注: 参考代码 fast_atomic_add_half.cu 中, 笔者认为存在一些错误, 包括 main() 函数中 output_device 需要至少分配 2 个 half 元素大小, 即 sizeof(half)*2; 同时 dot() 函数调用 FastAdd() 时第三个参数应为 output_device 的大小 2 而非N. 选择 2 的原因正是为了让 FastSpecializedAtomicAdd() 函...
cuda.atomic.exch(array, idx, val)它只是原子赋值array[idx] = val，返回array[idx]的旧值(原子加载)。因为我们不会使用这个函数的返回值，所以可以把它看作一个原子赋值(例如，atomic_add(array, idx, val)是array[idx] += val，就像exch(array, idx, val)是array[idx] = val一样)。我们介绍了锁定和...
nb_time+=time1-time0print('The time cost of numpy is: {}s'.format(np_time))print('The time cost of numba is: {}s'.format(nb_time)) 这里需要重点关注的就是用CUDA实现的简单函数ReducedSum,这个函数中调用了CUDA的atomic.add方法,用这个方法直接替代系统内置的加法,就完成了所有的操作。我们将...
For example, locks and atomic operations help ensure correct behavior by protecting updates to shared values. However, we are all fallible. In complex code with thousands of threads, it may be ambiguous whether there is even an issue. The shared value may well still increase, just not in ...
我们介绍了锁定和解锁机制,让我们使用使用互斥锁实现原子“add”。 # Example 4.5: An atomic add with mutex. @cuda.jit(device=True) def lock(mutex): while cuda.atomic.compare_and_swap(mutex, 0, 1) != 0: pass cuda.threadfence() @cuda.jit(device=True) ...
cuda.atomic.add(x, 0, 1) # Arguments are array, array index, value to add dev_val = cuda.to_device(np.zeros((1,))) add_one_atomic[10, 16](dev_val) dev_val.copy_to_host() --- array([160.]) 原子加法:计算直方图 为了更好地理解原子序数在哪里以及如何使用,我们将使用直方图计算。
gpu_increment_without_atomic << <NUM_THREADS / BLOCK_WIDTH, BLOCK_WIDTH >> >(d_a); // copy back the array to host memory cudaMemcpy(h_a, d_a, ARRAY_BYTES, cudaMemcpyDeviceToHost); printf("Number of times a particular Array index has been incremented without atomic add is: \n")...
// then add the shared memory values to the values from // the other thread blocks using global memory // atomic adds // same as before, since we have 256 threads, updating the // global histogram is just one write per thread!
cuda float atomic操作 atomic add.用第二个,暂时还没弄明白 #ifdef FLOAT#defineT float#else#defineT int#endif#ifdef FORUM __device__ inlinevoidatomicAdd(float*address,floatval){inti_val =__float_as_int(val);inttmp0 =0;inttmp1;while( (tmp1 = atomicCAS((int*)address, tmp0, i_val)) ...
使用共享内存原子操作: #include<iostream> #include"cuda_runtime.h"#include"device_launch_parameters.h"#define N10__global__ void f(int* a ,int*b) { __shared__ unsignedinttemp[N];intx = blockIdx.x*blockDim.x + threadIdx.x; temp[x]=0;//将共享内存所有元素清0__syncthreads(); ato...