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

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; }

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.

Boost.Compute provides the `BOOST_COMPUTE_ADAPT_STRUCT`

macro which allows a C++ struct/class to be wrapped and used in OpenCL.

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));

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;

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);

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; }

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;