When it comes to numerical analysis and performance, Fortran has long been considered a powerhouse in the field. However, with the rise of modern programming languages like C++, many developers wonder if Fortran still holds any advantages in this domain. In this article, we will delve into the intricacies of Fortran and C++ to understand their strengths and weaknesses in numerical analysis.
What is Fortran?
Fortran, short for Formula Translation, is a programming language that was specifically designed for scientific and engineering computations. It has been around since the 1950s and has evolved over the years to become a highly efficient language for numerical analysis. Fortran’s strict aliasing semantics and its focus on numerical performance make it an attractive choice for computationally intensive tasks.
The Power of Fortran in Numerical Analysis
One of the key advantages of Fortran lies in its ability to work with arrays of data efficiently. Algorithms that heavily rely on CPU operations with arrays can often benefit from a Fortran implementation. In fact, in a programming languages shootout, Fortran consistently ranks as the fastest language for numerical benchmarks. Out of the 15 benchmarks, Fortran outperforms other languages in terms of speed on four of them, including spectral norm, fasta, mandelbrot, and pidigits.
It is important to note that Fortran’s strengths lie in heavily numerical tasks. For example, in the k-nucleotide benchmark, which focuses on sophisticated data structures and string processing, Fortran may not perform as well as other languages. However, on average, Fortran code is closest to the fastest implementation for each benchmark, indicating its superiority in numerical analysis.
Considering Performance and Investment
When deciding whether to use Fortran for numerical analysis, there are a few factors to consider. Firstly, you need to evaluate the criticality of speed for your specific function. If performance is of utmost importance and the function can benefit significantly from a Fortran implementation, it may be worth your time to re-implement it in Fortran.
Secondly, you should assess the overall importance of performance in your project and whether investing time in learning Fortran will pay off. Fortran’s extensive history and optimization for numerical computations make it a valuable skill for those working in scientific and engineering fields.
Lastly, it is worth exploring the possibility of using existing libraries like ATLAS instead of writing the code from scratch. Leveraging well-established libraries can save time and effort while still benefiting from Fortran’s performance advantages.
Availability of Sample Code and Reference Implementations
Another aspect to consider is the availability of sample code and reference implementations. Fortran’s long-standing presence in the scientific community means that there is a wealth of numerical code available for download and reference. This can be a significant advantage when starting a new project or seeking solutions to specific numerical problems.
However, it is important to note that not all code available may be of high quality. It is essential to carefully evaluate and select the most reliable and efficient implementations to ensure optimal performance.
While C++ has made significant strides in recent years and offers a wide range of features and libraries, Fortran’s historical dominance in numerical analysis cannot be overlooked. By carefully considering the trade-offs and evaluating your project’s needs, you can make an informed decision on whether Fortran is the right choice for your numerical analysis tasks.
