Unexpected Behavior with __syncthreads() in CUDA Kernel Leading to Deadlock and Garbage Values

Accelerated Computing CUDA CUDA Programming and Performance
1

Hello everyone,

I am encountering a perplexing issue with a CUDA kernel I’ve written. The kernel is designed to update a matrix and compute some results. It runs perfectly on my local laptop’s GPU, but when I compile and run it on a server or Google Colab, the application runs indefinitely, suggesting a deadlock.

Kernel Code

__global__ void updateMatrix(double* matrix, double* result, int lpivot, int m, int n, double* up, double* cl)
{
    double b = (*up) * matrix[(lpivot - 1) * n + lpivot - 1];
    if (b >= 0.0) return;
    b = 1 / b;

    extern __shared__ double sharedPivot[];
    __shared__ double sharedResults[ROW_PER_BLOCK][COL_PER_BLOCK];

    int tidx = threadIdx.x + blockIdx.x * blockDim.x;
    int tidy = threadIdx.y + blockIdx.y * blockDim.y;
    
    int row = lpivot + blockIdx.y * blockDim.y + threadIdx.y; 
    
    int iterations =  (n - lpivot + COL_PER_BLOCK - 1) / COL_PER_BLOCK;  
        
    for(int it = 0; it < iterations; it++)
    {
        int colStart = lpivot + it * blockDim.x; 
        
        if (threadIdx.y == 0 && colStart + threadIdx.x < n) 
        {   
            sharedPivot[threadIdx.x] = matrix[(lpivot - 1) * n + colStart + threadIdx.x];
        }    
        
        __syncthreads();  

        if (row < m && colStart + threadIdx.x < n) 
        {  
            sharedResults[threadIdx.y][threadIdx.x] = matrix[row * n + colStart + threadIdx.x] * sharedPivot[threadIdx.x];
        }
        else 
        {
            sharedResults[threadIdx.y][threadIdx.x] = 0;
        }
        __syncthreads();  
        
        reduceSum<COL_PER_BLOCK>(sharedResults[threadIdx.y]);
        
        if (row < m && threadIdx.x == 0) 
        {
            result[row - lpivot] += sharedResults[threadIdx.y][0];
        }

        __syncthreads();

        if(tidx < COL_PER_BLOCK && tidy < ROW_PER_BLOCK)
        {
            sharedPivot[tidx] = 0;
            sharedResults[tidy][tidx] = 0;
        }
        __syncthreads();
    }
   
    if(row < m && threadIdx.x == 0)
    {     
        result[row - lpivot] += matrix[row * n + lpivot - 1] * (*up);   
        if (result[row - lpivot] == 0) return;
        result[row - lpivot] *= b;
        matrix[row * n + lpivot - 1] += result[row - lpivot] * (*up);
    }
    __syncthreads();

    for (int it = 0; it < iterations; it++) 
    {
       int col = it * blockDim.x + threadIdx.x + lpivot;
       
       if (col < n && row < m)  
        {
            matrix[row * n + col] += result[row - lpivot] * matrix[(lpivot - 1) * n + col];
        }
        __syncthreads();

        if (row == lpivot && col < n)
        {
            sharedPivot[threadIdx.x] = matrix[row * n + col] * matrix[row * n + col];
        }
        else 
        {
            sharedPivot[threadIdx.x] = 0;
        }
        __syncthreads();

        reduceSum<COL_PER_BLOCK>(sharedPivot);
        if (threadIdx.x == 0) 
        {
            atomicAdd(&sumOfSquares, sharedPivot[0]);
        }     
        __syncthreads();
    }

    if (lpivot == 1)
        printf("sumOfSquares = %f\n", sumOfSquares);

    atomicAdd(&counter, 1);

    printf("lpivot = %d counter = %d\n", counter);

    if (counter == blockDim.x * blockDim.y * gridDim.y)
    {
        *cl = matrix[lpivot * n + lpivot];
        *cl = (*cl > 0.0) ? -sqrt(sumOfSquares) : sqrt(sumOfSquares);
        *up = matrix[lpivot * n + lpivot] - *cl;
        matrix[lpivot * n + lpivot] = *cl;
        sumOfSquares = 0; // Reset for the next iteration
        counter = 0;
    }
}

Deadlock Identification: I initially identified that the threads entering the following block were not leaving it due to conditional synchronization.

if (row == lpivot && col < n)
{
    sharedPivot[threadIdx.x] = matrix[row * 
  n + col] * matrix[row * n + col];
    __syncthreads();
    reduceSum<COL_PER_BLOCK>(sharedPivot);
    if (threadIdx.x == 0) 
    {
        sumOfSquares += sharedPivot[0];
    }
    sharedPivot[threadIdx.x] = 0;
    __syncthreads();
}

After moving __syncthreads() out of conditional blocks, the kernel avoids deadlock but results in garbage values being loaded into shared memory, leading to incorrect computation of sumOfSquares, which is printed as zero.

 for (int it = 0; it < iterations; it++) 
    {
       int col = it * blockDim.x + threadIdx.x + lpivot;
       
        if (col < n && row < m)  
        {
            matrix[row * n + col] += result[row - lpivot] * matrix[(lpivot - 1) * n + col];
        }
         __syncthreads();

        if(row == lpivot && col < n)
        {
            if(lpivot == 1)
            printf("before shared pivot loaded sharedPivot[%d] = %f\n",threadIdx.x , sharedPivot[threadIdx.x]);
            sharedPivot[threadIdx.x] = matrix[row * n + col] * matrix[row * n + col];
            if(lpivot == 1)
            printf("after shared pivot loaded sharedPivot[%d] = %f\n",threadIdx.x , sharedPivot[threadIdx.x]);
        }

        else
            sharedPivot[threadIdx.x] = 0;
         __syncthreads();
                
         if(row == lpivot && col < n)
         {
            reduceSum<COL_PER_BLOCK>(sharedPivot);        
            if(threadIdx.x == 0) 
            {
              sumOfSquares += sharedPivot[0];
            }  
              printf("sum of sqaures = %f\n",sumOfSqaures);
        }
        sharedPivot[threadIdx.x] = 0;  
        __syncthreads();
    }

How can I properly handle synchronization in this scenario to avoid deadlocks without introducing garbage values? Is there a more robust way to ensure correct accumulation of sumOfSquares across all threads?

2

Please post a complete minimal example that reproduces your problem.

4

The code does not compile for me using cuda 12.4

main.cu(180): error: identifier "gpuErrchk" is undefined
      gpuErrchk(cudaMalloc((void**)&matrix, sizeof(double) * m * n));
      ^

main.cu(196): error: identifier "start" is undefined
      cudaEventRecord(start);
                      ^

main.cu(198): error: identifier "updatePivotElement" is undefined
      updatePivotElement<<<1, 512, 512 * sizeof(double)>>>(matrix, 1, m, n, d_up_old, d_cl);
      ^

main.cu(225): error: expected a ";"
      householder(input_matrix);
5

I am sorry , i have updated the code , please try again

6

Now the code compiles but execution gives segmentation fault.