.. _xrt_native_apis.rst:

..
   comment:: SPDX-License-Identifier: Apache-2.0
   comment:: Copyright (C) 2019-2021 Xilinx, Inc. All rights reserved.


XRT Native APIs
===============

From 2020.2 release XRT provides a new XRT API set in C, C++, and Python flavor. 

To use the native XRT APIs, the host application must link with the **xrt_coreutil** library. 
Compiling host code with XRT native C++ API requires C++ standard with -std=c++17 (or newer). 

Example g++ command

.. code-block:: shell

    g++ -g -std=c++17 -I$XILINX_XRT/include -L$XILINX_XRT/lib -o host.exe host.cpp -lxrt_coreutil -pthread


The XRT native API supports both the C and C++ flavor of APIs. For general host code development, C++-based APIs are recommended, hence this document only describes the C++-based API interfaces. The full Doxygen generated C and C++ API documentation can be found in :doc:`xrt_native.main`.


The C++ Class objects used for the APIs are 

+----------------------+-------------------+------------------------------------------------+
|                      |   C++ Class       |  Header files                                  |
+======================+===================+================================================+
|   Device             | ``xrt::device``   |  ``#include <xrt/xrt_device.h>``               |
+----------------------+-------------------+------------------------------------------------+
|   XCLBIN             | ``xrt::xclbin``   |  ``#include <experimental/xrt_xclbin.h>``      |
+----------------------+-------------------+------------------------------------------------+
|   Buffer             | ``xrt::bo``       |  ``#include <xrt/xrt_bo.h>``                   |
+----------------------+-------------------+------------------------------------------------+
|   Kernel             | ``xrt::kernel``   |  ``#include <xrt/xrt_kernel.h>``               |
+----------------------+-------------------+------------------------------------------------+
|   Run                | ``xrt::run``      |  ``#include <xrt/xrt_kernel.h>``               |
+----------------------+-------------------+------------------------------------------------+
| User-managed Kernel  | ``xrt::ip``       |  ``#include <experimental/xrt_ip.h>``          |
+----------------------+-------------------+------------------------------------------------+
|   Graph              | ``xrt::graph``    |  ``#include <experimental/aie.h>``             |
|                      |                   |                                                |
|                      |                   |  ``#include <experimental/graph.h>``           |
+----------------------+-------------------+------------------------------------------------+

Majority of the core data structures are defined inside in the header files at ``$XILINX_XRT/include/xrt/`` directory. Few newer features such as ``xrt::ip``, ``xrt::aie`` related header files are inside ``$XILINX_XRT/include/experimental`` directory. The API interfaces that are in the experimental folder are subject to breaking changes. 

The common host code flow using the above data structures is as below
   
- Open Xilinx **Device** and Load the **XCLBIN**
- Create **Buffer** objects to transfer data to kernel inputs and outputs
- Use the Buffer class member functions for the data transfer between host and device (before and after the kernel execution).
- Use **Kernel** and **Run** objects to offload and manage the compute-intensive tasks running on FPGA. 
       
      
Below we will walk through the common API usage to accomplish the above tasks. 

Device and XCLBIN
-----------------

Device and XCLBIN class provide fundamental infrastructure-related interfaces. The primary objective of the device and XCLBIN related APIs are
 
- Open a Device
- Load compiled kernel binary (or XCLBIN) onto the device 


The simplest code to load an XCLBIN as below  

.. code:: c++
      :number-lines: 10
           
           unsigned int dev_index = 0;
           auto device = xrt::device(dev_index);
           auto xclbin_uuid = device.load_xclbin("kernel.xclbin");

       
The above code block shows

- The ``xrt::device`` class's constructor is used to open the device (enumerated as 0)
- The member function ``xrt::device::load_xclbin`` is used to load the XCLBIN from the filename. 
- The member function ``xrt::device::load_xclbin`` returns the XCLBIN UUID, which is required to open the kernel (refer the Kernel Section). 

The class constructor ``xrt::device::device(const std::string& bdf)`` also supports opening a device object from a Pcie BDF passed as a string.

.. code:: c++
      :number-lines: 10
           
           auto device = xrt::device("0000:03:00.1");


The ``xrt::device::get_info()`` is a useful member function to obtain necessary information about a device. Some of the information such as Name, BDF can be used to select a specific device to load an XCLBIN

.. code:: c++
      :number-lines: 10
      
           std::cout << "device name:     " << device.get_info<xrt::info::device::name>() << "\n";
           std::cout << "device bdf:      " << device.get_info<xrt::info::device::bdf>() << "\n";

Buffers
-------

Buffers are primarily used to transfer the data between the host and the device. The Buffer related APIs are discussed in the following three subsections

1. Buffer allocation and deallocation
2. Data transfer using Buffers
3. Miscellaneous other Buffer APIs



1. Buffer allocation and deallocation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The C++ interface for buffers as below
   
The class constructor ``xrt::bo`` is mainly used to allocates a buffer object 4K aligned. By default, a regular buffer is created (optionally the user can create other types of buffers by providing a flag). 

.. code:: c++
      :number-lines: 15
           
           auto bank_grp_arg0 = kernel.group_id(0); // Memory bank index for kernel argument 0
           auto bank_grp_arg1 = kernel.group_id(1); // Memory bank index for kernel argument 1
    
           auto input_buffer = xrt::bo(device, buffer_size_in_bytes,bank_grp_arg0);
           auto output_buffer = xrt::bo(device, buffer_size_in_bytes, bank_grp_arg1);

In the above code ``xrt::bo`` buffer objects are created using the class constructor. Please note the following 

- As no special flags are used a regular buffer will be created. Regular buffer is most common type of buffer that has a host backing pointer allocated by user space in heap memory and a device buffer allocated in the specified memory bank. 
- The second argument specifies the buffer size. 
- The third argument is used to specify the enumerated memory bank index (to specify the buffer location) where the buffer should be allocated. There are two ways to specify the memory bank index

 - Through kernel arguments: In the above example, the ``xrt::kernel::group_id()`` member function is used to pass the memory bank index. This member function accept kernel argument-index and automatically detect corresponding memory bank index by inspecting XCLBIN.
 - Passing Memory bank index:  The ``xrt::kernel::group_id()`` also accepts the direct memory bank index (as observed from ``xrt-smi examine --report memory`` output). 
  
  
Creating special Buffers
************************

The ``xrt::bo()`` constructors accept multiple other buffer flags those are described using ``enum class`` argument with the following enumerator values

- ``xrt::bo::flags::normal``: Regular buffer (default)
- ``xrt::bo::flags::device_only``: Device only buffer (meant to be used only by the kernel, there is no host backing pointer).
- ``xrt::bo::flags::host_only``: Host only buffer (buffer resides in the host memory directly transferred to/from the kernel)
- ``xrt::bo::flags::p2p``: P2P buffer, A special type of device-only buffer capable of peer-to-peer transfer 
- ``xrt::bo::flags::cacheable``: Cacheable buffer can be used when the host CPU frequently accessing the buffer (applicable for edge platform).

The below example shows creating a P2P buffer on a device memory bank connected to argument 3 of the kernel. 

.. code:: c++
      :number-lines: 15
           
           auto p2p_buffer = xrt::bo(device, buffer_size_in_byte,xrt::bo::flags::p2p, kernel.group_id(3));

  
Creating Buffers from the user pointer
**************************************

The ``xrt::bo()`` constructor can also be called using a pointer provided by the user. The user pointer must be aligned to 4K boundary.

.. code:: c++
      :number-lines: 15
           
           // Host Memory pointer aligned to 4K boundary
           int *host_ptr;
           posix_memalign(&host_ptr,4096,MAX_LENGTH*sizeof(int)); 
 
           // Sample example filling the allocated host memory       
           for(int i=0; i<MAX_LENGTH; i++) {
           host_ptr[i] = i;  // whatever 
           }
           
           auto mybuf = xrt::bo (device, host_ptr, MAX_LENGTH*sizeof(int), kernel.group_id(3)); 


2. Data transfer using Buffers
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

XRT Buffer API library provides a rich set of APIs helping the data transfers between the host and the device, between the buffers, etc. We will discuss the following data transfer style

I. Data transfer between host and device by Buffer read/write API
II. Data transfer between host and device by Buffer map API
III. Data transfer between buffers by copy API


I. Data transfer between host and device by Buffer read/write API
*****************************************************************

To transfer the data from the host to the device, the user first needs to update the host-side buffer backing pointer followed by a DMA transfer to the device. 

   
The ``xrt::bo`` class has following member functions for the same functionality

1. ``xrt::bo::write()``
2. ``xrt::bo::sync()`` with flag ``XCL_BO_SYNC_BO_TO_DEVICE``

To transfer the data from the device to the host, the steps are reversed, the user first needs to do a DMA transfer from the device followed by the reading data from the host-side buffer backing pointer. 


The corresponding ``xrt::bo`` class's member functions are

1. ``xrt::bo::sync()`` with flag ``XCL_BO_SYNC_BO_FROM_DEVICE``
2. ``xrt::bo::read()``


Code example of transferring data from the host to the device

.. code:: c++
      :number-lines: 20    
           
           auto input_buffer = xrt::bo(device, buffer_size_in_bytes, bank_grp_idx_0);
           // Prepare the input data
           int buff_data[data_size];
           for (auto i=0; i<data_size; ++i) {
               buff_data[i] = i;
           }
    
           input_buffer.write(buff_data);
           input_buffer.sync(XCL_BO_SYNC_BO_TO_DEVICE);

Note the C++ ``xrt::bo::sync``, ``xrt::bo::write``, ``xrt::bo::read`` etc has overloaded version that can be used for partial buffer sync/read/write by specifying the size and the offset. For the above code example, the full buffer size and offset=0 are assumed as default arguments. 

Also note that if the buffer is created through the user-pointer, the ``xrt::bo::write`` or ``xrt::bo::read`` is not required before or after the ``xrt::bo::sync`` call. 

For the device only buffers (created with ``xrt::bo::flags::device_only`` flag) the ``xrt::bo::sync()`` operation is not required, only ``xrt::bo::write()`` (or ``xrt::bo::read()``) is sufficient for DMA operation. As for the device only buffer there is no host backing storage, the ``xrt::bo::write()`` (or ``xrt::bo::read()``) directly performs DMA operation to (or from) the device memory.

Below is the example for creation of device only buffers.

.. code:: c++
      :number-lines: 18

           xrt::bo::flags device_flags = xrt::bo::flags::device_only;
           auto device_only_buffer = xrt::bo(device, size_in_bytes, device_flags, bank_grp_arg0);

Here is how ``xrt::bo::read()`` and ``xrt::bo::write()`` API's to read/write directly from/to device only buffer there is no host backing storage.

- ``xrt::bo::write(const void* src, size_t size, size_t seek)``: Copies data from src to device buffer directly.
- ``xrt::bo::read(void* dst, size_t size, size_t skip)``: Copies data from device buffer to dst.

II. Data transfer between host and device by Buffer map API
***********************************************************

The API ``xrt::bo::map()`` allows mapping the host-side buffer backing pointer to a user pointer. The host code can subsequently exercise the user pointer for the data reads and writes. However, after writing to the mapped pointer (or before reading from the mapped pointer) the API ``xrt::bo::sync()`` should be used with direction flag for the DMA operation. 

Code example of transferring data from the host to the device by this approach

.. code:: c++
      :number-lines: 20
           
           auto input_buffer = xrt::bo(device, buffer_size_in_bytes, bank_grp_idx_0);
           auto input_buffer_mapped = input_buffer.map<int*>();

           for (auto i=0;i<data_size;++i) {
               input_buffer_mapped[i] = i;
           }

           input_buffer.sync(XCL_BO_SYNC_BO_TO_DEVICE);


III. Data transfer between the buffers by copy API
**************************************************

XRT provides ``xrt::bo::copy()`` API for deep copy between the two buffer objects if the platform supports a deep-copy (for detail refer M2M feature described in :doc:`m2m`). If deep copy is not supported by the platform the data transfer happens by shallow copy (the data transfer happens via host). 

.. code:: c++
      :number-lines: 25
           
           
           dst_buffer.copy(src_buffer, copy_size_in_bytes);

The API ``xrt::bo::copy()`` also has overloaded versions to provide a different offset than 0 for both the source and the destination buffer. 

3. Miscellaneous other Buffer APIs
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

This section describes a few other specific use-cases using buffers. 

DMA-BUF API
***********

XRT provides Buffer export and import APIs primarily used for sharing buffer across devices (P2P application) and processes. The buffer handle obtained from ``xrt::bo::export_buffer()`` is essentially a file descriptor, hence sending across the processes requires a suitable IPC mechanism (example, UDS or Unix Domain Socket) to translate the file descriptor of one process into another process.

- ``xrt::bo::export_buffer()``: Export the buffer to an exported buffer handle
- ``xrt::bo()`` constructor: Allocate a BO imported from exported buffer handle


Consider the situation of exporting buffer from device 1 to device 2 (inside same host process). 

.. code:: c++
      :number-lines: 18
      
           auto buffer_exported = buffer_device_1.export_buffer();
           auto buffer_device_2 = xrt::bo(device_2, buffer_exported);

In the above example

- The buffer buffer_device_1 is a buffer allocated on device 1
- buffer_device_1 is exported by the member function ``xrt::bo::export_buffer``
- The new buffer buffer_device_2 is imported for device_2 by the constructor ``xrt::bo``


 
Sub-buffer support
******************

The ``xrt::bo`` class constructor can also be used to allocate a sub-buffer from a parent buffer by specifying a start offset and the size. 

In the example below a sub-buffer is created from a parent buffer of size 4 bytes starting from its offset 0 

.. code:: c++ 
      :number-lines: 18
           
           size_t sub_buffer_size = 4; 
           size_t sub_buffer_offset = 0; 

           auto sub_buffer = xrt::bo(parent_buffer, sub_buffer_size, sub_buffer_offset);


Buffer information
******************

XRT provides few other API Class member functions to obtain information related to the buffer. 

- The member function ``xrt::bo::size()``: Size of the buffer
- The member function ``xrt::bo::address()`` : Physical address of the buffer



Kernel and Run
--------------

To execute a kernel on a device, a kernel class (``xrt::kernel``) object has to be created from currently loaded xclbin.  The kernel object can be used to execute the kernel function on the hardware instance (Compute Unit or CU) of the kernel.  

A Run object (``xrt::run``) represents an execution of the kernel. Upon finishing the kernel execution, the Run object can be reused to invoke the same kernel function if desired. 

The following topics are discussed below

- Obtaining kernel object from XCLBIN
- Getting the bank group index of a kernel argument
- Execution of kernel and dealing with the associated run
- Other kernel related API
       

Obtaining kernel object from XCLBIN
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The kernel object is created from the device, XCLBIN UUID and the kernel name using ``xrt::kernel()`` constructor as shown below

.. code:: c++
      :number-lines: 35
          
           auto xclbin_uuid = device.load_xclbin("kernel.xclbin");
           auto krnl = xrt::kernel(device, xclbin_uuid, name); 

**Note**: A single kernel object (when created by a kernel name) can be used to execute multiple CUs as long as CUs are having identical interface connectivity. If all the CUs of the kernel are not having identical connectivity, XRT assigns a subset of CUs (one or more CUs with identical connectivity) to the created kernel object and discards the rest of the CUs (discarded CUs are not used during the execution of a kernel).  For this type of situation creating a kernel object using mangled CU names can be more useful. 

As an example, assume a kernel name is foo having 3 CUs foo_1, foo_2, foo_3. The CUs foo_1 and foo_2 are connected to DDR bank 0, but the CU foo_3 is connected to DDR bank 1. 

- Opening kernel object for foo_1 and foo_2 (as they have identical interface connection)
       
.. code:: c
      :number-lines: 35
                  
           krnl_obj_1_2 = xrt::kernel(device, xclbin_uuid, "foo:{foo_1,foo_2}");     
   
- Opening kernel object for foo_3
          
.. code:: c
      :number-lines: 35
                  
           krnl_obj_3 = xrt::kernel(device, xclbin_uuid, "foo:{foo_3}");     

   
Getting bank group index of the kernel argument
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

We have seen in the Buffer creation section that it is required to provide the buffer location during the buffer creation. The member function ``xrt::kernel::group_id()`` returns the memory bank index (or id) of a specific argument of the kernel. This id is passed as a parameter of ``xrt::bo()`` constructor to create the buffer on the same memory bank. 


Let us review the example below where the buffer is allocated for the kernel's first (argument index 0) argument. 

.. code:: c++
      :number-lines: 15
                       
           auto input_buffer = xrt::bo(device, buffer_size_in_bytes, kernel.group_id(0));



If the kernel bank index is ambiguous then ``kernel.group_id()`` returns the last memory bank index in the list it maintains. This is the case when the kernel has multiple CU with different connectivity for that argument. For example, let's assume a kernel argument (argument 0) is connected to memory bank 0, 1, 2 (for 3 CUs), then ``kernel.group_id(0)`` will return the last index from the group {0,1,2}, i.e. 2. As a result the buffer is created on the memory bank 2, so the buffer cannot be used for the CU0 and CU1.  

However, in the above situation, the user can always create 3 distinct kernel objects corresponds to 3 CUs (by using the ``{kernel_name:{cu_name(s)}}`` for xrt::kernel constructor) to execute the CUs by separate ``xrt::kernel`` objects. 


Executing the kernel
~~~~~~~~~~~~~~~~~~~~

Execution of the kernel is associated with a **Run** object. The kernel can be executed by the ``xrt::kernel::operator()`` that takes all the kernel arguments in order. The kernel execution API returns a run object corresponding to the execution. 

.. code:: c++
      :number-lines: 50
   
           // 1st kernel execution
           auto run = kernel(buf_a, buf_b, scalar_1); 
           run.wait();
    
           // 2nd kernel execution with just changing 3rd argument
           run.set_arg(2,scalar_2); // Arguments are specified starting from 0 
           run.start();
           run.wait();


The ``xrt::kernel`` class provides **overloaded operator ()** to execute the kernel with a comma-separated list of arguments.  


The above c++ code block is demonstrating 
  
- The kernel execution using the ``xrt::kernel()`` operator with the list of arguments that returns a ``xrt::run`` object. This is an asynchronous API and returns after submitting the task.    
- The member function ``xrt::run::wait()`` is used to block the current thread until the current execution is finished. 
- The member function ``xrt::run::set_arg()`` is used to set one or more kernel argument(s) before the next execution. In the example above, only the last (3rd) argument is changed.  
- The member function ``xrt::run::start()`` is used to start the next kernel execution with new argument(s).   


Other kernel APIs
~~~~~~~~~~~~~~~~~

**Obtaining the run object before execution**: Example of the previous section shows to obtain a ``xrt::run`` object when the kernel is executed (kernel execution returns a run object). However, a ``xrt::run`` object can be obtained even before the kernel execution. The flow is as below

- Open a Run object by the ``xrt::run`` constructor with a kernel argument). 
- Set the kernel arguments associated for the next execution by the member function ``xrt::run::set_arg()``. 
- Execute the kernel by the member function ``xrt::run::start()``.
- Wait for the execution finish by the member function ``xrt::run::wait()``. 

**Timeout while wait for kernel finish**: The member function ``xrt::run::wait()`` blocks the current thread until the kernel execution finishes. To specify a timeout supported API ``xrt::run::wait()`` also accepts a timeout in millisecond unit.


User Managed Kernel
-------------------

The ``xrt::kernel`` is used to execute the kernels with standard control interface through AXI-Lite control registers. These standard control interfaces are well defined and understood by XRT but transparent to the user. These XRT managed kernels should always be represented by ``xrt::kernel`` objects in the host code.  

The XRT also supports custom control interface for a kernel. These type of kernels (a.k.a User-Managed Kernel) must be managed by the user by writing/reading to/from the AXI-Lite registers controlling these kernels. To differentiate from the XRT managed kernel, class ``xrt::ip`` is used to specify a user-managed kernel inside the user host code. 

Creating ``xrt::ip`` object from XCLBIN
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The ``xrt::ip`` object creation is very similar to creating a kernel. 

.. code:: c++
      :number-lines: 35
          
           auto xclbin_uuid = device.load_xclbin("kernel.xclbin");
           auto ip = xrt::ip(device, xclbin_uuid, "ip_name");
           
An ip object can only be opened in exclusive mode. That means at a time, only one thread/process can access IP at the same time. This is required for a safety reason because multiple threads/processes reading/writing to the AXI-Lite registers at the same time potentially leads to a race situation. 

Allocating buffers for the IP inputs/outputs 
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Similar to XRT managed kernel ``xrt::bo`` objects are used to create buffers for IP ports. However, the memory bank location must be specified explicitly by providing enumerated index of the memory bank. 

Below is a example of creating two buffers. Note the last argument of ``xrt::bo`` is the enumerated index of the memory bank as seen by the XRT (in this example index 8 corresponds to the host-memory bank). The bank index can be obtained by ``xrt-smi examine --report memory`` command.  

.. code:: c++
      :number-lines: 35
          
           auto buf_in_a = xrt::bo(device, DATA_SIZE, xrt::bo::flags::host_only, 8);
           auto buf_in_b = xrt::bo(device, DATA_SIZE, xrt::bo::flags::host_only, 8);


Reading and write CU mapped registers
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

To read and write from the AXI-Lite register space to a CU (specified by ``xrt::ip`` object in the host code), the required member functions from the ``xrt::ip`` class are
  
-  ``xrt::ip::read_register``
-  ``xrt::ip::write_register``

.. code:: c++
      :number-lines: 35
       
           int read_data; 
           int write_data = 7; 
              
           auto ip = xrt::ip(device, xclbin_uuid, "foo:{foo_1}"); 

           read_data = ip.read_register(READ_OFFSET);
           ip.write_register(WRITE_OFFSET,write_data); 

In the above code block

- The CU named "foo_1" (name syntax: "kernel_name:{cu_name}") is opened exclusively.
- The Register Read/Write operation is performed. 


Graph
-----

In Versal ACAPs with AI Engines, the XRT Graph class (``xrt::graph``) and its member functions can be used to dynamically load, monitor, and control the graphs executing on the AI Engine array. 

**A note regarding Device and Buffer**: In AIE based application, the device and buffer have some additional functionlities. For this reason the classes ``xrt::aie::device`` and ``xrt::aie::buffer`` are recommended to specify device and buffer objects. 

Graph Opening and Closing
~~~~~~~~~~~~~~~~~~~~~~~~~

The ``xrt::graph`` object can be opened using the uuid of the currently loaded XCLBIN file as shown below 

.. code:: c
      :number-lines: 35
           
           auto xclbin_uuid = device.load_xclbin("kernel.xclbin");
           auto graph = xrt::graph(device, xclbin_uuid, "graph_name");
           

The graph object can be used to execute the graph function on the AIE tiles.

Reset Functions
~~~~~~~~~~~~~~~

The member function ``xrt::graph::reset()`` is used to reset a specified graph by disabling tiles and enabling tile reset. 


.. code:: c
      :number-lines: 45
           
           auto device = xrt::aie::device(0);
           
           // load XCLBIN 
           ...
           
           auto graph = xrt::graph(device, xclbin_uuid, "graph_name");
           // Graph Reset
           graph.reset();


The member function ``xrt::aie::device::reset_array()`` is used to reset the whole AIE array. But after this AIE reset functionality is called, the PDI get lost, so a special AIE only XCLBIN has be loaded (This flow is for advanced user only). 



Graph execution
~~~~~~~~~~~~~~~

XRT provides basic graph execution control interfaces to initialize, run, wait, and terminate graphs for a specific number of iterations. Below we will review some of the common graph execution styles. 

Graph execution for a fixed number of iterations
************************************************

A graph can be executed for a fixed number of iterations followed by a "busy-wait" or a "time-out wait". 

**Busy Wait scheme**

The graph can be executed for a fixed number of iteration by ``xrt::graph::run()`` API using an iteration argument. Subsequently, ``xrt::graph::wait()`` or ``xrt::graph::end()`` API should be used (with argument 0) to wait until graph execution is completed. 

Let's review the below example

- The graph is executed for 3 iterations by API ``xrt::graph::run()`` with the number of iterations as an argument. 
- The API ``xrt::graph::wait(0)`` is used to wait till the iteration is done. 

     - The API `xrt::graph::wait()` is used because the host code needs to execute the graph again. 
- The Graph is executed again for 5 iteration
- The API ``xrt::graph::end(0)`` is used to wait till the iteration is done. 

    - After ``xrt::graph::end()`` the same graph can not be executed. 

.. code:: c
      :number-lines: 35
           
           // start from reset state
           graph.reset();
           
           // run the graph for 3 iteration
           graph.run(3);
           
           // Wait till the graph is done 
           graph.wait(0);  // Use graph::wait if you want to execute the graph again
           
           
           graph.run(5);
           graph.end(0);  // Use graph::end if you are done with the graph execution


**Timeout wait scheme**

As shown in the above example ``xrt::graph::wait(0)`` performs a busy-wait and suspend the execution till the graph is not done. If desired a timeout version of the wait can be achieved by ``xrt::graph::wait(std::chrono::milliseconds)`` which can be used to wait for some specified number of milliseconds, and if the graph is not done do something else in the meantime. An example is shown below

.. code:: c++
      :number-lines: 35
           
           // start from reset state
           graph.reset();
           
           // run the graph for 100 iteration
           graph.run(100);
           
            while (1) {
                          
              try {
                 graph.wait(5);
              }
              catch (const std::system_error& ex) {
            
                 if (ex.code().value() == ETIME) {          
                   
                    std::cout << "Timeout, reenter......" << std::endl;
                    // Do something
             
                 } 
             }
            
             

Infinite Graph Execution
************************

The graph runs infinitely if ``xrt::graph::run()`` is called with iteration argument 0. While a graph running infinitely the APIs ``xrt::graph::wait()``, ``xrt::graph::suspend()`` and ``xrt::graph::end()`` can be used to suspend/end the graph operation after some number of AIE cycles. The API ``xrt::graph::resume()`` is used to execute the infinitely running graph again. 


.. code:: c
      :number-lines: 39
           
           // start from reset state
           graph.reset();
           
           // run the graph infinitely
           graph.run(0);
           
           graph.wait(3000);  // Suspends the graph after 3000 AIE cycles from the previous start 
           
           
           graph.resume(); // Restart the suspended graph again to run forever
           
           graph.suspend(); // Suspend the graph immediately
           
           graph.resume(); // Restart the suspended graph again to run forever
           
           graph.end(5000);  // End the graph operation after 5000 AIE cycles from the previous start


In the example above

- The member function ``xrt::graph::run(0)`` is used to execute the graph infinitely
- The member function ``xrt::graph::wait(3000)`` suspends the graph after 3000 AIE cycles from the graph starts. 

       - If the graph was already run more than 3000 AIE cycles the graph is suspended immediately. 
- The member function ``xrt::graph::resume()`` is used to restart the suspended graph
- The member function ``xrt::graph::suspend()`` is used to suspend the graph immediately
- The member function ``xrt::graph::end(5000)`` is  ending the graph after 5000 AIE cycles from the previous graph start. 
       
       - If the graph was already run more than 5000 AIE cycles the graph ends immediately.
       - Using ``xrt::graph::end()`` eliminates the capability of rerunning the Graph (without loading PDI and a graph reset again). 


Measuring AIE cycle consumed by the Graph
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The member function ``xrt::graph::get_timestamp()`` can be used to determine AIE cycle consumed between a graph start and stop. 

Here in this example, the AIE cycle consumed by 3 iteration is calculated
 

.. code:: c++
      :number-lines: 35
           
           // start from reset state
           graph.reset();
           
           uint64_t begin_t = graph.get_timestamp();
           
           // run the graph for 3 iteration
           graph.run(3);
           
           graph.wait(0); 
           
           uint64_t end_t = graph.get_timestamp();
           
           std::cout<<"Number of AIE cycles consumed in the 3 iteration is: "<< end_t-begin_t; 
           

RTP (Runtime Parameter) control
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The ``xrt::graph`` class contains member function to update and read the runtime parameters of the graph. 

- The member function ``xrt::graph::update()`` to update the RTP 
- The member function ``xrt::graph::read()`` to read the RTP. 

.. code:: c++
      :number-lines: 35

           graph.reset();

           graph.run(2);

           float increment = 1.0;
           graph.update("mm.mm0.in[2]", increment);
     
           // Do more things
           graph.run(16);
           graph.wait(0);
     
           // Read RTP
           float increment_out;
           graph.read("mm.mm0.inout[0]", &increment_out);
           std::cout<<"\n RTP value read<<increment_out; 
 
In the above example, the member function ``xrt::graph::update()`` and ``xrt::graph::read()`` are used to update and read the RTP values respectively. Note the function arguments 
   
- The hierarchical name of the RTP port
- Variable to set/read the RTP

DMA operation to and from Global Memory IO
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The AIE buffer class ``xrt::aie::bo`` supports member function ``xrt::aie::bo::sync()`` that can be used to synchronize the buffer contents between global memory and AIE. The following code shows a sample example


.. code:: c++
      :number-lines: 35

           auto device = xrt::aie::device(0);
       
           // Buffer from global memory (GM) to AIE
           auto in_bo  = xrt::aie::bo (device, SIZE * sizeof (float), 0, 0);
       
           // Buffer from AIE to global memory (GM)
           auto out_bo  = xrt::aie::bo (device, SIZE * sizeof (float), 0, 0);
       
           auto inp_bo_map = in_bo.map<float *>(); 
           auto out_bo_map = out_bo.map<float *>();

           // Prepare input data 
           std::copy(my_float_array,my_float_array+SIZE,inp_bo_map);


           in_bo.sync("in_sink", XCL_BO_SYNC_BO_GMIO_TO_AIE, SIZE * sizeof(float),0); 

           out_bo.sync("out_sink", XCL_BO_SYNC_BO_AIE_TO_GMIO, SIZE * sizeof(float), 0);
       
       
The above code shows

- Input and output buffer (``in_bo`` and ``out_bo``) to the graph are created and mapped to the user space
- The member function ``xrt::aie::bo::sync`` is used for data transfer using the following arguments
    
          - The name of the GMIO ports associated with the DMA transfer
          - The direction of the buffer transfer 
          
                   - GMIO to Graph: ``XCL_BO_SYNC_BO_GMIO_TO_AIE``
                   - Graph to GMIO: ``XCL_BO_SYNC_BO_AIE_TO_GMIO``
          - The size and the offset of the buffer
    
               
GMIOs & External Buffers
------------------------

XRT provides a buffer class ``xrt::aie::buffer`` which represents GMIO & External-Buffers. GMIOs & External Buffers facilitates the movement of data from global memory (like DDR) to the AI Engine and vice versa.Both GMIO and External buffers work together to manage data flow efficiently by ensuring that large datasets can be processed effectively without overwhelming local memory resources


The AIE buffer ``xrt::aie::buffer`` object creation would be succesful if GMIO/External buffer exists with given name.

This class has overloaded member function ``xrt::aie::buffer::sync(...)`` that can be used to synchronize the buffer contents between global memory and AIE.

xrt::aie::buffer::sync(xrt::bo bo, ...) synchronizes the buffer content between xrt::aie::buffer (GMIO/External Buffer) & xrt::bo (Global Memory)

xrt::aie::buffer::sync(xrt::bo ping, xrt::bo pong, ...) configures the External buffer with ping/pong buffer for parallelism. 

The following code shows a sample example with a single input/output GMIO/External Buffer. Data gets transferred from global buffer "in_bo" to "gr.in1"

.. code:: c++
        :number-lines: 1

           auto device = xrt::aie::device(0);
           auto uuid = device.load_xclbin(xclbin-filename);
       
           // Create Buffer in DDR/Global memory & prepare input
           auto in_bo  = xrt::aie::bo (device, SIZE * sizeof (float), 0, 0);
           auto inp_bo_map = in_bo.map<float *>(); 
           std::copy(my_float_array,my_float_array+SIZE,inp_bo_map);
       
           // Create Buffer in DDR/Global memory to store output
           auto out_bo  = xrt::aie::bo (device, SIZE * sizeof (float), 0, 0);
           auto out_bo_map = out_bo.map<float *>();
           
           // create GMIO/External Buffer object for input & sync from in_bo
           auto xrt::aie::buffer in_buffer = xrt::aie::buffer(device, uuid,"gr.in1");
           in_buffer.sync(in_bo, XCL_BO_SYNC_BO_GMIO_TO_AIE, SIZE * sizeof(float),0); 

           //run your graphs which uses output GMIO/External buffer
           
           // create GMIO/External Buffer object for output & sync from out_bo
           auto xrt::aie::buffer out_buffer  = xrt::aie::buffer(device, uuid,"gr.out1");
           out_buffer.sync(out_bo, XCL_BO_SYNC_BO_AIE_TO_GMIO, SIZE * sizeof(float),0); 

This class has overloaded member function ``xrt::aie::buffer::async(...)`` that can be used to initiate an asynchronize operation to synchronize the xrt::bo buffer object

xrt::aie::buffer::async(xrt::bo bo, ...) initiate an asynchronize operation between xrt::aie::buffer (GMIO/External Buffer) & xrt::bo (Global Memory)

xrt::aie::buffer::async(xrt::bo ping,xrt::bo pong, ...) initiate an asynchronize operation between xrt::aie::buffer (GMIO/External Buffer) & ping/pong xrt::bo objects

xrt::aie::buffer::wait() waits for the asynchronize operation to complete

The following code shows a sample example with a single input/output GMIO/External Buffer. Data gets transferred from global buffer "in_bo" to "gr.in1"

.. code:: c++
        :number-lines: 1

           auto device = xrt::aie::device(0);
           auto uuid = device.load_xclbin(xclbin-filename);
       
           // Create Buffer in DDR/Global memory & prepare input
           auto in_bo  = xrt::aie::bo (device, SIZE * sizeof (float), 0, 0);
           auto inp_bo_map = in_bo.map<float *>(); 
           std::copy(my_float_array,my_float_array+SIZE,inp_bo_map);
       
           // Create Buffer in DDR/Global memory to store output
           auto out_bo  = xrt::aie::bo (device, SIZE * sizeof (float), 0, 0);
           auto out_bo_map = out_bo.map<float *>();
           
           // create GMIO/External Buffer object for input & sync from in_bo
           auto xrt::aie::buffer in_buffer = xrt::aie::buffer(device, uuid,"gr.in1");
           in_buffer.async(in_bo, XCL_BO_SYNC_BO_GMIO_TO_AIE, SIZE * sizeof(float),0); 

           //run your graphs which uses output GMIO/External buffer
           
           // create GMIO/External Buffer object for output & sync from out_bo
           auto xrt::aie::buffer out_buffer  = xrt::aie::buffer(device, uuid,"gr.out1");
           out_buffer.async(out_bo, XCL_BO_SYNC_BO_AIE_TO_GMIO, SIZE * sizeof(float),0); 
           out_buffer.wait();

Ping Pong buffers
~~~~~~~~~~~~~~~~~
The following code shows ping-pong buffer example.This shows an example with a ping/ping buffer being set on one of External buffer
           
.. code:: c++
        :number-lines: 1

           auto device = xrt::aie::device(0);
           auto uuid = device.load_xclbin(xclbin-filename);

           // Create a Buffer in DDR/Global Memory & prepare input
           auto in_bo  = xrt::aie::bo (device, SIZE * sizeof (float), 0, 0);
           auto in_bo_map = in_bo.map<float *>(); 
           std::copy(my_float_array,my_float_array+SIZE,in_bo_map);
           
           // create GMIO/External Buffer object for input
           auto xrt::aie::buffer in_buffer  = xrt::aie::buffer(device, uuid,"gr.in1");
           in_buffer.sync(in_bo, XCL_BO_SYNC_BO_AIE_TO_GMIO, SIZE * sizeof(float),0); 
           
           // Create a Buffer in DDR/Global Memory for storing output
           auto out_bo  = xrt::aie::bo (device, SIZE * sizeof (float), 0, 0);
           auto out_bo_map = out_bo.map<float *>();

           // Create Ping/Pong Buffers in global memory for intermediate buffers
           auto ext1_bo  = xrt::aie::bo (device, SIZE * sizeof (float), 0, 0);
           auto ext2_bo  = xrt::aie::bo (device, SIZE * sizeof (float), 0, 0);

           // create GMIO/External Buffer object for input
           auto xrt::aie::buffer ping_pong_bo  = xrt::aie::buffer(device, uuid,"gr.ext1");
           ping_pong_bo.sync(ext1_bo, ext2_bo, XCL_BO_SYNC_BO_GMIO_TO_AIE, SIZE * sizeof(float),0); 
           // create GMIO/External Buffer object for output
           auto xrt::aie::buffer out_buffer  = xrt::aie::buffer(device, uuid,"gr.out1");
           out_buffer.sync(out_bo, XCL_BO_SYNC_BO_AIE_TO_GMIO, SIZE * sizeof(float),0); 

XRT Error API
-------------

In general, XRT APIs can encounter two types of errors:
 
- Synchronous error: Error can be thrown by the API itself. The host code can catch these exception and take necessary steps. 
- Asynchronous error: Errors from the underneath driver, system, hardware, etc. 
       
XRT provides an ``xrt::error`` class and its member functions to retrieve the asynchronous errors into the userspace host code. This helps to debug when something goes wrong.
 
- Member function ``xrt::error::get_error_code()`` - Gets the last error code and its timestamp of a given error class
- Member function ``xrt::error::get_timestamp()`` - Gets the timestamp of the last error
- Member function ``xrt:error::to_string()`` - Gets the description string of a given error code.

**NOTE**: The asynchronous error retrieving APIs are at an early stage of development and only supports AIE related asynchronous errors. Full support for all other asynchronous errors is planned in a future release. 

Example code

.. code:: c++
      :number-lines: 41

           graph.run(runInteration);
           
           try {
              graph.wait(timeout);
           }
           catch (const std::system_error& ex) {
            
              if (ex.code().value() == ETIME) {          
                 xrt::error error(device, XRT_ERROR_CLASS_AIE);

                 auto errCode = error.get_error_code(); 
                 auto timestamp = error.get_timestamp();
                 auto err_str = error.to_string(); 
                  
                 /* code to deal with this specific error */
                 std::cout << err_str << std::endl;
              } else {
               /* Something else */
              }
           }
        
       
The above code shows
     
- After timeout occurs from ``xrt::graph::wait()`` the member functions ``xrt::error`` class are called to retrieve asynchronous error code and timestamp
- Member function ``xrt::error::to_string()`` is called to obtain the error string. 



Profiling
---------

In Versal ACAPs with AI Engines, the XRT Profiling class (``xrt::aie::profiling``) and its member functions can be used to configure AI Engine hardware resources for performance profiling and event tracing.

Create Profiling Event
~~~~~~~~~~~~~~~~~~~~~~

The class constructor ``xrt::aie::profiling`` is used to create profiling event object as shown below 

.. code:: c
      :number-lines: 35
           
           auto event = xrt::aie::profiling(device);
           
The profling object can be used to execute the profiling functions and collect profile statistics by calling profiling APIs.

Start Profiling
~~~~~~~~~~~~~~~

The member function ``xrt::aie::profiling::start()`` is used to start performance counters in AI Engine as per the profiling option passed as an argument. This function configures the performance counters in the AI Engine and starts profiling.


.. code:: c
      :number-lines: 45
           
           auto graph = xrt::graph(device, xclbin_uuid, "graph_name");
           auto handle = event.start(xrt::aie::profiling::profiling_option option, std::string& port1, std::string& port2, int value);
           
           // run graph 
           ...
           s2mm_run.wait();
           
It returns a handle to be used by read and stop.

Read Profiling
~~~~~~~~~~~~~~

The ``xrt::aie::profiling::read`` function will return the current performance counter value associated with the profiling handle. It can be used using profiling event object as shown below

.. code:: c
      :number-lines: 35
           
           long long cycle_count = profile.read();
           
Stop Profiling
~~~~~~~~~~~~~~

The ``xrt::aie::profiling::stop`` function stops the performance profiling associated with the profiling handle and releases the corresponding hardware resources.

.. code:: c
      :number-lines: 35
           
        event.stop();
        double throughput = output_size_in_bytes / (cycle_count *0.8 * 1e-3); 
        // Every AIE cycle is 0.8ns in production board
        std::cout << "Throughput of the graph: " << throughput << " MB/s" << std::endl;
           


Asynchornous Programming with XRT (experimental)
------------------------------------------------

From the 22.1 release, XRT offers a simple asynchronous programming mechanism through the user-defined queues. The ``xrt::queue`` is lightweight, general-purpose queue implementation which is completely separated from core XRT native API data structures. If needed, the user can also use their own queue implementation instead of the implementation offered by ``xrt::queue``. 

XRT queue implementation needs ``#include <experimental/xrt_queue.h`` to be added as the header file. The implementation also use C++17 features so the host code must be compiled with ``g++ -std=c++17``

As a premise, by default, all XRT native APIs execute from the host-thread, so if an API has synchronous behavior the host-thread blocks until that task is finished. For example 

.. code:: c++
      :number-lines: 41
      
          buffer.sync(XCL_BO_SYNC_BO_TO_DEVICE);

As ``xrt::bo::sync`` is a synchronous API, the host thread blocks until its completion. 

XRT defines a queue with ``xrt::queue`` with the following properties

  - Any task enqueued on the queue runs parallel to the original host-thread. So the host threads does not wait for its completion and can do other tasks in parallel. 
  - The task enqueued on the queue must be synchronous in nature
  - All the tasks enqueued on the queue will be completed in the order it is enqueued (strict in-order execution). 
  - The task enqueued on a queue can be any C++ Callable, which can conveniently be expressed by a C++ lambda
  - When a synchronous task is enqueued on a queue, an event (``xrt::queue:event``) is returned. The event can be used for multiple purposes, such as: 
  
       - The host-thread can wait on that event to synchronize with the ``queue::enqueue(task)`` from where the event was generated. 
       - The event can be enqueued to other queues to synchronize among the queues 
       
Let's understand the above properties by reviewing the examples below

Executing synchronous task asynchronously 
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~


.. code:: c++
      :number-lines: 41
      
          auto bo0 = xrt::bo(device, vector_size_bytes, krnl.group_id(0));
          auto bo0_map = bo0.map<dtype*>();
          .... // fill buffer content

          xrt::queue my_queue;
          auto sync_event = queue.enqueue([&bo0] {bo0.sync(XCL_BO_SYNC_BO_TO_DEVICE); });

          myCpuTask(b);  // here we can perform other host task that will run parallel to the above bo::sync task
          
          sync_event.wait();  // stall the host-thread till sync operation completes 
          
The above code shows the synchronous API ``xrt::bo::sync`` is enqueued through an ``xrt::queue``. The argument of xrt::queue is unnamed callable written using C++ lambda capturing buffer object. This technique is useful to execute any synchronous task asynchronously from the host-thread, and while this task is ongoing, the host-thread can do other operation in parallel (``myCpuTask()`` in the above code). The return type of ``xrt::queue::enqueue()`` is type of ``xrt::queue::event`` which is later synchronized to the host-thread by ``xrt::queue::event::wait()`` blocking function. 


Executing multiple tasks through queue 
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Every new ``xrt::queue`` can be thought of as a new thread running parallel to the host-thread executing a series of synchronous tasks following the order they were submitted (enqueued) on the queue. For example, let's consider tasks A, B, C and D as below
   
    - Task A: Host to device data transfer (buffer bo0)
    - Task B: Execute the kernel and wait for the kernel to finish execution
    - Task C: Device to host data transfer (buffer bo_out) 
    - Task D: Check return data bo_out
    
The above four tasks should be executed in-order for correct functionality. To execute them in parallel to the host-thread, these four tasks can be enqueued through a queue as below. 


.. code:: c++
      :number-lines: 41
      
        
          xrt::queue queue;
          queue.enqueue([&bo0] {bo0.sync(XCL_BO_SYNC_BO_TO_DEVICE); });
          queue.enqueue([&run] {run.start(); run.wait(); });
          queue.enqueue([&bo_out] {bo_out.sync(XCL_BO_SYNC_BO_FROM_DEVICE); });
          queue.enqueue([&bo_out_map]{my_function_to_check_data(bo_out_map)});
          
The user can create and use as many queues in the host code to overlap tasks in parallel. Next, we will see how it is possible to synchronize among the queues using the event. 


Using events to synchronize among the queues 
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Let's assume in the above example, it is required to do two host-to-device buffer transfers before the kernel execution. If using a single queue the code would appear as

.. code:: c++
      :number-lines: 41
      
        
          xrt::queue main_queue;
          main_queue.enqueue([&bo0] {bo0.sync(XCL_BO_SYNC_BO_TO_DEVICE); });
          main_queue.enqueue([&bo1] {bo1.sync(XCL_BO_SYNC_BO_TO_DEVICE); });
          main_queue.enqueue([&run] {run.start(); run.wait(); });
          main_queue.enqueue([&bo_out] {bo_out.sync(XCL_BO_SYNC_BO_FROM_DEVICE); });

In the above code, as a single queue (``main_queue``) is used, the host-to-device data transfers for buffer ``bo0`` and ``bo1`` would happen sequentially. In order to do parallel data transfer for ``bo0`` and ``bo1``, a separate queue is needed for one of the buffers, and also it is required to ensure that the kernel executes only after both the buffer transfers are completed. 

.. code:: c++
      :number-lines: 41
      
        
          xrt::queue main_queue;
          xrt::queue queue_bo1;
          main_queue.enqueue([&bo0] {bo0.sync(XCL_BO_SYNC_BO_TO_DEVICE); });
          auto bo1_event = queue_bo1.enqueue([&bo1] {bo1.sync(XCL_BO_SYNC_BO_TO_DEVICE); });
          main_queue.enqueue(bo1_event); 
          main_queue.enqueue([&run] {run.start(); run.wait(); });
          main_queue.enqueue([&bo_out] {bo_out.sync(XCL_BO_SYNC_BO_FROM_DEVICE); });

In line number 43 and 44 ``bo0`` and ``bo1`` host-to-device data transfers are enqueued through two separate queues to achieve parallel transfers. To synchronize between these two queues the returned event from the ``queue_bo1`` is enqueued in the ``main_queue``, similar to a task enqueue (line 45). As a result, any other task submitted after that event won't execute until the event is finished. So in the above code example, subsequent task in the ``main_queue`` (such as kernel execution) would wait till the ``bo1_event`` is completed. By submitting an event returned from a ``queue::enqueue`` to another queue, we can synchronize among the queues. 

