My research and investigations

As a programming language, ML is quite powerful: it features both functional and procedural styles.

ML is born a functional language — indeed the very first implementation of ML is in Lisp, the grand-daddy of functional programming. Like Lisp, ML has a top-level read-evaluate-print loop, making it interactive. However, ML is not an interpretative language: the top-level interactive loop is actually running an incremental compiler. Most ML implementations will compile ML into some form of lambda expressions. Possible ways to deal with these lambda expressions are:

  1. Interpret the lambda expressions.
  2. Compile the lambda expressions into byte-codes of some abstract machine, and run the abstract machine.
  3. Convert the abstract machine byte-codes into native machine codes, to be run directly in real machines.

In pure functional programming, data are only transformed, never modified, hence no side-effects. This simplifies reasoning about program correctness. However, real-world applications need to interact with the world (e.g. response to mouse clicks) — which depends on side-effects. Procedural programming is largely based on data changes, the most explicit being the use of pointers in C/C++ to modify memory locations directly.

Intended for building practical applications (like theorem provers) ML provides procedural features such as assignments and references. As a result ML shares many goodies of mainstream programming languages, e.g. use of modules and separate compilation.

However, even with these many features, ML is not a bloated language. It has a well-thought-out design, just using typed lambda calculus. The evolution of ML is a story about how to implement functional languages efficiently, and how to achieve performance comparable to procedural languages.


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