Algorithms in a Nutshell

Choosing a Programming Language | 53

Patterns and

Domains

pass allocates 26 bytes of memory, this program terminated after nearly 15.9

gigabytes ofallocated memory on a machine with 16 gigabytes of available

memory. To properly release memory once it is no longer needed (as determined

by the programmer), the program mustfreethe memory, which returns the allo-

cated memory to the Heap for future use.

Choosing a Programming Language

Throughout this book we use a variety of programming languages to illustrate

the algorithms. No single language is the right one to use in all circumstances.

Too often, a specific programming language is used simply because it was used

on a similar project. Since you are interested in algorithms, it is likely that you

also want to ensure that your implementations run as fast as possible. This level

of fine-tuning or optimization is beyond the scope of this book, though we

describe several instances where carefully designed code optimizations result in

impressive performance benefits. Choosing a language often depends on a

number of factors:

Garbage collection versus manual memory allocation

In the previous section we described low-level details about the way infor-

mation is stored as a C program executes. Using the standard memory

allocation packages available, most C programmers are used to allocating

memory as needed, and freeing it when done. An alternative approach is to

use a language such as Java or Scheme that provides built-in garbage collec-

tion to manage allocated memory. Garbage collection technology is

increasingly efficient, and there are existing packages available to enable

even C programs to integrate garbage collection with the default memory

allocation schemes.

Bytecode interpretation versus compiled code

The common perception is that compiled code will outperform interpreted

code every time. In Java, for example, the Java compiler produces byte code

that is interpreted and executed by the JVM. You should seriously consider

whether using a language such as Java will improve the understanding of the

code as it is written, even though there may be a runtime penalty in using that

code.

Dynamic versus static typing

Statically typed languages enforce the rules of types to detect errors during

compilation, which should improve productivity because errors are caught

immediately rather than at runtime. For strongly typed functional languages,

such as ML, there is a common perception in the functional language

community that most defects can be prevented by the proper application of

the type system. Dynamic typed languages are often interpreted and variable

values are known only at runtime, and therefore cannot be checked stati-

cally. Many scripting languages offer dynamic typing.

Algorithms in a Nutshell
Algorithms in a Nutshell By Gary Pollice, George T. Heineman, Stanley Selkow ISBN:
9780596516246 Publisher: O'Reilly Media, Inc.

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