Shared Memory Error

Accelerated Computing CUDA CUDA Programming and Performance
1

My use of shared memory seems to be causing a problem in a program I am writing. It works in deviceemulation mode, but not without. On my linux machine (CUDA 2.0, GTX280) the program crashes X. On my windows machine (CUDA 2.0, GTS8800512) I don’t get a crash, but I do get a memory transfer error copying from the device to the host. On a laptop (CUDA 2.1, 9400M) the program runs fine.

Instead of copying the actual program I’ve produced a small test program that only has the neccesary parts. If inside the kernel I comment out the part reading into shared memory then the program runs fine. Otherwise, we encounter the errors mentioned above.

If anyone has any idea what is causing the crashes I would be grateful. I note it seems to work under CUDA 2.1, so if this has been a bug fix then does anyone know what the bug was in the first place?

#include <stdio.h> #include <cuda.h> struct myStruct { int a; int4 b; float3 c; float3 d; float e; float3 f; float3 g; float3 h; float3 i; float j; int k; }; __global__ void test(myStruct *data) { extern __shared__ myStruct s_data[]; int tid = threadIdx.x; int i = blockIdx.x * blockDim.x + tid; if (i < 300) {	s_data[tid] = data[i]; } __syncthreads(); } void algorithm() { size_t size = sizeof(myStruct); myStruct* h_data; h_data = (myStruct *) malloc(300 * size); myStruct bob; bob.a = 1; bob.b = make_int4(1,1,1,1); bob.c = make_float3(1,1,1); bob.d = make_float3(1,1,1); bob.e = 1; bob.f = make_float3(1,1,1); bob.g = make_float3(1,1,1); bob.h = make_float3(1,1,1); bob.i = make_float3(1,1,1); bob.j = 1; bob.k = 1; for (int i = 0; i < 300; i++)	h_data[i] = bob; myStruct* d_data; if (cudaMalloc((void **) &d_data, 300 * size) != cudaSuccess) { printf("Error allocating memory on device!\n"); exit(1); } if (cudaMemcpy(d_data, h_data, 300 * size, cudaMemcpyHostToDevice) != cudaSuccess) { printf("Error copying host->device"); exit(1); } test <<< 5, 64, 64*size >>> (d_data); if (cudaMemcpy(h_data, d_data, 300 * size, cudaMemcpyDeviceToHost) != cudaSuccess) { printf("Error copying device->host"); exit(1); }; } int main(int argc, char **argv) { printf("Calling algorithm...\n"); algorithm(); printf("Done!"); return 0; }
2

From what I can see, you haven’t allocated any shared memory, so when you try to write to it, it crashes.

This should fix that problem.

extern shared myStruct s_data[300 * sizeof(myStruct)];

3

computerulz!

He used extern shared memory so in his called kernel function

size_t size = sizeof(myStruct);

test <<< 5, 64, 64*size >>> (d_data);

MissSusan!

In your Kernel program, I don’t see any thing wrong.

can you put “extern shared myStruct s_data;” outside the kernel function, like this.

extern shared myStruct s_data;

global void test(myStruct *data)

{

int tid = threadIdx.x;

int i = blockIdx.x * blockDim.x + tid;

if (i < 300) {

s_data[tid] = data[i];

}

__syncthreads();

}

4

“can you put “extern shared myStruct s_data;” outside the kernel function, like this.”

makes no difference :(

5

Runs fine for me. And Valgrind is happy about it, so maybe your simplified test-case is a bit overly simple?

6

I did some experimenting and at first I thought it might be a compiler bug. I was getting “Error copying device->host”.

I finally determined the problem has to do with alignment of the structure.

struct myStruct { int a; int4 b; };

does not exhibit the problem and reports a size of 32, meaning three ints of padding are inserted between a and b.

But

struct myStruct { int4 b; int a; };

reports a size of 20 and gives the “Error copying device->host” error.

However, adding align(16) to the latter case causes it to report a size of 32 and has no error.

So I think what is happening is your structure has an “odd” size, and when the array elments are packed together, each one taking sizeof(myStruct) bytes, the alignment of the int4 is violated. By forcing the alignment of the structure to be 16, it causes the structures to be padded at the end too, which makes all the members have the correct alignment.

Perhaps nvcc should emit a warning if a structure has a weaker alignment restriction than one or more of its members.

7

Thankyou very much, this has indeed solved my problem :)

8

Thank for your experimenting. It will be useful for my future programs.

I will pay attention in this point.

:)