d_B = cuda.mem_alloc(B.nbytes) cuda.memcpy_htod(d_A, A) cuda.memcpy_htod(d_B, B) # 定义矩阵乘法的内核函数matmul_kernel= ElementwiseKernel( "float *A, float *B, float *C", "C[i] = A[i] * B[i]", "matmul_kernel" ) # 执行矩阵乘法 C = gpuarray.empty_like(A) matmul...
b = np.random.rand(N, N).astype(np.float32)cuda.memcpy_htod(a_gpu, a)cuda.memcpy_htod(b_gpu, b) 定义CUDA内核函数 @cuda.jitdef matmul_kernel(a, b, c): tx = cuda.threadIdx.x ty = cuda.threadIdx.y bw = cuda.blockDim.x bh = cuda.blockDim.y ix = tx + cuda.blockIdx.x...
cudaMalloc((void **)&d_res, a_shape_0 * b_shape_1 * sizeof(float)); cudaMemcpy(d_a, ptrA, a_shape_0 * a_shape_1 * sizeof(float), cudaMemcpyHostToDevice); cudaMemcpy(d_b, ptrB, a_shape_1 * b_shape_1 * sizeof(float), cudaMemcpyHostToDevice); //constexpr const int ...
while my TRTengine.infer function is as bellow: def infer(self, batch, scales=None, nms_threshold=None): ** outputs =** ** for shape, dtype in self.output_spec():** ** outputs.append(np.zeros(shape, dtype))** ** cuda.memcpy_htod(self.inputs...
CUDA_SAFE_CALL(cuMemcpyHtoD(dY, hY, bufferSize)); // Execute SAXPY. void*args[] = { &a, &dX, &dY, &dOut, &n }; CUDA_SAFE_CALL( cuLaunchKernel(kernel, NUM_BLOCKS, 1, 1,// grid dim NUM_THREADS, 1, 1,// block dim ...
cuda.memcpy_htod(a_gpu, a) cuda.memcpy_htod(b_gpu, b) # 调用 CUDA 核函数 dot_product = mod.get_function("dot_product") block_size =256 grid_size = (n + block_size -1) // block_size dot_product(a_gpu, b_gpu, c_gpu, np.int32(n), block=(block_size,1,1), grid=(gri...
cuMemcpyHtoD(d_B, h_B, size); // Get function handle from module CUfunction vecAdd; cuModuleGetFunction(&vecAdd, cuModule, "VecAdd"); // Invoke kernel int threadsPerBlock = 256; int blocksPerGrid = (N + threadsPerBlock - 1) / threadsPerBlock; ...
cuMemcpyHtoD(d_B, h_B, size); // Get function handle from module CUfunction vecAdd; cuModuleGetFunction(&vecAdd, cuModule, "VecAdd"); // Invoke kernel int threadsPerBlock = 256; int blocksPerGrid = (N + threadsPerBlock - 1) / threadsPerBlock; ...
在上述示例中,我们首先将输入数组和输出数组分配到CUDA设备上,并使用cuda.memcpy_htod和cuda.memcpy_dtoh函数将数据传输到CUDA设备和主机内存之间。 然后,我们使用SourceModule函数定义了一个名为double_array的CUDA核函数。这个核函数会被调用并在CUDA设备上执行。在这个例子中,我们使用了CUDA提供的threadIdx.x和block...
下面的代码用来统计100000000之内的所有素数个数。 import time import pycuda.autoinit import pycuda.dr...