对于每个结果数组,使用copy_to_host方法将其从设备复制到主机。Numba 核函数由装饰器@cuda.jit标识。函数主体包含许多关键的 2D 随机变量:stock price s_path、isMO、buySellMO、alpha_path、inventory q_path、wealth X_path。 @cuda.jit(debug=False) def
my_str_array_gpu = cuda.to_device(my_str_array) histo_gpu = cuda.device_array((128,), dtype=np.int64) kernel_zero_init[1, 128](histo_gpu) kernel_histogram[blocks_per_grid, threads_per_block](my_str_array_gpu, histo_gpu) histo_cuda = histo_gpu.copy_to_host() 太棒了!所以至少...
但是,CUDA C++开发者可以访问许多目前未在 Python 中公开的库,包括 CUDA 核心计算库(CCCL)、cuRAND 以及头文件实现的数字类型,例如 bfloat16 等。 虽然每个 CUDA C++ 库都可以用自己的方式介绍给 Python,但是手动为每个库进行绑定是一项费力、重复的工作,并且容易出现不一致。例如,float16 和 bfloat16 数据类型定...
def reduce_naive(array, partial_reduction): i_start = cuda.grid(1) threads_per_grid = cuda.blockDim.x * cuda.gridDim.x s_thread = 0.0 for i_arr in range(i_start, array.size, threads_per_grid): s_thread += array[i_arr] # We need to create a special *shared* array which wi...
简单的numba + CUDA 实测 起因 numba + CUDA numba天生支持NumPy,但是CUDA部分仅提供非常有限的支持 CUDA部分代码 简单的numba + CUDA 实测 起因 一时兴起,是我太闲了吧。 最近需要对一个4k图像进行单个像素级别的处理,由于用python用得人有点懒,直接上python在所有像素上循环一遍。每个像素做的工作其实很简单,就...
version using shared memory. ''' s_A = cuda.shared.array((TPB, TPB), dtype=float64) # s_ --> shared s_B = cuda.shared.array((TPB, TPB), dtype=float64) x, y = cuda.grid(2) tx = cuda.threadIdx.x ty = cuda.threadIdx.y bw = cuda.blockDim.x bh = cuda.blockDim.y #...
简单的numba + CUDA 实测 起因 numba + CUDA numba天生支持NumPy,但是CUDA部分仅提供非常有限的支持 CUDA部分代码 简单的numba + CUDA 实测 起因 一时兴起,是我太闲了吧。 最近需要对一个4k图像进行单个像素级别的处理,由于用python用得人有点懒,直接上python在所有像素上循环一遍。每个像素做的工作其实很简单,就...
Creating a copy of a device array is not trivial, and should be. A couple of current workarounds are: # Variant 1 @numba.vectorize(['float32(float32)'], target='cuda') def copy(x): return x # Variant 2 def copy_devarray(arr): new_arr = cuda.device_array_like(arr) new_arr[...
In addition to JIT compiling NumPy array code for the CPU or GPU, Numba exposes “CUDA Python”: the NVIDIA®CUDA®programming model for NVIDIA GPUs in Python syntax. By speeding up Python, its ability is extended from a glue language to a complete programming environment that can execute...
If you pass a NumPy array to a CUDA function, Numba will allocate the GPU memory and handle the host-to-device and device-to-host copies automatically. This may not be the most performant way to use the GPU, but it is extremely convenient when prototyping. Those NumPy arrays can always ...