Classification of Programming Languages Based on Machine Abstraction

Posted: October 31, 2010 in Programming
Tags: , , , , , , , , , , , ,

There are wide choice of programming languages catering to a diverse application domain. This post is an attempt at classifying them based on how much the language/implementation tries to abstract the machine details from the programmer.

We can broadly classify languages as

  • Assembly language (x86 assembler AT&T syntax or Intel syntax)
  • Compiled (C)
  • Compiled to byte code of VM and compiled just in time (Java2)
  • Compiled to byte code of VM and interpreted (Python)
  • String interpreted (TCL)
Assembly Languages

Such languages need the least amount of work to make them executable. Assemblers like the x86 assembly language is closely tied to the machine code. It is basically a one is to one mapping between machine instruction to a human readable string called mnemonics. Assemblers also make it easy to assign labels to address locations to ease programming effort. Modern assemblers also support macros which can make programming repetitive code easier. There is little scope for optimization or higher order data structures in assembly languages, since they just mirror the target machine code. Such languages are the least portable since they are always tied to a target machine architecture. Few examples of assembly languages would be the x86 assembly language, Motorola 6800 assembly etc.

Compiled Languages

Such languages are a step taken for portability. They abstract the underlying machine instructions and give higher order constructs for arithmetic operations, branching, looping and basic data types. They are compiled in to target machine code directly. Once compiled, the binary can be natively run in the target machine. Arguably they are slower than assembler, since the actual machine code is generated by a compiler and will not be as optimized as hand coded assembler. But in practice, for any modern processor with multiple cores and pipelining, the compiler tends to generate more optimized code. Such languages can have support for higher order data structures like lists, maps etc either natively or via standard libraries. C, C++, Pascal etc are some examples of compiled languages.

Compiled to Byte Code of a VM (JIT compiled)

These languages are another step towards portability. First they are compiled to byte code of a virtual machine. The virtual machine executes the byte code by compiling it in to native machine code Just In Time (JIT). Practically these are slower than compiled languages since there is another layer of abstraction. But they are more portable than compiled languages. The same compiled byte code can be run on any platform that supports the VM, while for compiled languages, different binaries are required for different platforms. Typically they support a full range of higher order data structures. Examples include Java2, Ruby core 1.9.

Compiled to Byte Code of a VM (Interpreted)

The first step of these languages is the same as the above. They are compiled to byte code of a virtual machine. The virtual machine itself executes the byte code by interpreting it. They are generally slower than JIT implementations. They have same portability as JIT implementations. Performance also depends on the optimization effort gone in to the VM implementation. For example Python2 (byte code interpreted) is faster than Ruby core 1.9 (JIT) while Java2 (JIT) is way faster than compiled Lisp (SBCL). Examples include Python, Ruby.

String Interpreted

Such languages interpret the source code string directly. Because of this, these are usually the slowest of the lot. Consider this statement – a = 100 + 2. The 100 and 2 are strings and instead of doing the addition 100 + 2 natively, the interpreter knows how to add integers as strings. The interpreter is easier to implement than byte code compilation but performance is the least. TCL, JavaScript are examples of string interpreted languages.

We can see a pattern emerging. From assembly to string interpretation, the language/implementation abstracts machine details more and more from the programmer. As a result performance keeps decreasing while portability keeps increasing. Beyond a point, performance decreases without any increase in portability, but implementation becomes easier. Also more abstracted languages usually provide higher order data structures and automatic memory management for free.

Also the level of abstraction really depends on the implementation rather than the language itself. For example, Python2 is both byte code interpreted (official CPython) and JIT (PyPy). The implementation can vary not only in the VM. For example Common Lisp has a compiled implementation (SBCL), compiled to C/C++ or byte code interpreted implementation (ECL).

About these ads
Comments

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s