This library implements fast, open, multi-methods for C++17. It is strongly inspired by the papers by Peter Pirkelbauer, Yuriy Solodkyy, and Bjarne Stroustrup.
If you are familiar with the concept of open multi-methods, or if you prefer to learn by reading code, go directly to the synopsis. The documentation is here
You have a matrix math library. It deals with all sort of matrices: dense, diagonal, tri-diagonal, etc. Each matrix subtype has a corresponding class in a hierarchy rooted in Matrix.
Now you would like to render Matrix objects as JSON strings. The representation will vary depending on the exact type of the object; for example, if a matrix is a DiagonalMatrix, you only need to store the diagonal - the other elements are all zeroes.
This is an example of a "cross-cutting concern". How do you do it?
It turns out that OOP doesn't offer a good solution to this.
You can stick a pure virtual to_json
function in the Matrix
base class and
override it in the subclasses. It is an easy solution but it has severe
drawbacks. It requires you to change the Matrix class and its subclasses, and
recompile the library. And now all the applications that use it will contain
the to_json
functions even if they don't need them, because of the way
virtual functions are implemented.
Or you may resort on a "type switch": have the application test for each category and generate the JSON accordingly. This is tedious, error prone and, above all, not extensible. Adding a new matrix subclass requires updating all the type switches. The Visitor pattern also suffers from this flaw.
Wouldn't it be nice if you could add behavior to existing types, just as easily and unintrusively as you can extend existing class hierarchies via derivation? What if you could solve the so-called Expression Problem:
existing behaviors += new types
existing types += new behaviors
This is exactly what Open Methods are all about: solving the Expression Problem.
Let's look at an example.
// -----------------------------------------------------------------------------
// library code
struct matrix {
virtual ~matrix() {
}
// ...
};
struct dense_matrix : matrix { /* ... */
};
struct diagonal_matrix : matrix { /* ... */
};
// -----------------------------------------------------------------------------
// application code
#include <memory>
#include <yorel/yomm2/keywords.hpp>
register_classes(matrix, dense_matrix, diagonal_matrix);
declare_method(std::string, to_json, (virtual_<const matrix&>));
define_method(std::string, to_json, (const dense_matrix& m)) {
return "json for dense matrix...";
}
define_method(std::string, to_json, (const diagonal_matrix& m)) {
return "json for diagonal matrix...";
}
int main() {
yorel::yomm2::update();
const matrix& a = dense_matrix();
const matrix& b = diagonal_matrix();
std::cout << to_json(a) << "\n"; // json for dense matrix
std::cout << to_json(b) << "\n"; // json for diagonal matrix
return 0;
}
<yorel/yomm2/keywords.hpp>
is the library's main entry point. It declares a
set of macros, and injects a single name, virtual_
, in the global
namespace. The purpose of the header is to make it look as if open methods
are part of the language.
register_classes
informs the library of the existence of the classes, and
their inheritance relationships. Any class that can appear in a method call
needs to be registered, even if it is not directly referenced by a method.
declare_method
declares an open method called to_json
, which takes one
virtual argument of type const matrix&
and returns a std::string. The
virtual_<>
decorator specifies that the argument must be taken into account
to select the appropriate specialization. In essence, this is the same thing
as having a virtual std::string to_json() const
inside class Matrix -
except that the virtual function lives outside of any classes, and you can
add as many as you want without changing the classes. NOTE: DO NOT specify
argument names, i.e. virtual_<const matrix&> arg
is not permitted.
define_method
defines two implementations for the to_json
method: one for
dense matrices, and one for diagonal matrices.
yorel::yomm2::update()
creates the dispatch tables; it must be called
before any method is called, and after dynamically loading and unloading
shared libraries.
The example can be compiled (from the root of the repository) with:
clang++- -I include -std=c++17 tutorials/README.cpp -o example
Methods can have more than one virtual argument. This is handy in certain situations, for example to implement binary operations on matrices:
// -----------------------------------------------------------------------------
// matrix * matrix
declare_method(
std::shared_ptr<const matrix>,
times, (virtual_<const matrix&>, virtual_<const matrix&>));
// catch-all matrix * matrix -> dense_matrix
define_method(
std::shared_ptr<const matrix>,
times, (const matrix& a, const matrix& b)) {
return std::make_shared<dense_matrix>();
}
// diagonal_matrix * diagonal_matrix -> diagonal_matrix
define_method(
std::shared_ptr<const matrix>,
times, (const diagonal_matrix& a, const diagonal_matrix& b)) {
return std::make_shared<diagonal_matrix>();
}
Open methods are almost as fast as ordinary virtual member functions once you turn on optimization (-O2). With both clang and gcc, dispatching a call to a method with one virtual argument takes 15-30% more time than calling the equivalent virtual member function (unless the call goes through a virtual base, which requires a dynamic cast). It does not involve branching or looping, only a few memory reads (which the CPU can be parallelize), a multiplication, a bit shift, a final memory read, then an indirect call. If the body of the method does any amount of work, the difference is unnoticeable.
virtual_ptr
, a fat
pointer class, can be used to make method dispatch even faster - three
instructions and two memory reads.
Examples are available on Compiler Explorer.
YOMM2 is available on both major package managers. This is the easiest way of integrating it in your project, along with its dependencies. See the vcpkg example and the Conan2 example.
YOMM2 can also be built and installed from the sources, using straight cmake
.
First clone the repository:
git clone https://github.com/jll63/yomm2.git
Run cmake:
cmake -S yomm2 -B build.yomm2
cmake --build build.yomm2
If you want to run the tests, specify it when running cmake
:
cmake -S yomm2 -B build.yomm2 -DYOMM2_ENABLE_TESTS=1
cmake --build build.yomm2
ctest --test-dir build.yomm2
YOMM2 uses the following Boost libraries:
- Mp11, Preprocessor, DynamicBitset: included by YOMM2 headers
- Test: only used to run the test suite
If you want to run the benchmarks (and in this case you really want a release build):
cmake -S yomm2 -B build.yomm2 -DYOMM2_ENABLE_TESTS=1 -DYOMM2_ENABLE_BENCHMARKS=1 -DCMAKE_BUILD_TYPE=Release
./build.yomm2/tests/benchmarks
The benchmarks use the Google benchmark library.
If you like YOMM2, and you want to install it, either system-wide:
sudo cmake --install build.yomm2
...or to a specific directory:
DESTDIR=/path/to/my/libs cmake --install build.yomm2
This will install the headers and a CMake package configuration. By default, YOMM2 is installed as a headers only library. The examples can be compiled like this (after installation):
clang++ -std=c++17 -O3 examples/synopsis.cpp -o synopsis
Or directly from the repository (i.e. without installing):
clang++ -std=c++17 -O3 -Iinclude examples/synopsis.cpp -o synopsis
The YOMM2 runtime - responsible for building the dispatch tables - adds ~75K to the image, or ~64K after stripping.
The runtime can also be built and installed as a shared library, by adding
-DYOMM2_SHARED=1 to the cmake
command line.
A CMake package configuration is also installed. If the install location is in
CMAKE_PREFIX_PATH
, you can use find_package(YOMM2)
to locate YOMM2, then
target_link_libraries(<your_target> YOMM2::yomm2)
to add the necessary include
paths and the library. See this example.
Make sure to add the install location to CMAKE_PREFIX_PATH
so that you can use
find_package(YOMM2)
from your including project. For linking, the use
target_link_library(<your_target> YOMM2::yomm2)
. This will automatically add
the necessary include directories, so this should be all you need to do to link
to yomm2.
The documentation is here. Since version 1.3.0, some of the internals are documented, which make it possible to use the library without using macros - see the API tutorial.
YOMM2 has experimental support for writing templatized methods and definitions
The library comes with a series of examples:
-
The Asteroids example used in Wikipedia's article on Multiple Dispatch
-
friendship: an example with namespaces, method containers and friend declarations
I presented the library at CppCon 2018. Here are the video recording and the slides.
YOMM2 has been stable (in the sense of being backward-compatible) for many years, but it is still evolving. Here are the items on which I intend to work in the future. No promises, no time table.
- Dispatch on
std::any
. - Static offsets (i.e. set at compile time).
- Static linking of dispatch data.
- Minimal perfect hash tables as an option.
- Multi-threaded hash search.
- Get closer to Stroustrup et al's papers (version 2.0):
- use compatible return types for disambiguation
- move support for
std::shared_ptr
andunique_ptr
to an optional header
If you have ideas, comments, suggestions...get in touch! If you use YOMM2, I would appreciate it if you take the time to send me a description of your use case(s), and links to the project(s), if they are publicly available.