350MB of GPU memory disappear right after context initialization

Accelerated Computing CUDA CUDA Programming and Performance
#1

Hello All,
I’ve just noticed that 350MB of GPU memory mysteriously disappear right after context initialization. Moreover, GPU0 looses twice as much, i.e. 700MB. I have 3 cards in a box –
Device 0: GeForce GTX 480, 1535MB, CC 2.0
Device 1: Tesla C2050, 3071MB, CC 2.0
Device 2: GeForce GTX 480, 1535MB, CC 2.0

But when I call cuMemGetInfo(), I get
0: available GPU memory: 835MB
1: available GPU memory: 2720MB
2: available GPU memory: 1183MB

I’m not running X. When X is launched, it grabs additional ~30MB from GPU0 –
0: available GPU memory: 801MB
1: available GPU memory: 2720MB
2: available GPU memory: 1183MB
which makes a lot of sense… But where are those 350MB?
I use cuda 3.2 on fedora

Any ideas what’s going on?
Thanks a lot!

#2

Apparently, this problem doesn’t exist on another machine running centos. I would greatly appreciate if you could compile/run this code and post your results here!

nvcc memory.cu -o memory -L/usr/local/cuda/lib64 -lcuda

#include <stdio.h>

#include <pthread.h>

#include <cuda.h>

__global__ void Null(){}

void* PrintMemory(void* index)

{

   int id = *((int*)index);

   cudaSetDevice(id);

   Null <<< 1, 1 >>> ();

   size_t mem_tot, mem_free;

   cuMemGetInfo(&mem_free, &mem_tot);

   printf("%d: %dMB free, %dMB total\n", id, mem_free >> 20, mem_tot >> 20);

   return NULL;

}

int main(int argc, char* argv[])

{

   int tot_gpus = 0;

   cudaGetDeviceCount(&tot_gpus);

   if(!tot_gpus){

      printf("GPU not found\n");

      return 1;

   }

   printf("found %d CUDA devices\n", tot_gpus);

for(int i = 0; i < tot_gpus; i++){

      cudaDeviceProp dprop;

      cudaGetDeviceProperties(&dprop, i);

      printf("   Device %d: %16s, %4dMB, CC %d.%d\n", i, dprop.name, dprop.totalGlobalMem >> 20, dprop.major, dprop.minor);

   }

int data[8] = {0,1,2,3,4,5,6,7};

   pthread_t thread_handle[8];

   for(int i = 0; i < tot_gpus; i++)

      pthread_create(thread_handle+i, NULL, PrintMemory, (void*)(data+i));

   for(int i = 0; i < tot_gpus; i++)

      pthread_join(thread_handle[i], NULL);

   return 0;

}
#3

200-300 MB loss seems normal. In my machine

% ./memory

found 2 CUDA devices

Device 0: GeForce GTX 480, 1535MB, CC 2.0

Device 1: GeForce GTX 480, 1535MB, CC 2.0

0: 1336MB free, 1535MB total

1: 1336MB free, 1535MB total

#4

It’s probably static context buffer allocation, which are configurable. The defaults have been increased in toolkit 3.2 for printf() buffers and such, but you can set them yourself with something like:

cudaThreadSetLimit(cudaLimitStackSize, 32768);

Why one GPU could use more than another, I don’t know, but perhaps you haven’t initialized the same number of contexts on all of them.

#5

This looks like a bug in the CUDA 3.2 driver. We are actively investigating this. Thanks for the report!

–Cliff

#6

Cliff, Steve was right regarding context initialization – I did initialize two on card 0 for some stupid reason.
Thank you guys!