cuda

Maximum number of resident blocks per SM?

It seems the that there is a maximum number of resident blocks allowed per SM. But while other "hard" limits are easily found (via, for example, `cudaGetDeviceProperties'), a maximum number of resident blocks doesn't seem to be widely documented.

In the following sample code, I configure the kernel with one thread per block. To test the hypothesis that this GPU (a P100) has a maximum of 32 resident blocks per SM, I create a grid of 56*32 blocks (56 = number of SMs on the P100). Each kernel takes 1 second to process (via a "sleep" routine), so if I have configured the kernel correctly, the code should take 1 second. The timing results confirm this. Configuring with 32*56+1 blocks takes 2 seconds, suggesting the 32 blocks per SM is the maximum allowed per SM.

What I wonder is, why isn't this limit made more widely available? For example, it doesn't show up `cudaGetDeviceProperties'. Where can I find this limit for various GPUs? Or maybe this isn't a real limit, but is derived from other hard limits?

I am running CUDA 10.1

#include <stdio.h>
#include <sys/time.h>

double cpuSecond() {
    struct timeval tp;
    gettimeofday(&tp,NULL);
    return (double) tp.tv_sec + (double)tp.tv_usec*1e-6;
}

#define CLOCK_RATE 1328500  /* Modify from below */
__device__ void sleep(float t) {    
    clock_t t0 = clock64();
    clock_t t1 = t0;
    while ((t1 - t0)/(CLOCK_RATE*1000.0f) < t)
        t1 = clock64();
}

__global__ void mykernel() {
    sleep(1.0);    
}

int main(int argc, char* argv[]) {
    cudaDeviceProp  prop;
    cudaGetDeviceProperties(&prop, 0); 
    int mp = prop.multiProcessorCount;
    //clock_t clock_rate = prop.clockRate;

    int num_blocks = atoi(argv[1]);

    dim3 block(1);
    dim3 grid(num_blocks);  /* N blocks */

    double start = cpuSecond();
    mykernel<<<grid,block>>>();
    cudaDeviceSynchronize();
    double etime = cpuSecond() - start;

    printf("mp          %10d\n",mp);
    printf("blocks/SM   %10.2f\n",num_blocks/((double)mp));
    printf("time        %10.2f\n",etime);

    cudaDeviceReset();
}

Results : 

% srun -p gpuq sm_short 1792
mp                  56
blocks/SM        32.00
time              1.16

% srun -p gpuq sm_short 1793
mp                  56
blocks/SM        32.02
time              2.16

% srun -p gpuq sm_short 3584
mp                  56
blocks/SM        64.00
time              2.16

% srun -p gpuq sm_short 3585
mp                  56
blocks/SM        64.02
time              3.16
Solution

  • Yes, there is a limit to the number of blocks per SM. The maximum number of blocks that can be contained in an SM refers to the maximum number of active blocks in a given time. Blocks can be organized into one- or two-dimensional grids of up to 65,535 blocks in each dimension but the SM of your gpu will be able to accommodate only a certain number of blocks. This limit is linked in two ways to the Compute Capability of your Gpu.

    Hardware limit stated by CUDA.

    Each gpu allows a maximum limit of blocks per SM, regardless of the number of threads it contains and the amount of resources used. For example, a Gpu with compute capability 2.0 has a limit of 8 Blocks/SM while one with compute capability 7.0 has a limit of 32 Blocks/SM. This is the best number of active blocks for each SM that you can achieve: let's call it MAX_BLOCKS.

    Limit derived from the amount of resources used by each block.

    A block is made up of threads and each thread uses a certain number of registers: the more registers it uses, the greater the number of resources used by the block that contains it. Similarly, the amount of shared memory assigned to a block increases the amount of resources the block needs to be allocated. Once a certain value is exceeded, the number of resources needed for a block will be so large that SM will not be able to allocate as many blocks as it is allowed by MAX_BLOCKS: this means that the amount of resources needed for each block is limiting the maximum number of active blocks for each SM.

    How do I find these boundaries?

    CUDA thought about that too. On their site is available the Cuda Occupancy Calculator file (see update at the end) with which you can discover the hardware limits grouped by compute capability. You can also enter the amount of resources used by your blocks (number of threads, registers per threads, bytes of shared memory) and get graphs and important information about the number of active blocks. The first tab of the linked file allows you to calculate the actual use of SM based on the resources used. If you want to know how many registers per thread you use you have to add the -Xptxas -v option to have the compiler tell you how many registers it is using when it creates the PTX. In the last tab of the file you will find the hardware limits grouped by Compute capability.

    --- Update 16 January 2025