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Bulk Build Status

The bulk-synchronous parallel (BSP) paradigm is an effective framework for parallel programs. Bulk is a modern interface for writing BSP programs in C++. Modern programming language features allow for the implementation of safe and generic parallel algorithms for shared-memory, distributed-memory, and hybrid systems. Our interface should appeal to BSP programmers who want to write fast, safe, clear, and portable parallel programs.

About BSP

In the bulk-synchronous parallel (BSP) programming model, communication between processors (or nodes, or cores) does not happen asynchronously. Instead, all communication is staged and resolved at fixed synchronization points. These synchronizations delimit so-called supersteps. This way of structuring parallel programs has a number of advantages, at the cost of limiting the application’s freedom to overlap communication and computation.

  • The resulting programs are structured, easy to understand and maintain, and their performance and correctness can be reasoned about.
  • Data races are eliminated almost by construction, because of simple rules which can be enforced at runtime.
  • Scalability is straightforward to obtain. Programs are written in a SPMD fashion.
  • There are only two types of communication mechanisms, message passing and named communication (through distributed variables). This makes BSP based libraries economic: you can accomplish a lot with few building blocks.
  • It has a gentle learning curve. It is easy to write correct BSP programs, while it is notoriously hard to write correct asynchronous parallel programs.

Code examples

Hello world!

bulk::thread::environment env;
env.spawn(env.available_processors(), [](auto& world) {
    auto s = world.rank();
    auto p = world.active_processors();

    world.log("Hello world from processor %d / %d!", s, p);

Distributed variables are the bread and butter of communication in Bulk.

auto a = bulk::var<int>(world);
a(world.next_rank()) = s;
// ... a is now updated

auto b = a(world.next_rank()).get();
// ... b.value() is now available

Coarrays are convenient distributed arrays.

auto xs = bulk::coarray<int>(world, 10);
xs(world.next_rank())[3] = s;

Message passing can be used for more flexible communication.

auto q = bulk::queue<int, float>(world);
for (int t = 0; t < p; ++t) {
    q(t).send(s, 3.1415f);  // send (s, pi) to processor t

// messages are now available in q
for (auto [tag, content] : q) {
    world.log("%d got sent %d, %f\n", s, tag, content);


Bulk should work across all major Linux distributions. It requires an up-to-date compiler that supports C++20, e.g. GCC >= 11.0, or Clang >= 12.0.


Bulk supports a number of different backends, allowing the programs to run in parallel using:

  • thread for multi-core systems using standard C++ <thread> threading support
  • mpi for distributed environments using MPI

There is also a special legacy backend available for the Epiphany coprocessor, which can be found in the epiphany branch. This branch has a modified version of Bulk to support portability between MPI, <thread> and the Epiphany coprocessor. See backends/epiphany/ for more details.


The examples in the examples directory work for every backend. To build them, do the following. The backends (e.g. thread, mpi) are built optionally, just remove or add the option if you do not require them.

mkdir build
cd build
cmake ..
make thread mpi

The examples will be compiled in the bin/{backend} directory, prepended with the backend name, i.e. to run the hello example with the thread backend:


Developing on top of Bulk

The easiest way to get started using Bulk is to download the source code from GitHub. If you use Bulk in a project we suggest to add Bulk as a submodule:

git submodule add ext/bulk
git submodule update --init

If you use CMake for your project, adding Bulk as a dependency is straightforward. For this, you can use the bulk and bulk_[backend] targets. For example, if your CMake target is called your_program and it uses Bulk with the thread backend, you can use the following:

target_link_libraries(your_program bulk_thread)


Bulk is released under the MIT license, see

Please Cite Us

If you have used Bulk for a scientific publication, we would appreciate citations to the following paper:

Buurlage JW., Bannink T., Bisseling R.H. (2018) Bulk: A Modern C++ Interface for Bulk-Synchronous Parallel Programs. In: Aldinucci M., Padovani L., Torquati M. (eds) Euro-Par 2018: Parallel Processing. Euro-Par 2018. Lecture Notes in Computer Science, vol 11014. Springer, Cham

Applications using Bulk

Article Code
A projection-based partitioning method for distributed tomographic reconstruction. SIAM PP20. DOI


Bulk is developed at Centrum Wiskunde & Informatica (CWI) in Amsterdam by:

  • Jan-Willem Buurlage (@jwbuurlage)
  • Tom Bannink (@tombana)

Also thanks to:

  • Rob Bisseling
  • Sarita de Berg (@SdeBerg)


We welcome contributions! Please submit pull requests against the develop branch. For each PR:

  • Describe the change in
  • New authors may add their name to the ‘thanks to’ section in
  • Format the code using clang-format, with the configuration found in the root of this project

If you have any issues, questions, or remarks, then please open an issue on GitHub.