487
Kernel 146: 4603248615|4603248639
Kernel 147: 4605345767|4605345791
Kernel 148: 4607442919|4607442943
Kernel 149: 4609540071|4609540095
Kernel 150: 4611637223|4611637247
Kernel 151: 4613734375|4613734399
Kernel 152: 4615831527|4615831551
Kernel 153: 4617928679|4617928703
Kernel 154: 4620025831|4620025855
Kernel 155: 4622122983|4622123007
Kernel 156: 4624220135|4624220159
Kernel 157: 4626317287|4626317311
Kernel 158: 4628414439|4628414463
Kernel 159: 4630511591|4630511615
Kernel 160: 4632608743|4632608767
Kernel 161: 4634705895|4634705919
Kernel 162: 4636803047|4636803071
Kernel 163: 4638900199|4638900223
Kernel 164: 4640997351|4640997375
Kernel 165: 4643094503|4643094527
Kernel 166: 4645191655|4645191679
Kernel 167: 4647288807|4647288831
Kernel 168: 4649385959|4649385983
Kernel 169: 4651483111|4651483135
Kernel 170: 4653580263|4653580287
Kernel 171: 4655677415|4655677439
Kernel 172: 4657774567|4657774591
Kernel 173: 4659871719|4659871743
Kernel 174: 4661968871|4661968895
Kernel 175: 4664066023|4664066047
Kernel 176: 4666163175|4666163199
Kernel 177: 4668260327|4668260351
Kernel 178: 4670357479|4670357503
Kernel 179: 4672454631|4672454655
Kernel 180: 4674551783|4674551807
Kernel 181: 4676648935|4676648959
Kernel 182: 4678746087|4678746111
Kernel 183: 4680843239|4680843263
Kernel 184: 4682940391|4682940415
Kernel 185: 4685037543|4685037567
Kernel 186: 4687134695|4687134719
Kernel 187: 4689231847|4689231871
Kernel 188: 4691328999|4691329023
Kernel 189: 4693426151|4693426175
Kernel 190: 4695523303|4695523327
Kernel 191: 4697620455|4697620479
Kernel 192: 4699717607|4699717631
Kernel 193: 4701814759|4701814783
Kernel 194: 4703911911|4703911935
Kernel 195: 4706009063|4706009087
Kernel 196: 4708106215|4708106239
Kernel 197: 4710203367|4710203391
Kernel 198: 4712300519|4712300543
Kernel 199: 4714397671|4714397695
Kernel 200: 4716494823|4716494847
Kernel 201: 4718591975|4718591999
Kernel 202: 4720689127|4720689151
Kernel 203: 4722786279|4722786303
Kernel 204: 4724883431|4724883455
Kernel 205: 4726980583|4726980607
Kernel 206: 4729077735|4729077759
Kernel 207: 4731174887|4731174911
Kernel 208: 4733272039|4733272063
Kernel 209: 4735369191|4735369215
Kernel 210: 4737466343|4737466367
Kernel 211: 4739563495|4739563519
Kernel 212: 4741660647|4741660671
Kernel 213: 4743757799|4743757823
Kernel 214: 4745854951|4745854975
Kernel 215: 4747952103|4747952127
Kernel 216: 4750049255|4750049279
Kernel 217: 4752146407|4752146431
Kernel 218: 4754243559|4754243583
Kernel 219: 4756340711|4756340735
Kernel 220: 4758437863|4758437887
Kernel 221: 4760535015|4760535039
Kernel 222: 4762632167|4762632191
Kernel 223: 4764729319|4764729343
Kernel 224: 4766826471|4766826495
Kernel 225: 4768923623|4768923647
Kernel 226: 4771020775|4771020799
Kernel 227: 4773117927|4773117951
Kernel 228: 4775215079|4775215103
Kernel 229: 4777312231|4777312255
Kernel 230: 4779409383|4779409407
Kernel 231: 4781506535|4781506559
Kernel 232: 4783603687|4783603711
Kernel 233: 4785700839|4785700863
Kernel 234: 4787797991|4787798015
Kernel 235: 4789895143|4789895167
Kernel 236: 4791992295|4791992319
Kernel 237: 4794089447|4794089471
Kernel 238: 4796186599|4796186623
Kernel 239: 4798283751|4798283775
Kernel 240: 4800380903|4800380927
Kernel 241: 4802478055|4802478079
Kernel 242: 4804575207|4804575231
Kernel 243: 4806672359|4806672383
Kernel 244: 4808769511|4808769535
Kernel 245: 4810866663|4810866687
Kernel 246: 4812963815|4812963839
Kernel 247: 4815060967|4815060991
Kernel 248: 4817158119|4817158143
Kernel 249: 4819255271|4819255295
Kernel 250: 4821352423|4821352447
Kernel 251: 4823449575|4823449599
Kernel 252: 4825546727|4825546751
Kernel 253: 4827643879|4827643903
Kernel 254: 4829741031|4829741055
Kernel 255: 4831838183|4831838207
Kernel 256: 4833935335|4833935359
Kernel 257: 4836032487|4836032511
Kernel 258: 4838129639|4838129663
Kernel 259: 4840226791|4840226815
Kernel 260: 4842323943|4842323967
Kernel 261: 4844421095|4844421119
Kernel 262: 4846518247|4846518271
Kernel 263: 4848615399|4848615423
Kernel 264: 4850712551|4850712575
Kernel 265: 4852809703|4852809727
Kernel 266: 4854906855|4854906879
Kernel 267: 4857004007|4857004031
Kernel 268: 4859101159|4859101183
Kernel 269: 4861198311|4861198335
Kernel 270: 4863295463|4863295487
Kernel 271: 4865392615|4865392639
Kernel 272: 4867489767|4867489791
Kernel 273: 4869586919|4869586943
Kernel 274: 4871684071|4871684095
Kernel 275: 4873781223|4873781247
Kernel 276: 4875878375|4875878399
Kernel 277: 4877975527|4877975551
Kernel 278: 4880072679|4880072703
Kernel 279: 4882169831|4882169855
Kernel 280: 4884266983|4884267007
Kernel 281: 4886364135|4886364159
Kernel 282: 4888461287|4888461311
Kernel 283: 4890558439|4890558463
Kernel 284: 4892655591|4892655615
Kernel 285: 4894752743|4894752767
Kernel 286: 4896849895|4896849919
Kernel 287: 4898947047|4898947071
Kernel 288: 4901044199|4901044223
Kernel 289: 4903141351|4903141375
Kernel 290: 4905238503|4905238527
Kernel 291: 4907335655|4907335679
Kernel 292: 4909432807|4909432831
Kernel 293: 4911529959|4911529983
Kernel 294: 4913627111|4913627135
Kernel 295: 4915724263|4915724287
Kernel 296: 4917821415|4917821439
Kernel 297: 4919918567|4919918591
Kernel 298: 4922015719|4922015743
Kernel 299: 4924112871|4924112895
Kernel 300: 4926210023|4926210047
Kernel 301: 4928307175|4928307199
Kernel 302: 4930404327|4930404351
Kernel 303: 4932501479|4932501503
Kernel 304: 4934598631|4934598655
Kernel 305: 4936695783|4936695807
Kernel 306: 4938792935|4938792959
Kernel 307: 4940890087|4940890111
Kernel 308: 4942987239|4942987263
Kernel 309: 4945084391|4945084415
Kernel 310: 4947181543|4947181567
Kernel 311: 4949278695|4949278719
Kernel 312: 4951375847|4951375871
Kernel 313: 4953472999|4953473023
Kernel 314: 4955570151|4955570175
Kernel 315: 4957667303|4957667327
Kernel 316: 4959764455|4959764479
Kernel 317: 4961861607|4961861631
Kernel 318: 4963958759|4963958783
Kernel 319: 4966055911|4966055935
Kernel 384: 5102370791|5102370815
Kernel 385: 5104467943|5104467967
Kernel 386: 5106565095|5106565119
Kernel 387: 5108662247|5108662271
Kernel 388: 5110759399|5110759423
Kernel 389: 5112856551|5112856575
Kernel 390: 5114953703|5114953727
Kernel 391: 5117050855|5117050879
Kernel 392: 5119148007|5119148031
Kernel 393: 5121245159|5121245183
Kernel 394: 5123342311|5123342335
Kernel 395: 5125439463|5125439487
Kernel 396: 5127536615|5127536639
Kernel 397: 5129633767|5129633791
Kernel 398: 5131730919|5131730943
Kernel 399: 5133828071|5133828095
Kernel 400: 5135925223|5135925247
Kernel 401: 5138022375|5138022399
Kernel 402: 5140119527|5140119551
Kernel 403: 5142216679|5142216703
Kernel 404: 5144313831|5144313855
Kernel 405: 5146410983|5146411007
Kernel 406: 5148508135|5148508159
Kernel 407: 5150605287|5150605311
Kernel 408: 5152702439|5152702463
Kernel 409: 5154799591|5154799615
Kernel 410: 5156896743|5156896767
Kernel 411: 5158993895|5158993919
Kernel 412: 5161091047|5161091071
Kernel 413: 5163188199|5163188223
Kernel 414: 5165285351|5165285375
Kernel 415: 5167382503|5167382527
Kernel 512: 5370806247|5370806271
Kernel 513: 5372903399|5372903423
Kernel 514: 5375000551|5375000575
Kernel 515: 5377097703|5377097727
Kernel 516: 5379194855|5379194879
Kernel 517: 5381292007|5381292031
Kernel 518: 5383389159|5383389183
Kernel 519: 5385486311|5385486335
Kernel 520: 5387583463|5387583487
Kernel 521: 5389680615|5389680639
Kernel 522: 5391777767|5391777791
Kernel 523: 5393874919|5393874943
Kernel 524: 5395972071|5395972095
Kernel 525: 5398069223|5398069247
Kernel 526: 5400166375|5400166399
Kernel 527: 5402263527|5402263551
Kernel 528: 5404360679|5404360703
Kernel 529: 5406457831|5406457855
Kernel 530: 5408554983|5408555007
Kernel 531: 5410652135|5410652159
Kernel 532: 5412749287|5412749311
Kernel 533: 5414846439|5414846463
Kernel 534: 5416943591|5416943615
Kernel 535: 5419040743|5419040767
Kernel 536: 5421137895|5421137919
Kernel 537: 5423235047|5423235071
Kernel 538: 5425332199|5425332223
Kernel 539: 5427429351|5427429375
Kernel 540: 5429526503|5429526527
Kernel 541: 5431623655|5431623679
Kernel 542: 5433720807|5433720831
Kernel 543: 5435817959|5435817983
Kernel 576: 5505023975|5505023999
Kernel 577: 5507121127|5507121151
Kernel 578: 5509218279|5509218303
Kernel 579: 5511315431|5511315455
Kernel 580: 5513412583|5513412607
Kernel 581: 5515509735|5515509759
Kernel 582: 5517606887|5517606911
Kernel 583: 5519704039|5519704063
Kernel 584: 5521801191|5521801215
Kernel 585: 5523898343|5523898367
Kernel 586: 5525995495|5525995519
Kernel 587: 5528092647|5528092671
Kernel 588: 5530189799|5530189823
Kernel 589: 5532286951|5532286975
Kernel 590: 5534384103|5534384127
Kernel 591: 5536481255|5536481279
Kernel 592: 5538578407|5538578431
Kernel 593: 5540675559|5540675583
Kernel 594: 5542772711|5542772735
Kernel 595: 5544869863|5544869887
Kernel 596: 5546967015|5546967039
Kernel 597: 5549064167|5549064191
Kernel 598: 5551161319|5551161343
Kernel 599: 5553258471|5553258495
Kernel 600: 5555355623|5555355647
Kernel 601: 5557452775|5557452799
Kernel 602: 5559549927|5559549951
Kernel 603: 5561647079|5561647103
Kernel 604: 5563744231|5563744255
Kernel 605: 5565841383|5565841407
Kernel 606: 5567938535|5567938559
Kernel 607: 5570035687|5570035711
Kernel 416: 5169479655|5169479679
Kernel 417: 5171576807|5171576831
Kernel 418: 5173673959|5173673983
Kernel 419: 5175771111|5175771135
Kernel 420: 5177868263|5177868287
Kernel 421: 5179965415|5179965439
Kernel 422: 5182062567|5182062591
Kernel 423: 5184159719|5184159743
Kernel 424: 5186256871|5186256895
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It’s more a freakish accident than anything else. I’ve seen multiples of 3 show up as “primes”, while also seeing multiples of 3 that succeed it in the same segment be skipped/sieved. There doesn’t seem to be a rhyme or reason to it.

Sounds like you may be running into the GUI watchdog timer limit imposed by Windows on any GPU that services the Windows GUI. The default limit is about 2 seconds. If a CUDA kernel runs longer than that, it will be terminated by the operating system, which basically forces a reset of the GPU, causing the CUDA context to be destroyed.

Otherwise, “unspecified launch failure” is the equivalent of a “segmentation fault” on Linux or a “general protection fault” on Windows, i.e. indicative that your code has an out-of-bound memory access.

I would suggest you start small and avail yourself of the numerous resources on the internet for CUDA beginners. The model of diving straight in and then looking to these forums for one-on-one custom tutorials isn’t going to scale.