We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil.
-- Donald Knuth credited to Charles Anthony Richard Hoare.Robert Pike's Rules of Programming
Rule 1. You can't tell where a program is going to spend its time. Bottlenecks occur in surprising places, so don't try to second guess and put in a speed hack until you've proven that's where the bottleneck is.
Rule 2. Measure. Don't tune for speed until you've measured, and even then don't unless one part of the code overwhelms the rest.
Rule 3. Fancy algorithms are slow when n is small, and n is usually small. Fancy algorithms have big constants. Until you know that n is frequently going to be big, don't get fancy. (Even if n does get big, use Rule 2 first.) For example, binary trees are always faster than splay trees for workaday problems.
Rule 4. Fancy algorithms are buggier than simple ones, and they're much harder to implement. Use simple algorithms as well as simple data structures.
The following data structures are a complete list for almost all practical programs:
array
linked list
hash table
binary tree
Of course, you must also be prepared to collect these into compound data structures. For instance, a symbol table might be implemented as a hash table containing linked lists of arrays of characters.
Rule 5. Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming. (See The Mythical Man-Month: Essays on Software Engineering by F. P. Brooks, page 102.)
Rule 6. There is no Rule 6.
Representation is the Essence of Programming
Frederick Phillips Brooks Jr.
Beyond craftmanship lies invention, and it is here that lean, spare, fast programs are born. Almost always these are the result of strategic breakthrough rather than tactical cleverness. Sometimes the strategic breakthrough will be a new algorithm, such as the Cooley-Tukey Fast Fourier Transform or the substitution of an n log n sort for an n2 set of comparisons.
Much more often, strategic breakthrough will come from redoing the representation of the data or tables. This is where the heart of your program lies. Show me your flowcharts and conceal your tables, and I shall be continued to be mystified. Show me your tables, and I won’t usually need your flowcharts; they’ll be obvious.
It is easy to multiply examples of the power of representations. I recall a young man undertaking to build an elaborate console interpreter for an IBM 650. He ended up packing it onto an incredibly small amount of space by building an interpreter for the interpreter, recognizing that human interactions are slow and infrequent, but space was dear. Digitek’s elegant little Fortran compiler uses a very dense, specialized representation for the compiler code itself, so that external storage is not needed. That time lost in decoding this representation is gained back tenfold by avoiding input-output. (The exercises at the end of Chapter 6 in Brooks and Iverson, “Automatic Data Processing” include a collection of such examples, as do many of Knuth’s exercises.)
The programmer at wit’s end for lack of space can often do best by disentangling himself from his code, rearing back, and contemplating his data. Representation is the essence of programming
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