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## Advanced Topics

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

### 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 `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.

    #include <iostream>

    #include <boost/compute/algorithm/copy.hpp>
    #include <boost/compute/algorithm/accumulate.hpp>
    #include <boost/compute/container/vector.hpp>
    #include <boost/compute/types/fundamental.hpp>

    namespace compute = boost::compute;

    // the point centroid example calculates and displays the
    // centroid of a set of 3D points stored as float4's
    int main()
    {
        using compute::float4_;

        // get default device and setup context
        compute::device device = compute::system::default_device();
        compute::context context(device);
        compute::command_queue queue(context, device);

        // point coordinates
        float points[] = { 1.0f, 2.0f, 3.0f, 0.0f,
                           -2.0f, -3.0f, 4.0f, 0.0f,
                           1.0f, -2.0f, 2.5f, 0.0f,
                           -7.0f, -3.0f, -2.0f, 0.0f,
                           3.0f, 4.0f, -5.0f, 0.0f };

        // create vector for five points
        compute::vector<float4_> vector(5, context);

        // copy point data to the device
        compute::copy(
            reinterpret_cast<float4_ *>(points),
            reinterpret_cast<float4_ *>(points) + 5,
            vector.begin(),
            queue
        );

        // calculate sum
        float4_ sum = compute::accumulate(
            vector.begin(), vector.end(), float4_(0, 0, 0, 0), queue
        );

        // calculate centroid
        float4_ centroid;
        for(size_t i = 0; i < 3; i++){
            centroid[i] = sum[i] / 5.0f;
        }

        // print centroid
        std::cout << "centroid: " << centroid << std::endl;

        return 0;
    }

### 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 `transform()` and `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 `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 "Custom OpenCL functions in C++ with Boost.Compute" for more details.

### Custom Types

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

### 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));

### 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;

### 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 `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);

### Performance Timing

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

    #include <vector>
    #include <cstdlib>
    #include <iostream>

    #include <boost/compute/event.hpp>
    #include <boost/compute/system.hpp>
    #include <boost/compute/algorithm/copy.hpp>
    #include <boost/compute/async/future.hpp>
    #include <boost/compute/container/vector.hpp>

    namespace compute = boost::compute;

    int main()
    {
        // get the default device
        compute::device gpu = compute::system::default_device();

        // create context for default device
        compute::context context(gpu);

        // create command queue with profiling enabled
        compute::command_queue queue(
            context, gpu, compute::command_queue::enable_profiling
        );

        // generate random data on the host
        std::vector<int> host_vector(16000000);
        std::generate(host_vector.begin(), host_vector.end(), rand);

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

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

        // wait for copy to finish
        future.wait();

        // get elapsed time from event profiling information
        boost::chrono::milliseconds duration =
            future.get_event().duration<boost::chrono::milliseconds>();

        // print elapsed time in milliseconds
        std::cout << "time: " << duration.count() << " ms" << std::endl;

        return 0;
    }

### 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;