histo_local = cuda.shared.array((128,), numba.int64) histo_local[cuda.threadIdx.x] = 0# initialize to zero cuda.syncthreads() # 确保同一块中的所有线程“注册”初始化 i = cuda.grid(1) threads_per_grid = cuda.gridsize(1) for
from numba.core.errors import NumbaPerformanceWarning def run(size): with nvtx.annotate("Compilation", color="red"): dev_a = cuda.device_array((BLOCKS_PER_GRID,), dtype=np.float32) dev_a_reduce = cuda.device_array((BLOCKS_PER_GRID,), dtype=dev_a.dtype) dev_a_sum = cuda.device_ar...
1], threads_per_grid_y): s_thread += array2d[i0, i1] # Allocate shared array s_block = cuda.shared.array(shared_array_len, numba.float32) # Index the threads linearly: each tid identifies a unique thread in the # 2D grid. tid = cuda.threadIdx.x + cuda....
进行Shared Memory优化后,计算部分的耗时减少了近一半: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 matmul time:1.4370720386505127 在上面的实现过程中,有些地方也比较容易让人迷惑。 声明Shared Memory。这里使用了cuda.shared.array(shape,type),shape为这块数据的向量维度大小,type为Numba数据类型,例如是int...
# own shared array. See the warning below! s_block = cuda.shared.array((threads_per_block,), numba.float32) # We now store the local temporary sum of a single the thread into the # shared array. Since the shared array is sized ...
# own shared array.See the warning below!s_block=cuda.shared.array((threads_per_block,),numba.float32)# We now store the local temporary sumofa single the thread into the # shared array.Since the shared array is sized # threads_per_block==blockDim.x #(1024inthisexample),we should ind...
# Example 4.4: A GPU histogram without as many memory conflicts@cuda.jitdef kernel_histogram_shared(arr, histo): # Create shared array to hold local histogram histo_local = cuda.shared.array((128,), numba.int64) histo_local[cuda.threadIdx.x] = 0 # initialize to zero cuda...
def str_to_array(x): return np.frombuffer(bytes(x, "utf-8"), dtype=np.uint8) def grab_uppercase(x): return x[65 : 65 + 26] def grab_lowercase(x): return x[97 : 97 + 26] my_str = "CUDA by Numba Examples" my_str_array = str_to_array(my_str) ...
fromnumbaimportcuda, float32 # Controls threads per block and shared memory usage. # The computation will be done on blocks of TPBxTPB elements. TPB = 16 @cuda.jit deffast_matmul(A, B, C): # Define an array in the shared memory ...
好家伙,这是直接来写池化层了么,但是提供了池化的算法应用一下就可以了(这里不用 shared 也可以过样例)。 shared = cuda.shared.array(TPB, numba.float32) i = cuda.blockIdx.x * cuda.blockDim.x + cuda.threadIdx.x local_i = cuda.threadIdx.x ...