CUDA kernel: performance drops by 10x when increased loop count by 10%

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I have a simple CUDA kernel to test loop unrolling, then discovered another thing: when the loop count is 10, kernel takes 34 milliseconds to perform, when the loop count is 90, it takes 59 milliseconds, but when the loop count is 100, the time it takes is 423 milliseconds! Launch configuration is the same, only loop count changed. So, my question is, what could be the reason for this performance drop?

Here is the code, input is an array of 128x1024x1024 elements, and I'm using PyCUDA:

__global__ void copy(float *input, float *output) {
  int tidx = blockIdx.y * blockDim.x + threadIdx.x;
  int stride = 1024 * 1024;
  for (int i = 0; i < 128; i++) {
    int idx = i * stride + tidx;
    float x = input[idx];
    float y = 0;

    for (int j = 0; j < 100; j += 10) {
      x = x + sqrt(float(j));
      y = sqrt(abs(x)) + sin(x) + cos(x);

      x = x + sqrt(float(j+1));
      y = sqrt(abs(x)) + sin(x) + cos(x);

      x = x + sqrt(float(j+2));
      y = sqrt(abs(x)) + sin(x) + cos(x);

      x = x + sqrt(float(j+3));
      y = sqrt(abs(x)) + sin(x) + cos(x);

      x = x + sqrt(float(j+4));
      y = sqrt(abs(x)) + sin(x) + cos(x);

      x = x + sqrt(float(j+5));
      y = sqrt(abs(x)) + sin(x) + cos(x);

      x = x + sqrt(float(j+6));
      y = sqrt(abs(x)) + sin(x) + cos(x);

      x = x + sqrt(float(j+7));
      y = sqrt(abs(x)) + sin(x) + cos(x);

      x = x + sqrt(float(j+8));
      y = sqrt(abs(x)) + sin(x) + cos(x);

      x = x + sqrt(float(j+9));
      y = sqrt(abs(x)) + sin(x) + cos(x);
    }

    output[idx] = y;
  }
}

The loop count I mentioned is this line:

for (int j = 0; j < 100; j += 10)

And sample outputs here:

10 loops

griddimx: 1 griddimy: 1024 griddimz: 1
blockdimx: 1024 blockdimy: 1 blockdimz: 1
nthreads: 1048576 blocks: 1024
prefetch.py:82: UserWarning: The CUDA compiler succeeded, but said the following:
ptxas info    : 0 bytes gmem, 24 bytes cmem[3]
ptxas info    : Compiling entry function 'copy' for 'sm_61'
ptxas info    : Function properties for copy
32 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads
ptxas info    : Used 21 registers, 336 bytes cmem[0], 52 bytes cmem[2]

computation takes 34.24 miliseconds

90 loops

griddimx: 1 griddimy: 1024 griddimz: 1
blockdimx: 1024 blockdimy: 1 blockdimz: 1
nthreads: 1048576 blocks: 1024
prefetch.py:82: UserWarning: The CUDA compiler succeeded, but said the following:
ptxas info    : 0 bytes gmem, 24 bytes cmem[3]
ptxas info    : Compiling entry function 'copy' for 'sm_61'
ptxas info    : Function properties for copy
32 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads
ptxas info    : Used 21 registers, 336 bytes cmem[0], 52 bytes cmem[2]

computation takes 59.33 miliseconds

100 loops

griddimx: 1 griddimy: 1024 griddimz: 1
blockdimx: 1024 blockdimy: 1 blockdimz: 1
nthreads: 1048576 blocks: 1024
prefetch.py:82: UserWarning: The CUDA compiler succeeded, but said the following:
ptxas info    : 0 bytes gmem, 24 bytes cmem[3]
ptxas info    : Compiling entry function 'copy' for 'sm_61'
ptxas info    : Function properties for copy
32 bytes stack frame, 0 bytes spill stores, 0 bytes spill loads
ptxas info    : Used 22 registers, 336 bytes cmem[0], 52 bytes cmem[2]

computation takes 422.96 miliseconds

cuda parallel-processing kernel loops performance
1 Answers

The problem seems to come from loop unrolling.

Indeed, the 10-loops case can be trivially unrolled by NVCC since the loop is actually always executed once (thus the for line can be removed with j set to 0). The 90-loops case is unrolled by NVCC (there are only 9 actual iterations). The resulting code is thus much bigger but still fast since no branches are performed (GPUs hate branches). However, the 100-loops case is not unrolled by NVCC (you hit a threshold of the compiler optimizer). The resulting code is small, but it leads to more branches being executed at runtime: branching is performed for each executed loop iteration (a total of 10). You can see the assembly code difference here.

You can force unrolling using the directive #pragma unroll. However, keep in mind that increasing the size of a code can reduce its performance.

PS: the slightly higher number of register used in the last version may decrease performance, but simulations show that it should be OK in this case.

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