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namespace arm_compute
{
/**
@page programming_model Programming Model

@tableofcontents

@section programming_model_functions Functions

Functions will automatically allocate the temporary buffers mentioned above, and will automatically multi-thread kernels' executions using the very basic scheduler described in the previous section.

Simple functions only call a single kernel (e.g NEConvolution3x3), while more complex ones consist of several kernels pipelined together (e.g @ref NEFullyConnectedLayer ). Check their documentation to find out which kernels are used by each function.

@code{.cpp}
//Create a function object:
MyFunction function;
// Initialize the function with the input/output and options you want to use:
function.configure( input, output, option0, option1);
// Execute the function:
function.run();
@endcode

@warning The Compute Library requires Arm® Mali™ OpenCL DDK r8p0 or higher (OpenCL kernels are compiled using the -cl-arm-non-uniform-work-group-size flag)

@note All OpenCL functions and objects in the runtime library use the command queue associated with CLScheduler for all operations, a real implementation would be expected to use different queues for mapping operations and kernels in order to reach a better GPU utilization.

@section programming_model_scheduler OpenCL Scheduler

The Compute Library runtime uses a single command queue and context for all the operations.

The user can get / set this context and command queue through CLScheduler's interface.

The user can get / set the target GPU device through the CLScheduler's interface.

@attention Make sure the application is using the same context as the library as in OpenCL it is forbidden to share objects across contexts. This is done by calling @ref CLScheduler::init() or @ref CLScheduler::default_init() at the beginning of your application.

@attention Make sure the scheduler's target is not changed after function classes are created.

@section programming_model__events_sync OpenCL events and synchronization

In order to block until all the jobs in the CLScheduler's command queue are done executing the user can call @ref CLScheduler::sync() or create a sync event using @ref CLScheduler::enqueue_sync_event()

@section programming_model_cl_neon OpenCL / Arm® Neon™ interoperability

You can mix OpenCL and Arm® Neon™ kernels and functions. However it is the user's responsibility to handle the mapping/unmapping of OpenCL objects.
*/
} // namespace arm_compute