EQ2EMu/EQ2/source/depends/boost_1_72_0/libs/compute/doc/advanced_topics.qbk

[/===========================================================================
 Copyright (c) 2013-2015 Kyle Lutz <kyle.r.lutz@gmail.com>

 Distributed under the Boost Software License, Version 1.0
 See accompanying file LICENSE_1_0.txt or copy at
 http://www.boost.org/LICENSE_1_0.txt
=============================================================================/]

[section Advanced Topics]

The following topics show advanced features of the Boost Compute library.

[section Vector Data Types]

In addition to the built-in scalar types (e.g. `int` and `float`), OpenCL
also provides vector data types (e.g. `int2` and `vector4`). These can be
used with the Boost Compute library on both the host and device.

Boost.Compute provides typedefs for these types which take the form:
`boost::compute::scalarN_` where `scalar` is a scalar data type (e.g. `int`,
`float`, `char`) and `N` is the size of the vector. Supported vector sizes
are: 2, 4, 8, and 16.

The following example shows how to transfer a set of 3D points stored as an
array of `float`s on the host the device and then calculate the sum of the
point coordinates using the [funcref boost::compute::accumulate accumulate()]
function. The sum is transferred to the host and the centroid computed by
dividing by the total number of points.

Note that even though the points are in 3D, they are stored as `float4` due to
OpenCL's alignment requirements.

[import ../example/point_centroid.cpp]
[point_centroid_example]

[endsect] [/ vector data types]

[section Custom Functions]

The OpenCL runtime and the Boost Compute library provide a number of built-in
functions such as sqrt() and dot() but many times these are not sufficient for
solving the problem at hand.

The Boost Compute library provides a few different ways to create custom
functions that can be passed to the provided algorithms such as
[funcref boost::compute::transform transform()] and
[funcref boost::compute::reduce reduce()].

The most basic method is to provide the raw source code for a function:

``
boost::compute::function<int (int)> add_four =
    boost::compute::make_function_from_source<int (int)>(
        "add_four",
        "int add_four(int x) { return x + 4; }"
    );

boost::compute::transform(input.begin(), input.end(), output.begin(), add_four, queue);
``

This can also be done more succinctly using the [macroref BOOST_COMPUTE_FUNCTION
BOOST_COMPUTE_FUNCTION()] macro:
``
BOOST_COMPUTE_FUNCTION(int, add_four, (int x),
{
    return x + 4;
});

boost::compute::transform(input.begin(), input.end(), output.begin(), add_four, queue);
``

Also see [@http://kylelutz.blogspot.com/2014/03/custom-opencl-functions-in-c-with.html
"Custom OpenCL functions in C++ with Boost.Compute"] for more details.

[endsect] [/ custom functions]

[section Custom Types]

Boost.Compute provides the [macroref BOOST_COMPUTE_ADAPT_STRUCT
BOOST_COMPUTE_ADAPT_STRUCT()] macro which allows a C++ struct/class to be
wrapped and used in OpenCL.

[endsect] [/ custom types]

[section Complex Values]

While OpenCL itself doesn't natively support complex data types, the Boost
Compute library provides them.

To use complex values first include the following header:

``
#include <boost/compute/types/complex.hpp>
``

A vector of complex values can be created like so:

``
// create vector on device
boost::compute::vector<std::complex<float> > vector;

// insert two complex values
vector.push_back(std::complex<float>(1.0f, 3.0f));
vector.push_back(std::complex<float>(2.0f, 4.0f));
``

[endsect] [/ complex values]

[section Lambda Expressions]

The lambda expression framework allows for functions and predicates to be
defined at the call-site of an algorithm.

Lambda expressions use the placeholders `_1` and `_2` to indicate the
arguments. The following declarations will bring the lambda placeholders into
the current scope:

``
using boost::compute::lambda::_1;
using boost::compute::lambda::_2;
``

The following examples show how to use lambda expressions along with the
Boost.Compute algorithms to perform more complex operations on the device.

To count the number of odd values in a vector:

``
boost::compute::count_if(vector.begin(), vector.end(), _1 % 2 == 1, queue);
``

To multiply each value in a vector by three and subtract four:

``
boost::compute::transform(vector.begin(), vector.end(), vector.begin(), _1 * 3 - 4, queue);
``

Lambda expressions can also be used to create function<> objects:

``
boost::compute::function<int(int)> add_four = _1 + 4;
``

[endsect] [/ lambda expressions]

[section Asynchronous Operations]

A major performance bottleneck in GPGPU applications is memory transfer. This
can be alleviated by overlapping memory transfer with computation. The Boost
Compute library provides the [funcref boost::compute::copy_async copy_async()]
function which performs an asynchronous memory transfers between the host and
the device.

For example, to initiate a copy from the host to the device and then perform
other actions:

``
// data on the host
std::vector<float> host_vector = ...

// create a vector on the device
boost::compute::vector<float> device_vector(host_vector.size(), context);

// copy data to the device asynchronously
boost::compute::future<void> f = boost::compute::copy_async(
    host_vector.begin(), host_vector.end(), device_vector.begin(), queue
);

// perform other work on the host or device
// ...

// ensure the copy is completed
f.wait();

// use data on the device (e.g. sort)
boost::compute::sort(device_vector.begin(), device_vector.end(), queue);
``

[endsect] [/ asynchronous operations]

[section Performance Timing]

For example, to measure the time to copy a vector of data from the host to the
device:

[import ../example/time_copy.cpp]
[time_copy_example]

[endsect]

[section OpenCL API Interoperability]

The Boost Compute library is designed to easily interoperate with the OpenCL
API. All of the wrapped classes have conversion operators to their underlying
OpenCL types which allows them to be passed directly to the OpenCL functions.

For example,
``
// create context object
boost::compute::context ctx = boost::compute::default_context();

// query number of devices using the OpenCL API
cl_uint num_devices;
clGetContextInfo(ctx, CL_CONTEXT_NUM_DEVICES, sizeof(cl_uint), &num_devices, 0);
std::cout << "num_devices: " << num_devices << std::endl;
``

[endsect] [/ opencl api interoperability]

[endsect] [/ advanced topics]