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25. Code-Tuning Strategies
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Relative Time Consumed Operation Level 2 object dereference Each additional dereference Integer Operations Integer assignment (local) Integer assignment (inherited) Integer addition Integer subtraction Integer multiplication Integer division Floating Point Operations Floating-point assignment Floating-point addition Floating-point subtraction Floating-point multiplication Floating-point division Transcendental Functions Floating-point square root Floating-point sine Floating-point logarithm Floating-point e Arrays Access integer array with constant subscript Access integer array with variable subscript Access two-dimensional integer array with constant subscripts Access two-dimensional integer array with variable subscripts Access floating-point array with constant subscript i = a[ 5 ] i = a[ j ] i = a[ 3, 5 ] 1 1 1 1 1 1
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C++ 1 not measurable
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i=j i=j i=j+k i=j+k i=j*k i=j%k
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1 1 1 1 1 5
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1 1 1 1 1 1.5
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x=y x=y+z x=y-z x=y*z x=y/z
1 1 1 1 4
1 1 1 1 1
x = sqrt( y ) x = sin( y ) x = log( y ) x = exp( y )
15 25 25 50
4 20 20 20
i = a[ j, k ]
x = z[ 5 ]
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Relative Time Consumed Operation Access floating-point array with integer-variable subscript Access two-dimensional floating-point array with constant subscripts Access two-dimensional floating-point array with integer-variable subscripts Example x = z[ j ] C++ 1 Java 1
x = z[ 3, 5 ]
x = z[ j, k ]
Note: Measurements in this table are highly sensitive to local machine environment, compiler optimizations, and code generated by specific compilers. Measurements between C++ and Java are not directly comparable.
Relative performance of these operations has changed significantly since the first edition of Code Complete, so if you re approaching code tuning with 10-year-old ideas about performance, you might need to update your thinking. Most of the common operations are about the same price routine calls, assignments, integer arithmetic, and floating-point arithmetic are all roughly equal. Transcendental math functions are extremely expensive. Polymorphic routine calls are a bit more expensive than other kinds of routine calls. This table, or a similar one that you make, is the key that unlocks all the speed improvements described in 26, Code-Tuning Techniques. In every case, improving speed comes from replacing an expensive operation with a cheaper one. The next chapter provides examples of how to do so.
25.4 Measurement
Since small parts of a program usually consume a disproportionate share of the run time, measure your code to find the hot spots. Once you ve found the hot spots and optimized them, measure the code again to assess how much you ve improved it. Many aspects of performance are counterintuitive. The earlier case in this chapter, in which 10 lines of code were significantly faster and smaller than one line, is one example of the ways that code can surprise you. Experience doesn t help much with optimization either. A person s experience might have come from an old machine, language, or compiler and when any of
8 KEY POINT
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those things changes, all bets are off. You can never be sure about the effect of an optimization until you measure the effect. A few years ago I wrote a program that summed the elements in a matrix. The original code looked like the next example.
C++ Example of Straightforward Code to Sum the Elements in a Matrix
sum = 0; for ( row = 0; row < rowCount; row++ ) { for ( column = 0; column < columnCount; column++ ) { sum = sum + matrix[ row ][ column ]; } }
This code was straightforward, but performance of the matrix-summation routine was critical, and I knew that all the array accesses and loop tests had to be expensive. I knew from computer-science classes that every time the code accessed a two-dimensional array, it performed expensive multiplications and additions. For a 100-by-100 matrix, that totaled 10,000 multiplications and additions plus the loop overhead. By converting to pointer notation, I reasoned, I could increment a pointer and replace 10,000 expensive multiplications with 10,000 relatively cheap increment operations. I carefully converted the code to pointer notation and got this:
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