cudaErrorLaunchOutOfResources(701) when launching __global__ function

I’m using cuda 10.2 for a project(sm_61, compute_61). Here’s a part of my code.

#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <cstdio>
#include <cstdlib>

constexpr unsigned int CUDA_MAX_THREAD_NUM = 256;
#define Block_Count(tot_thrd) (((tot_thrd) + CUDA_MAX_THREAD_NUM - 1u) / CUDA_MAX_THREAD_NUM)

static void HandleError(cudaError_t err, const char *file, int line) {
	if (err != cudaSuccess) {
		fprintf(stderr, "Error %d: \"%s\" in %s at line %d\n", int(err), cudaGetErrorString(err), file, line);
#define HANDLE_ERROR(err) (HandleError(err, __FILE__, __LINE__))

template <typename T>
__global__ void subConstructArray(T* dsts, size_t Len) {
	size_t Idi = blockIdx.x * CUDA_MAX_THREAD_NUM + threadIdx.x;
	if (Idi >= Len) return;

	new (dsts + Idi)T;

template <typename T>
__global__ void subDestructArray(T* dsts, size_t Len) {
	size_t Idi = blockIdx.x * CUDA_MAX_THREAD_NUM + threadIdx.x;
	if (Idi >= Len) return;

	(dsts + Idi)->~T();

template <typename T>
void ConstructArray(T*& dsts, size_t Len) {
	HANDLE_ERROR(cudaMalloc(&dsts, sizeof(T) * Len));
	subConstructArray<<<Block_Count(Len), CUDA_MAX_THREAD_NUM>>>
		(dsts, Len);

template <typename T>
void DestructArray(T* dsts, size_t Len) {
	subDestructArray<<<Block_Count(Len), CUDA_MAX_THREAD_NUM>>>
		(dsts, Len);
	HANDLE_ERROR(cudaGetLastError());  //cudaErrorLaunchOutOfResources Here

template<typename T>
class tensor {
		T* _elems;
		int _Ply;
		int _Height;
		int _Width;
		__device__ tensor() : _elems(nullptr), _Ply(0), _Height(0), _Width(0) {}
		__device__ ~tensor() { free(_elems); }

		__device__ void Set_size(int _ply, int _hgt, int _wid) {
			if (_ply * _hgt * _wid != _Ply * _Height * _Width) {
				_elems = (T*)malloc(sizeof(T) * _ply * _hgt * _wid);
			_Ply = _ply;
			_Height = _hgt;
			_Width = _wid;

size_t Tot_Thrd = 256;
tensor<double>* out;
tensor<double>* er;

void Set_Tot_Thrd(size_t _num_of_thrds) {
	DestructArray(out, Tot_Thrd);
	DestructArray(er, Tot_Thrd);
	ConstructArray(out, _num_of_thrds);
	ConstructArray(er, _num_of_thrds);
	Tot_Thrd = _num_of_thrds;

int main() {
	ConstructArray(out, Tot_Thrd);
	ConstructArray(er, Tot_Thrd);

But when I call function “Set_Tot_Thrd(8)”, HANDLE_ERROR(cudaGetLastError()) in host function DestructArray() turns out to be cudaErrorLaunchOutOfResources (701) - “too many resources requested for launch”. I used to think that the number of registers the function “global void subDestructArray” need is larger than the number of registers available per block. So I ran deviceQuery:

And here’s ptxas info:


256*17= 4352 < 65536. So the number of registers did not exceed the max limit.

Also I added -maxrregcount=16 but after compiling the same error “cudaErrorLaunchOutOfResources” occurred when running the program. Neither cuda-memcheck nor ptxas gave me helpful answer.

It seems like that cudaErrorLaunchOutOfResources means the number of not only registers but something else exceeded. But I have no idea what runs out when I call the function.

I’m desperate to solve this problem. What should I do? Many thanks in advance.

[Please post text as text, not as images. This avoids issues with technologies that assist the visually impaired]

It’s best to post a minimal self-contained reproducer code that others can compile and run. Quick sanity check: Are you building the code for the correct target architecture, i.e. sm_61?

Agreed that it would be more helpful if “cudaErrorLaunchOutOfResources” provided a sub-code that would point out which specific resource limit was exceeded. You may want to file an enhancement request with NVIDIA for this. You can do so by using the bug reporting form and prefixing the synopsis with “RFE:” to mark it as an enhancement request.

Generally speaking the out of resources error is triggered by (1) exceeding maximum block or grid dimensions (2) exceeding shared memory size (3) exceeding available registers per block.

It should be noted that the hardware allocated registers with a granularity greater than 1, so simple multiplication of the per-thread register usage with the number of threads will in most cases underestimate the registered allocated. Use the occupancy calculator instead:

Thanks for replying. I’ve just edited the code and description.

Unable to reproduce. I built for sm_61 using CUDA 11.1 on Windows 10. No error are reported when I run the app, and cuda-memcheck also indicates no errors.

Are you building the code for the architecture of your GPU (e.g. -arch=sm_61)?

Yes. I built on another computer on Windows 10 with larger GPU Memory, it sometimes runs successfully. I wonder if the only problem causes cudaErrorLaunchOutOfResources is “out of register”.

I already addressed this earlier.

I’ll manage to fix it according that. Anyway, thank you very much for replying.