Fortran explained

Fortran: The Pioneering Language Powering AI/ML and Data Science

Table of contents

Fortran, short for Formula Translation, is a high-level programming language that was developed in the 1950s and has played a significant role in the history of computing. It was primarily designed for scientific and Engineering computations, making it an ideal choice for AI/ML and data science applications. In this article, we will delve into the intricacies of Fortran, exploring its origins, use cases, relevance in the industry, and career aspects.

Origins and History of Fortran

Fortran was first developed by a team of IBM programmers led by John W. Backus in the 1950s. The team aimed to create a language that could efficiently handle complex scientific and numerical computations. Fortran I, released in 1957, was the first high-level programming language that allowed programmers to write code using English-like statements.

Over the years, Fortran has undergone several revisions, with each version introducing new features and improvements. Fortran II, released in 1958, added subroutines and functions, making the language more modular. Fortran IV, released in 1962, introduced character data types and logical operations. Fortran 77, released in 1977, standardized the language and added additional features like structured programming constructs.

The latest major revision of Fortran is Fortran 2018, which was released in November 2018. Fortran 2018 introduced several modern features like coarrays for parallel programming, improved support for object-oriented programming, and enhanced interoperability with C.

Features and Use Cases

Fortran's design and features make it well-suited for AI/ML and data science applications. Here are some key features that make Fortran relevant in these domains:

1. Numerical Computing Power

Fortran is renowned for its numerical computing capabilities. It provides a rich set of built-in mathematical functions and extensive support for complex numbers, arrays, and matrices. Fortran's efficient handling of numerical operations makes it highly effective for AI/ML algorithms that involve heavy computations, such as matrix multiplication, Linear algebra, and statistical analysis.

2. Performance and Efficiency

Fortran's focus on performance and efficiency has made it a popular choice for computationally intensive tasks. The language provides low-level control over memory management and array operations, allowing programmers to optimize their code for maximum performance. Fortran's ability to generate highly optimized machine code enables it to execute calculations faster than many other programming languages.

3. Legacy Code Integration

Fortran has a long history in scientific and Engineering domains, resulting in a vast amount of legacy code written in the language. Many legacy codes, such as numerical libraries and simulation software, are written in Fortran. As a result, Fortran remains relevant in the industry as it allows researchers and engineers to integrate their existing Fortran code with modern AI/ML and data science workflows.

4. Interoperability with Other Languages

Fortran provides seamless interoperability with other programming languages. It supports interoperability with C, allowing for easy integration with existing C/C++ codebases. This interoperability is crucial in AI/ML and data science, as it enables the use of popular libraries and frameworks written in other languages, such as Python or R.

5. Parallel Computing Support

Fortran has extensive support for parallel programming, making it suitable for large-scale AI/ML and data science applications. Fortran's coarray feature, introduced in Fortran 2008, simplifies the development of parallel programs by providing a high-level programming model for distributed memory systems. Parallelization techniques can significantly speed up computations in AI/ML algorithms, especially when dealing with large datasets or complex simulations.

Relevance in AI/ML and Data Science

Fortran continues to be relevant in the AI/ML and data science domains due to its performance, numerical computing capabilities, and legacy code integration. While newer languages like Python and R have gained popularity in these fields, Fortran remains a powerful tool for certain tasks. Here are some specific areas where Fortran finds relevance:

1. Numerical Libraries

Fortran is widely used in the development of numerical libraries and frameworks that are crucial in AI/ML and data science. Libraries like LAPACK (Linear algebra Package) and BLAS (Basic Linear Algebra Subprograms) are written in Fortran and provide efficient implementations of common linear algebra operations. These libraries are widely utilized by AI/ML practitioners for tasks like matrix factorization, eigenvalue computation, and solving systems of linear equations.

2. Computational Physics and Engineering

Fortran is extensively used in computational Physics and engineering domains. Many simulations and modeling software, such as weather forecasting models, fluid dynamics simulations, and structural analysis tools, are written in Fortran. These applications often require high-performance computing and complex numerical computations, where Fortran excels.

3. Legacy Code Maintenance and Modernization

The scientific and engineering communities have a substantial amount of legacy Fortran code that continues to be used and maintained. As AI/ML and data science advance, there is a need to integrate these legacy codes with modern workflows. Fortran's interoperability with other languages, especially C, allows researchers to leverage their existing Fortran code while incorporating modern AI/ML techniques.

4. High-Performance Computing (HPC)

Fortran's performance and efficiency make it a preferred choice for high-performance computing (HPC) applications. HPC systems are often used in AI/ML and data science for tasks like training large neural networks, running simulations, and processing massive datasets. Fortran's ability to generate highly optimized machine code and its support for parallel programming make it well-suited for such demanding tasks.

Career Aspects and Best Practices

As AI/ML and data science evolve, it is essential for professionals in these fields to have a diverse skill set that includes knowledge of multiple programming languages. While newer languages like Python are more popular for AI/ML and data science, having proficiency in Fortran can provide a competitive edge in certain domains. Here are a few career aspects and best practices related to Fortran in AI/ML and data science:

1. Leveraging Existing Fortran Code

Professionals working in AI/ML and data science may encounter situations where they need to integrate or optimize existing Fortran code. Understanding the basics of Fortran, such as its syntax, data types, and array operations, can be beneficial in such scenarios. Knowledge of interoperability techniques between Fortran and other languages, like C, is particularly valuable.

2. Contributing to Numerical Libraries

Contributing to or utilizing existing Fortran-based numerical libraries can be a valuable career move in AI/ML and data science. Understanding the inner workings of libraries like LAPACK or BLAS and being able to optimize their usage can significantly enhance performance in numerical computations. Contributing bug fixes or new features to these libraries can also help establish expertise and gain recognition in the field.

3. Embracing Modern Fortran Standards

As Fortran evolves, it is essential to stay up-to-date with the latest standards and features. Fortran 2018 introduced several modern features like coarrays for parallel programming and improved support for object-oriented programming. Familiarizing oneself with these advancements and incorporating them into AI/ML or data science workflows can lead to more efficient and maintainable code.

4. Collaborating with Domain Experts

Fortran is widely used in scientific and engineering domains, where AI/ML and data science find extensive applications. Collaborating with domain experts, such as physicists, engineers, or researchers, who work with Fortran-based code can provide valuable insights into the challenges and requirements of these domains. Such collaborations can lead to interdisciplinary research opportunities and open doors to new career prospects.

Conclusion

Fortran, with its rich history and powerful numerical computing capabilities, continues to be relevant in AI/ML and data science. Its performance, efficiency, and compatibility with existing codebases make it a valuable tool in certain domains. While newer languages have gained popularity, Fortran's legacy and ongoing developments ensure its continued relevance. By understanding Fortran's features, leveraging existing code, and collaborating with domain experts, professionals in AI/ML and data science can harness the power of Fortran to solve complex problems and contribute to cutting-edge research.

References: - Fortran (Wikipedia) - Fortran Standards Documents - Fortran Programming Language - IBM Archives