Auto Parallelizer

This is an automatic parallelization tool to translate serial C/C++ code to parallel code using OpenMP directives. It is mainly based on task parallelization and it also supports for parallelization of reduction operations.

Usage

Run the jar file and set the required arguments. Task parallelization is included by default and other options can be set as follow:

-i, -input <arg>      Input file
-o, -output <arg>     Output file
-r, -reduction        Enable the for parallelization of reduction operations 
-h, -help             Print this message
-v, -verbose          Print all the data found inside the source code

Example

Serial code

#include <iostream>

using namespace std;

int main () {
    int n = 10, sum1 = 0, sum2 = 0;
    for (int i = 0; i < n; ++i)
        sum1 += i;
    for (int i = 0; i < n; ++i)
        sum2 += 2 * i;
    cout << sum1 + sum2 << '\n';
}

Run the parallelizer

java -jar AutoParallelizer.jar -reduction -input input.cpp

Parallelized code

#include <iostream>
using namespace std;
int main () {
    int n = 10, sum1 = 0, sum2 = 0;
    #pragma omp parallel sections
    {
        #pragma omp section
        {
            #pragma omp parallel for reduction(+:sum2)
            for (int i = 0; i < n; ++i)
                sum2 += 2 * i;
        }
        #pragma omp section
        {
            #pragma omp parallel for reduction(+:sum1)
            for (int i = 0; i < n; ++i)
                sum1 += i;
        }
    }
    cout << sum1 + sum2 << '\n';
}

Considerations

As you may know, parallel programming is very different from sequential programming and to achieve the best performance, hand parallelization is probably the best option. Thus, this software does not support some parallelization techniques.
The C++ grammar is limited, it may not support some features of C++11 and C++14. This is a beta version, the parallelization process is not guaranteed for some aspects of the language such as pointers and reference variables, these aspects require a further implementation.

Take into account these considerations before parallelizing with our software.