cuMemcpyDtoHAsync( hOut.ctypes.get_data(), dOutclass, bufferSize, stream)err, = cuda.cuStreamSynchronize(stream) cuLaunchKernel 函式取得已编译的模块核心和执行配置参数。在与资料传输相同的资料流中启动装置程式码。可以确保仅会在完成资料传输后,执行核心运算,因为资料流中的所有API 呼叫及核心启动都已经...
void py_memcpy_dtoh_async(py::object dest, CUdeviceptr src, py::object stream_py) { py_buffer_wrapper buf_wrapper; buf_wrapper.get(dest.ptr(), PyBUF_ANY_CONTIGUOUS | PyBUF_WRITABLE); PYCUDA_PARSE_STREAM_PY; CUDAPP_CALL_GUARDED_THREADED(cuMemcpyDtoHAsync, (buf_wrapper.m_buf.buf, ...
[cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs] #推理 context.execute_async_v2(bindings=bindings, stream_handle=stream.handle) #复制结果到host上 [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs] # Synchronize the stream stream.synchronize...
HtoD prefetches first cudaStreamSynchronize(s2); cudaMemPrefetchAsync(a + tile_size * (i+1), tile_size * sizeof(size_t), 0, s2); cudaEventRecord(e2, s2); } // offload current tile to the cpu after the kernel is completed using the deferred path cudaMemPrefetchAsync(a + tile_...
(cuFunction,offset);2122cuFuncSetBlockShape(cuFunction,512,1,1);23cuLaunchGridAsync(cuFunction,100,1,stream[i]);24}2526for(inti =0;i <2;i++)27cuMemcpyDtoHAsync(hostPtr+i*size,outputDevPtr+i*size,size,stream[i]);2829cuCtxSynchronize();3031for(inti =0;i <2;i++)32cuStreamDestroy...
[cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in yolo_outputs] stream.synchronize() post_t = time.time() duration = post_t - pre_t compute_duration = end_t - start_t pre_duration = start_t - pre_t post_duration = post_t - end_t if iteration >= num_warmups...
drv.memcpy_dtoh_async(c,c_gpu,stream) 同步流以确保操作完成: stream.synchronize() 打印结果: print(c) 以上代码演示了如何使用PyCUDA进行流编程,包括创建流对象、编写CUDA内核函数、准备输入数据、将数据复制到GPU设备上、使用流执行CUDA内核函数、将结果从GPU设备复制回主机,以及同步流以确保操作完成。通过使用...
CUresult result = cuMemcpyDtoHAsync(g_pFrameYUV[active_field], pDecodedFrame[active_field], (nDecodedPitch * nHeight * 3 / 2), g_ReadbackSID); if (result != CUDA_SUCCESS) { printf("cuMemAllocHost returned %d\n", (int)result); ...
execute_async_v2(bindings=bindings, stream_handle=stream.handle) ### ERROR HAPPENS HERE ### [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs] stream.synchronize() print("TRT model inference result : ") output = outputs[0].host for one in output : print(one...
cuda.memcpy_dtoh_async(host_outputs[0], cuda_outputs[0], stream) # Synchronize the stream stream.synchronize() end = time.time() # Remove any context from the top of the context stack, deactivating it. self.ctx.pop() # Here we use the first row of output in that batch_size = 1...