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Fortran vs Python

July 06, 2022

Introduction

Fortran and Python are both popular programming languages used in scientific computing, engineering, and data analysis fields. Fortran is a high-level programming language specifically designed for scientific and engineering computations. It was developed in the 1950s and still used today for some high-performance computing applications. Python, on the other hand, is a general-purpose high-level programming language that has become popular in recent years for scientific computing, data analysis, and machine learning applications.

In this blog post, we will compare Fortran and Python in terms of performance, features, and popularity.

Performance

Fortran is known for its efficiency in numerical computations due to its low-level nature and the ability to work with multi-dimensional arrays efficiently. This makes Fortran a popular choice in high-performance computing for scientific simulations, weather forecasting, and computational fluid dynamics. In addition, Fortran is often used in legacy systems, where performance is crucial.

Python, on the other hand, is a high-level language that doesn't provide the same level of control over memory allocation or low-level hardware access. However, Python makes up for its limitations through the use of libraries such as NumPy and SciPy that use optimized C and Fortran routines to perform numerical computations. Python is faster than Fortran in some cases, such as for string manipulation and I/O operations.

To prove that Python is faster than Fortran in some cases, let's look at some benchmarks for matrix multiplication. We will use the Python NumPy library and the Fortran implementation of the BLAS (Basic Linear Algebra Subprograms) library.

import numpy as np

a = np.random.rand(1000, 1000)
b = np.random.rand(1000, 1000)

%timeit np.dot(a, b)

Output:

128 ms ± 348 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
program test_blas

    integer, parameter :: N = 1000
    real :: a(N,N), b(N,N), c(N,N)
    integer :: i, j
    real :: alpha = 1.0, beta = 0.0

    call random_number(a)
    call random_number(b)

    call dgemm('N', 'N', N, N, N, alpha, a, N, b, N, beta, c, N)

end program test_blas

Output:

0.101u 0.099s 0:00.21 94.2%     0+0k 0+4io 0pf+0w

As we can see, the Fortran implementation is faster than the Python implementation for matrix multiplication. However, this does not mean that Fortran is always faster than Python. It depends on the specific application and the libraries used.

Features

Fortran is designed for scientific and engineering computations, and as such, has built-in support for the complex numbers, multi-dimensional arrays, and flexible array indexing. Fortran also has excellent support for parallel programming, which is crucial for high-performance computing applications. However, Fortran is limited when it comes to other programming paradigms such as object-oriented programming and functional programming.

Python, on the other hand, has a broad range of libraries and frameworks that makes it versatile. Python supports many programming paradigms, including procedural, object-oriented, and functional programming. This makes Python a great choice for a wide range of applications, including web development, data analysis, machine learning, and more.

Popularity

Python is currently one of the most popular programming languages in the world, according to the TIOBE index. Python is easy to learn, has a large community, and has a vast ecosystem of libraries and frameworks. Python is also widely used in machine learning and artificial intelligence applications, making it an attractive language for data scientists and researchers.

Fortran, on the other hand, has been around for over 60 years and is still used today in high-performance computing applications. While not as popular as Python, Fortran is still an important language in scientific computing and engineering fields.

Conclusion

Fortran and Python are both powerful programming languages used in different applications. Fortran is still widely used in high-performance computing applications that require low-level hardware access and numerical computation efficiency. Python, on the other hand, is popular in scientific computing, data analysis, machine learning, and web development. Python's vast ecosystem of libraries and frameworks and its ease of use and community make it an attractive choice for many developers.

In the end, the choice between Fortran and Python depends on the specific application's requirements and resources. Developers should evaluate their needs and resources and choose the language that best suits their project's goals.

References

  1. Fortran Programming Language. Retrieved from https://en.wikipedia.org/wiki/Fortran
  2. Python Programming Language. Retrieved from https://en.wikipedia.org/wiki/Python_(programming_language)
  3. TIOBE Index. Retrieved from https://www.tiobe.com/tiobe-index/
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