Have you ever wondered how scientists can simulate complex phenomena like quantum mechanics, climate change or fusion energy? The secret lies in a pioneering programming language called Fortran that remains vital for pushing the boundaries of knowledge through computing.
In this guide, I‘ll explore Fortran‘s history, capabilities and why it persists as the choice for specialized technical computing:
How Fortran Originated to Advance Science
Back in the 1950s, formulas and equations that modeled real-world behavior were largely solved by hand or with calculator machines. Computers showed promise for automating sophisticated mathematics, but programming these early systems required writing long strings of absolute machine code instructions.
Seeing this challenge, a small team at IBM led by John Backus started work in 1954 on a radical idea – a programming language tailored for science. They called this new language "Formula Translation", or Fortran for short.
The innovation behind Fortran was allowing scientists themselves to directly write down formulas in a format familiar to them. This abstracted away low-level details, enabling concentration on mathematical expressions instead of implementation.
After intensive refinement of Fortran‘s compiler over 3 years to guaranteed performance on par with assembly language, Fortran officially released in 1957. Initial skepticism soon turned into widespread adoption as the coding efficiency gains were undeniable. By 1963, over 40 Fortran compilers were available for every major computer system.
This was only the start for Fortran. Through ongoing standards updates like Fortran 77 and Fortran 90 adding modern features, Fortran remains the choice for anyone needing to maximize calculation throughput. Next we‘ll see how Fortran achieves its trademark speed…
Why Fortran Excels at Number-Crunching
What makes Fortran so well-suited to fields relying on numerical computing like physics, engineering and statistics?
1. Innate efficiency – Modern Fortran compilers are extremely optimized, transforming higher-level code into tight machine instructions tuned to rival hand-written assembly.
2. Math built-in – Fortran has native support for vectors/matrices, complex numbers, random generation and specialized math functions. No reinventing wheels!
3. Legacy optimization – Codebases in science domains accumulate decades of performance tweaking. Fortran 2018 protects these investments.
4. Parallelism targeted – Fortran adds parallel computing syntax like coarrays to leverage multi-core and massively parallel hardware.
The combination of these attributes allows Fortran programmers to maximize utilization of processing cycles – yielding the reliable speed expected from $100k+ supercomputer systems.
Let‘s look at some real-world examples…
Fortran Supercharges Advanced Research Projects
Across many specialty research computers costing over $50 million, Fortran can be found working behind the scenes enabling scientists to push envelopes.
| Project | How Fortran Helps |
|---|---|
| Simulating nuclear fusion | Physics researchers leverage Fortran and the Cray XC50 system to pursue clean renewable energy |
| Hurricane forecasting | NOAA relies on Fortran data analytics and the Cray XC40 supercomputer‘s 700 teraflop speed to predict storms and save lives through earlier warning |
| Charting spacecraft trajectories | NASA scientists use Fortran‘s computational power running on the Pleiades machine to calculate intricate spacecraft coordiates through the solar system |
| Detecting new particles | Large Hadron Collider experiments process trillions of datapoints with Fortran code to reveal insights into the fundamental nature of matter |
Replacing Fortran is simply not an option when measurement systems and mathematical models have years of calibrated refinement baked in. Performance is paramount.
Interested to join teams furthering discovery? Learning Fortran is within reach…
Hitting the Ground Running with Modern Fortran
The latest Fortran 2018 standard delivers many improvements without sacrificing that signature number-crunching speed. Some starting points:
1. Use intuitive syntax – Take advantage of free-form coding with simple logic flow familiar from languages like Python.
2. Manipulate matrices – Dive into Fortran‘s purpose-built multi-dimensional arrays and operators.
3. Connect libraries – Interoperate with battle-tested C interfaces to incorporate robust functionality.
4. Debug effortlessly – Build right inside integrated environments like GNU Fortran shielding distracting details.
5. Distributed computing – Experiment with coarray syntax as a pathway to massive cluster parallelism.
With ample online references and communities to learn from, new engineers can leverage Fortran‘s strengths. Let‘s explore the future landscape for these essential skills…
The Exascale Era Ahead
Looking ahead, a major milestone called exascale computing nears deployment, referring to mind-bending speeds exceeding 1 quintillion calculations per second. That‘s over 50x faster than today‘s top petascale supercomputers!
Unlocking this level of number-crunching bandwidth opens possibilities around machine learning applications, personalized medicine discoveries and much more. However, it also poses unique ultra-parallelism design constraints.
As the leading platform for scalable high performance computing, you can bet Fortran will be pivotal in overcoming barriers to tap into the coming exaflop frontier. Advancements already in Fortran 2018 like coarrays hint at what‘s possible.
If you found this glimpse into Fortran‘s pedigree compelling, don‘t hesitate researching further! Our world increasingly depends on simulations of the real-world – making skills to harness computation toward discovery impactful.
Let me know what you build!
