mxx is a C++/C++11 template library for MPI. The main goal of this library is to provide two things:

1. Simplified, efficient, and type-safe C++11 bindings to common MPI operations.
2. A collection of scalable, high-performance standard algorithms for parallel distributed memory architectures, such as sorting.

As such, mxx is targeting use in rapid C++ and MPI algorithm development, prototyping, and deployment.

• All functions are templated by type. All MPI_Datatype are deducted from the C++ type given to the function.
• Custom reduction operations as lambdas, std::function, functor, or function pointer.
• Send/Receive and Collective operations take size_t sized input and automatically handle sizes larger than INT_MAX.
• Plenty of convenience functions and overloads for common MPI operations with sane defaults (e.g., super easy collectives: std::vector<size_t> allsizes = mxx::allgather(local_size)).
• Automatic type mapping of all built-in (int, double, etc) and other C++ types such as std::tuple, std::pair, and std::array.
• Non-blocking operations return a mxx::future<T> object, similar to std::future.
• Google Test based MPI unit testing framework
• Parallel sorting with similar API than std::sort (mxx::sort)

• Parallel random number engines (for use with C++11 standard library distributions)
• More parallel (standard) algorithms
• Wrappers for non-blocking collectives
• (maybe) serialization/de-serialization of non contiguous data types
• macros for easy datatype creation and handling for custom/own structs and classes
• Implementing and tuning different sorting algorithms
• Communicator classes for different topologies
• mxx::env similar to boost::mpi::env for wrapping MPI_Init and MPI_Finalize
• Increase test coverage:

Currently mxx is just a small personal project at very early stages.

This example shows the main features of mxx's wrappers for MPI collective operations:

• MPI_Datatype deduction according to the template type
• Handling of message sizes larger than INT_MAX (everything is size_t enabled)
• Receive sizes do not have to be specified
• convenience functions for std::vector, both for sending and receiving

    // local numbers, can be different size on each process
    std::vector<size_t> local_numbers = ...;
    // allgather the local numbers, easy as pie:
    std::vector<size_t> all_numbers = mxx::allgatherv(local_numbers, MPI_COMM_WORLD);

The following example showcases the C++11 interface to reductions:

    #include <mxx/reduction.hpp>

    // ...
    // lets take some pairs and find the one with the max second element
    std::pair<int, double> v = ...;
    std::pair<int, double> min_pair = mxx::allreduce(v,
                           [](const std::pair<int, double>& x,
                              const std::pair<int, double>& y){
                               return x.second > y.second ? x : y;
                           });

What happens here, is that the C++ types are automatically matched to the appropriate MPI_Datatype (struct of MPI_INT and MPI_DOUBLE), then a custom reduction operator (MPI_Op) is created from the given lambda, and finally MPI_Allreduce called for the given parameters.

Consider a simple example, where you might want to sort tuples (int key,double x, double y) by key key in parallel using MPI. Doing so in pure C/MPI requires quite a lot of coding (~100 lines), debugging, and frustration. Thanks to mxx and C++11, this becomes as easy as:

    typedef std::tuple<int, double, double> tuple_type;
    std::vector<tuple_type> data(local_size);
    // define a comparator for the tuple
    auto cmp = [](const tuple_type& x, const tuple_type& y) {
                   return std::get<0>(x) < std::get<0>(y); }

    // fill the vector ...

    // call mxx::sort to do all the heavy lifting:
    mxx::sort(data.begin(), data.end(), cmp, MPI_COMM_WORLD);

In the background, mxx performs many things, including (but not limited to):

• mapping the std::tuple to a MPI type by creating the appropriate MPI datatype (i.e., MPI_Type_struct).
• distributing the data if not yet done so
• calling std::sort as a local base case, in case the communicator consists of a single processor, mxx::sort will fall-back to std::sort
• in case the data size exceeds the infamous MPI size limit of MAX_INT, mxx will not fail, but continue to work as expected
• redistributing the data so that it has the same distribution as given in the input to mxx::sort

To our knowledge, there are two noteworthy, similar open libraries available.

1. boost::mpi offers C++ bindings for a large number of MPI functions. As such it corresponds to our main goal 1. Major drawbacks of using boost::mpi are the unnecessary overhead of boost::serialization (especially in terms of memory overhead). boost::mpi also doesn't support large message sizes (> INT_MAX), and the custom reduction operator implementation is rather limited.
2. mpp offers low-overhead C++ bindings for MPI point-to-point communication primitives. As such, this solutions shows better performance than boost::mpi, but was never continued beyond point-to-point communication.

• Patrick Flick

Since this is a header only library, simply copy and paste the mxx folder into your project, and you'll be all set.

At this point, mxx requires a C++11 compatible version of gcc. Some functions still rely on gcc specific function calls. mxx currently works with MPI-2 and MPI-3. However, some collective operations and sorting will work on data sizes >= 2 GB only with MPI-3.

Not necessary. This is a header only library. There is nothing to compile.

The tests can be compiled using cmake:

mkdir build && cd build
cmake ../ && make

Running the tests (with however many processes you want).

mpirun -np 13 ./bin/test-all

Our code is licensed under the Apache License 2.0 (see LICENSE). The licensing does not apply to the ext folder, which contains external dependencies which are under their own licensing terms.