CUDA provides an API called atomicAdd operations to avoid problems with parallel access of memory locations. It is a blocking operation, which means that when multiple threads try to access the same memory location, only one thread can access the memory location at a time. Other threads have to wait for this thread to finish and write its answer to memory. The kernel function to calculate a histogram using an atomicAdd operation is shown as follows:
import pycuda.autoinitimport pycuda.driver as drvimport numpyimport matplotlib.pyplot as pltfrom pycuda.compiler import SourceModulemod = SourceModule(""" __global__ void atomic_hist(int *d_b, int *d_a, int SIZE){ int tid = threadIdx.x + blockDim.x * blockIdx.x; int item ...
