ceph/src/boost/libs/compute/README.md

   1 # Boost.Compute #
   2
   3 [![Build Status](https://travis-ci.org/boostorg/compute.svg?branch=master)](https://travis-ci.org/boostorg/compute)
   4 [![Build status](https://ci.appveyor.com/api/projects/status/4s2nvfc97m7w23oi/branch/master?svg=true)](https://ci.appveyor.com/project/jszuppe/compute/branch/master)
   5 [![Coverage Status](https://coveralls.io/repos/boostorg/compute/badge.svg?branch=master)](https://coveralls.io/r/boostorg/compute)
   6 [![Gitter](https://badges.gitter.im/boostorg/compute.svg)](https://gitter.im/boostorg/compute?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
   7
   8 Boost.Compute is a GPU/parallel-computing library for C++ based on OpenCL.
   9
  10 The core library is a thin C++ wrapper over the OpenCL API and provides
  11 access to compute devices, contexts, command queues and memory buffers.
  12
  13 On top of the core library is a generic, STL-like interface providing common
  14 algorithms (e.g. `transform()`, `accumulate()`, `sort()`) along with common
  15 containers (e.g. `vector<T>`, `flat_set<T>`). It also features a number of
  16 extensions including parallel-computing algorithms (e.g. `exclusive_scan()`,
  17 `scatter()`, `reduce()`) and a number of fancy iterators (e.g.
  18 `transform_iterator<>`, `permutation_iterator<>`, `zip_iterator<>`).
  19
  20 The full documentation is available at http://boostorg.github.io/compute/.
  21
  22 ## Example ##
  23
  24 The following example shows how to sort a vector of floats on the GPU:
  25
  26 ```c++
  27 #include <vector>
  28 #include <algorithm>
  29 #include <boost/compute.hpp>
  30
  31 namespace compute = boost::compute;
  32
  33 int main()
  34 {
  35     // get the default compute device
  36     compute::device gpu = compute::system::default_device();
  37
  38     // create a compute context and command queue
  39     compute::context ctx(gpu);
  40     compute::command_queue queue(ctx, gpu);
  41
  42     // generate random numbers on the host
  43     std::vector<float> host_vector(1000000);
  44     std::generate(host_vector.begin(), host_vector.end(), rand);
  45
  46     // create vector on the device
  47     compute::vector<float> device_vector(1000000, ctx);
  48
  49     // copy data to the device
  50     compute::copy(
  51         host_vector.begin(), host_vector.end(), device_vector.begin(), queue
  52     );
  53
  54     // sort data on the device
  55     compute::sort(
  56         device_vector.begin(), device_vector.end(), queue
  57     );
  58
  59     // copy data back to the host
  60     compute::copy(
  61         device_vector.begin(), device_vector.end(), host_vector.begin(), queue
  62     );
  63
  64     return 0;
  65 }
  66 ```
  67
  68 Boost.Compute is a header-only library, so no linking is required. The example
  69 above can be compiled with:
  70
  71 `g++ -I/path/to/compute/include sort.cpp -lOpenCL`
  72
  73 More examples can be found in the [tutorial](
  74 http://boostorg.github.io/compute/boost_compute/tutorial.html) and under the
  75 [examples](https://github.com/boostorg/compute/tree/master/example) directory.
  76
  77 ## Support ##
  78 Questions about the library (both usage and development) can be posted to the
  79 [mailing list](https://groups.google.com/forum/#!forum/boost-compute).
  80
  81 Bugs and feature requests can be reported through the [issue tracker](
  82 https://github.com/boostorg/compute/issues?state=open).
  83
  84 Also feel free to send me an email with any problems, questions, or feedback.
  85
  86 ## Help Wanted ##
  87 The Boost.Compute project is currently looking for additional developers with
  88 interest in parallel computing.
  89
  90 Please send an email to Kyle Lutz (kyle.r.lutz@gmail.com) for more information.