gko::Operation Class Reference

Operations can be used to define functionalities whose implementations differ among devices. More...

#include <ginkgo/core/base/executor.hpp>

Public Member Functions
virtual void	run (std::shared_ptr< const OmpExecutor >) const
virtual void	run (std::shared_ptr< const CudaExecutor >) const
virtual void	run (std::shared_ptr< const ReferenceExecutor > executor) const
virtual const char *	get_name () const noexcept
	Returns the operation's name. More...

Detailed Description

Operations can be used to define functionalities whose implementations differ among devices.

This is done by extending the Operation class and implementing the overloads of the Operation::run() method for all Executor types. When invoking the Executor::run() method with the Operation as input, the library will select the Operation::run() overload corresponding to the dynamic type of the Executor instance.

Consider an overload of operator<< for Executors, which prints some basic device information (e.g. device type and id) of the Executor to a C++ stream:

std::ostream& operator<<(std::ostream &os, const gko::Executor &exec);

One possible implementation would be to use RTTI to find the dynamic type of the Executor, However, using the Operation feature of Ginkgo, there is a more elegant approach which utilizes polymorphism. The first step is to define an Operation that will print the desired information for each Executor type.

class DeviceInfoPrinter : public gko::Operation {
public:
    explicit DeviceInfoPrinter(std::ostream &os) : os_(os) {}

    void run(const gko::OmpExecutor *) const override { os_ << "OMP"; }

    void run(const gko::CudaExecutor *exec) const override
    { os_ << "CUDA(" << exec->get_device_id() << ")"; }

    // This is optional, if not overloaded, defaults to OmpExecutor overload
    void run(const gko::ReferenceExecutor *) const override
    { os_ << "Reference CPU"; }

private:
    std::ostream &os_;
};

Using DeviceInfoPrinter, the implementation of operator<< is as simple as calling the run() method of the executor.

std::ostream& operator<<(std::ostream &os, const gko::Executor &exec)
{
    DeviceInfoPrinter printer(os);
    exec.run(printer);
    return os;
}

Now it is possible to write the following code:

auto omp = gko::OmpExecutor::create();
std::cout << *omp << std::endl
          << *gko::CudaExecutor::create(0, omp) << std::endl
          << *gko::ReferenceExecutor::create() << std::endl;

which produces the expected output:

OMP
CUDA(0)
Reference CPU

One might feel that this code is too complicated for such a simple task. Luckily, there is an overload of the Executor::run() method, which is designed to facilitate writing simple operations like this one. The method takes two closures as input: one which is run for OMP, and the other one for CUDA executors. Using this method, there is no need to implement an Operation subclass:

std::ostream& operator<<(std::ostream &os, const gko::Executor &exec)
{
    exec.run(
        [&]() { os << "OMP"; },  // OMP closure
        [&]() { os << "CUDA("    // CUDA closure
                   << static_cast<gko::CudaExecutor&>(exec)
                        .get_device_id()
                   << ")"; });
    return os;
}

Using this approach, however, it is impossible to distinguish between a OmpExecutor and ReferenceExecutor, as both of them call the OMP closure.

Member Function Documentation

get_name()

virtual const char* gko::Operation::get_name ( ) const

virtualnoexcept

Returns the operation's name.

Returns

the operation's name

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The documentation for this class was generated from the following file:

• ginkgo/core/base/executor.hpp (f1a4eb68)