During initialization, the runtime creates a CUDA context for each device in the system (seeContextfor more details on CUDA contexts). This context is the primary context for this device and it is shared among all the host threads of the application. As part of this context creation, the d...
you want to load identical device code on all devices. This requires loading device code into each CUDA context explicitly. Moreover, libraries and frameworks that do not control context creation and destruction must keep track
此context是此设备的主要上下文,并在需要此设备上的活动上下文的第一个运行时函数中初始化。它在应用程序的所有主机线程之间共享。作为此上下文创建的一部分,设备代码会在必要时进行即时编译(请参阅即时编译)并加载到设备内存中。这一切都是透明地发生的。如果需要,例如对于驱动程序 API 互操作性,可以从驱动程序 API...
The CUPTI-API. The CUDA Profiling Tools Interface (CUPTI) enables the creation of profiling and tracing tools that target CUDA applications. Debugger API The CUDA debugger API. GPUDirect RDMA A technology introduced in Kepler-class GPUs and CUDA 5.0, enabling a direct path for communication betwee...
cudaHostGetDevicePointer() will fail if the cudaDeviceMapHost flag was not specified before deferred context creation occurred, or if called on a device that does not support mapped, pinned memory. For devices that have a non-zero value for the device attribute cudaDevAttrCanUseHostPointerFor...
# Example 3.5: Context Manager for CUDA Timer using Eventsclass CUDATimer: def __init__(self, stream): self.stream = stream self.event = None # in ms def __enter__(self): self.event_beg = cuda.event() self.event_end = cuda.event() self.event_beg.record...
As part of this context creation, the device code is just-in-time compiled if necessary (see Just-in-Time Compilation) and loaded into device memory. This all happens transparently. If needed, for example, for driver API interoperability, the primary context of a device can be accessed from...
来自一个 CUDA context 的内核不能与来自另一个 CUDA context 的内核并发执行。使用许多 textures 或...
function which requires an active context on this device. It is shared among all the host threads of the application. As part of this context creation, the device code is just-in-time compiled if necessary (see Just-in-Time Compilation) and loaded into device memory. This all happens ...
# Array copy to device and creation in the device. With Numba, you pass the # stream as an additional to API functions. dev_a = cuda.to_device(a, stream=stream) dev_a_reduce = cuda.device_array((blocks_per_grid,), dtype=dev_a.dtype, stream=stream) ...