My 1982 book Writing Efficient Programs was built around 27 rules for code tuning. That book is now out of print, so the rules are repeated here (with only a few small changes), together with examples of how they are used in this book.
Data Structure Augmentation. The time required for common operations on data can often be reduced by augmenting the structure with extra information or by changing the information within the structure so that it can be accessed more easily.
In Section 9.2, Wright wanted to find nearest neighbors (by angle) among a set of points on the surface of the globe represented by latitude and longitude, which involved expensive trigonometric operations. Appel augmented her data structure with the x, y and z coordinates, which allowed her to use Euclidean distances with much less compute time.
Store Precomputed Results. The cost of recomputing an expensive function can be reduced by computing the function only once and storing the results. Subsequent requests for the function are then handled by table lookup rather than by computing the function.
The cumulative arrays in Section 8.2 and Solution 8.11 replace a sequence of additions with two table lookups and a subtraction.
Solution 9.7 speeds up a program to count bits by looking up a byte or a word at a time.
Solution 10.6 replaces shifting and logical operations with a table lookup.
Caching. Data that is accessed most often should be the cheapest to access.
Section 9.1 describes how Van Wyk cached the most common size of node to avoid expensive calls to the system storage allocator. Solution 9.2 gives the details on one kind of node cache.
Column 13 caches nodes for lists, bins and binary search trees.
Caching can backfire and increase the run time of a program if locality is not present in the underlying data.
Lazy Evaluation. The strategy of never evaluating an item until it is needed avoids evaluations of unnecessary items.
Packing. Dense storage representations can decrease storage costs by increasing the time required to store and retrieve data.
The sparse array representations in Section 10.2 greatly reduce storage costs by slightly increasing the time to access the structures.
McIlroy’s dictionary for a spelling checker in Section 13.8 squeezes 75,000 English words into 52 kilobytes.
Kernighan’s arrays in Section 10.3 and the Heapsort in Section 14.4 both use overlaying to reduce data space by storing data items that are never simultaneously active in the same memory space.
Although packing sometimes trades time for space, the smaller representations are often also faster to process.
Interpreters. The space required to represent a program can often be decreased by the use of interpreters in which common sequences of operations are represented compactly.
Section 3.2 uses an interpreter for “form-letter programming” and Section 10.4 uses an interpreter for a simple graphics program.
Code Motion Out of Loops. Instead of performing a certain computation in each iteration of a loop, it is better to perform it only once, outside the loop.
Section 11.1 moves an assignment to the variable t out of the main loop of isort2.
Combining Tests. An efficient inner loop should contain as few tests as possible, and preferably only one. The programmer should therefore try to simulate some of the exit conditions of the loop by other exit conditions.
Sentinels are a common application of this rule: we place a sentinel at the boundary of a data structure to reduce the cost of testing whether our search has exhausted the structure. Section 9.2 uses a sentinel in sequentially searching an array. Column 13 uses sentinels to yield clean (and incidentally efficient) code for arrays, linked lists, bins and binary search trees. Solution 14.1 places a sentinel at one end of a heap.
Loop Unrolling. Unrolling a loop can remove the cost of modifying loop indices, and also help to avoid pipeline stalls, to reduce branches, and to increase instruction-level parallelism.
Unrolling a sequential search in Section 9.2 reduces its run time by about 50 percent, and unrolling a binary search in Section 9.3 reduces its run time by between 35 and 65 percent.
Transfer-Driven Loop Unrolling. If a large cost of an inner loop is devoted to trivial assignments, then those assignments can often be removed by repeating the code and changing the use of variables. Specifically, to remove the assignment i = j, the subsequent code must treat j as though it were i.
Unconditional Branch Removal. A fast loop should contain no unconditional branches. An unconditional branch at the end of a loop can be removed by “rotating” the loop to have a conditional branch at the bottom.
This operation is usually done by optimizing compilers.
Loop Fusion. If two nearby loops operate on the same set of elements, then combine their operational parts and use only one set of loop control operations.
Exploit Algebraic Identities. If the evaluation of a logical expression is costly, replace it by an algebraically equivalent expression that is cheaper to evaluate.
Short-Circuiting Monotone Functions. If we wish to test whether some monotone nondecreasing function of several variables is over a certain threshold, then we need not evaluate any of the variables once the threshold has been reached.
A more sophisticated application of this rule exits from a loop as soon as the purpose of the loop has been accomplished. The search loops in Columns 10, 13 and 15 all terminate once they find the desired element.
Reordering Tests. Logical tests should be arranged such that inexpensive and often successful tests precede expensive and rarely successful tests.
Solution 9.6 sketches a series of tests that might be reordered.
Precompute Logical Functions. A logical function over a small finite domain can be replaced by a lookup in a table that represents the domain.
Solution 9.6 describes how the Standard C library character classification functions can be implemented by table lookup.
Boolean Variable Elimination. We can remove boolean variables from a program by replacing the assignment to a boolean variable v by an if-else statement in which one branch represents the case that v is true and the other represents the case that v is false.
Collapsing Function Hierarchies. The run times of the elements of a set of functions that (nonrecursively) call themselves can often be reduced by rewriting functions in line and binding the passed variables.
Replacing the max function in Section 9.2 with a macro gives a speedup of almost a factor of two.
Writing the swap function inline in Section 11.1 gave a speedup of a factor of almost 3; writing the swap inline in Section 11.3 gave less of a speedup.
Exploit Common Cases. Functions should be organized to handle all cases correctly and common cases efficiently.
In Section 9.1, Van Wyk’s storage allocator handled all node sizes correctly, but handled the most common node size particularly efficiently.
In Section 6.1, Appel treated the expensive case of near objects with a special-purpose small time step, which allowed the rest of his program to use a more efficient large time step.
Coroutines. A multiple-pass algorithm can often be turned into a single-pass algorithm by use of coroutines.
The anagram program in Section 2.8 uses a pipeline, which can be implemented as a set of coroutines.
Transformations on Recursive Functions. The run time of recursive functions can often be reduced by applying the following transformations:
Rewrite recursion to iteration, as in the lists and binary search trees in Column 13. Convert the recursion to iteration by using an explicit program stack. (If a function contains only one recursive call to itself, then it is not necessary to store the return address on the stack.)
If the final action of a function is to call itself recursively, replace that call by a branch to its first statement; this is usually known as removing tail recursion. The code in Solution 11.9 is a candidate for this transformation. That branch can often be transformed into a loop. Compilers often perform this optimization.
It is often more efficient to solve small subproblems by use of an auxiliary procedure, rather than by recurring down to problems of size zero or one. The qsort4 function in Section 11.3 uses a value of cutoff near 50.
Parallelism. A program should be structured to exploit as much of the parallelism as possible in the underlying hardware.
Compile-Time Initialization. As many variables as possible should be initialized before program execution.
Exploit Algebraic Identities. If the evaluation of an expression is costly, replace it by an algebraically equivalent expression that is cheaper to evaluate.
In Section 9.2, Appel replaced expensive trigonometric operations with multiplications and additions, and also used monotonicity to remove an expensive square root operation.
Section 9.2 replaces an expensive C remainder operator % in an inner loop with a cheaper if statement.
We can often multiply or divide by powers of two by shifting left or right. Solution 13.9 replaces an arbitrary division used by bins with a shift. Solution 10.6 replaces a division by 10 with a shift by 4.
In Section 6.1, Appel exploited additional numeric accuracy in a data structure to replace 64-bit floating point numbers with faster 32-bit numbers.
Strength reduction on a loop that iterates through the elements of an array replaces a multiplication by an addition. Many compilers perform this optimization. This technique generalizes to a large class of incremental algorithms.
Common Subexpression Elimination. If the same expression is evaluated twice with none of its variables altered between evaluations, then the second evaluation can be avoided by storing the result of the first and using that in place of the second.
Modern compilers are usually good at eliminating common subexpressions that do not contain function calls.
Pairing Computation. If two similar expressions are frequently evaluated together, then we should make a new procedure that evaluates them as a pair.
In Section 13.1, our first pseudocode always uses member and insert functions in concert; the C++ code replaces those two functions with an insert that does nothing if its argument is already in the set.
Exploit Word Parallelism. Use the full data-path width of the underlying computer architecture to evaluate expensive expressions.
Problem 13.8 shows how bit vectors can operate on many bits at once by operating on chars or ints.