Code Tuning Strategies and Techniques

CS480 – Software Engineering IIAzusa Pacific UniversityDr. Sheldon X. Liang

Code Tuning Strategies and Techniques Overview Performance and Code Tuning Introduction to Code Tuning Common Sources of Inefficiency Measurement Iteration Code-Tuning Techniques Checklist & Summary

Performance and Code Tuning cont.

• Operating-system interactions• Inefficient operating system routines• Compiler generated system calls

• Code compilation• Good compilers optimize code speed

• Hardware• New hardware may be cheaper than optimizing code

• Code tuning (Lastly)• Practice of modifying correct code in ways that make it

run more efficiently

Introduction to Code Tuning

• More lines of code = less efficient - FALSE

• You should optimize as you go – FALSE• Make it work correctly first

• What do you want to tune for?• Code Size versus Speed

• Use a worker thread• The appearance of performance

Introduction to Code Tuning

• When to tune• Jackson's Rules of Optimization: Rule 1. Don't do it.

Rule 2 (for experts only). Don't do it yet—that is, not until you have a perfectly clear and unoptimized solution. —M. A. Jackson

• Use compiler optimization• Write clear code• Let the compiler to the optimizing

Common Sources of Inefficiency

• Input / Output Operations• In memory operation much faster than disk access• Organize and minimize I/O operations

• Paging• Operation that causes the operating system to swap

pages of memory is much slower than an operation that works on only one page of memory

for ( column = 0; column < MAX_COLUMNS; column++ ) { for ( row = 0; row < MAX_ROWS; row++ ) {

table[ row ][ column ] = BlankTableElement(); }} for ( row = 0; row < MAX_ROWS; row++ ) {

for ( column = 0; column < MAX_COLUMNS; column++ ) {table[ row ][ column ] = BlankTableElement(); }}

Common Sources of Inefficiency

• System calls• Calls to system routines are often expensive.

• Context Switch

• Possible Solutions• Write your own services.• Avoid going to the system.• Work with the system vendor to make the call faster.

• Interpreted languages • Interpreted languages exact significant performance

penalties• If performance matters, don’t use them

Measurement

• Measure your code to find the hot spots• You don’t know if or how much your improving if

you don’t measure

• Measurement needs to be precise• QueryPerformanceCounter – Windows• Only measure the code your tuning

Iteration

• Iteration• Multiple techniques may be necessary• Cumulative effect of tuning

Code-Tuning Techniques

• Logic• Stop Testing When You Know the Answer

negativeInputFound = false;for ( i = 0; i < count; i++ ) {

if ( input[ i ] < 0 ) {negativeInputFound = true; break;

}}

• Consider order of evaluation• if ( 5 < x ) and ( x < 10 ) then ...

Code-Tuning Techniques

• Order Tests by Frequency• Arrange tests so that the one that's fastest and

most likely to be true is performed first Select inputCharacter

Case "A" To "Z", "a" To "z“ProcessAlpha( inputCharacter )

Case " “ProcessSpace( inputCharacter )

Case ",", ".", ":", ";", "!", "?“ProcessPunctuation( inputCharacter )

Case "0" To "9“ProcessDigit( inputCharacter )

Case "+", "=" ProcessMathSymbol( inputCharacter )

Case ElseProcessError( inputCharacter )

End Select

Code-Tuning Techniques

• Compare Performance of Similar Logic Structures• Test from case statement versus if-then-else logic.

Language case if-then-else

Time Savings

Performance Ratio

C# 0.260 0.330 -27% 1:1

Java 2.56 0.460 82% 6:1

Visual Basic

0.260 1.00 258% 1:4

• This example illustrates the difficulty of performing any sort of "rule of thumb" to code tuning

• There is simply no reliable substitute for measuring results.

Code-Tuning Techniques

• Loops • Minimizing the Work Inside Loops

• One key to writing effective loops is to minimize the work done inside a loop

for ( i = 0; i < rateCount; i++ ) { netRate[ i ] = baseRate[ i ] * rates->discounts->factors->net;}

quantityDiscount = rates->discounts->factors->net; for ( i = 0; i < rateCount; i++ ) { netRate[ i ] = baseRate[ i ] * quantityDiscount;}

Code-Tuning Techniques

• Loops • Strength Reduction

• Reducing strength means replacing an expensive operation such as multiplication with a cheaper operation such as addition

For i = 0 to saleCount – 1commission( i ) = (i + 1) * revenue * baseCommission * discount

Next

incrementalCommission = revenue * baseCommission * discountcumulativeCommission = incrementalCommissionFor i = 0 to saleCount – 1

commission( i ) = cumulativeCommissioncumulativeCommission = cumulativeCommission +

incrementalCommissionNext

Code-Tuning Techniques

• Use the Fewest Array Dimensions Possible• Conventional wisdom maintains that multiple

dimensions on arrays are expensive. • If you can structure your data so that it's in a

one-dimensional array rather than a two-dimensional or three-dimensional array, you might be able to save some time

Code-Tuning Techniques

• Minimize Array References• Advantageous to minimize array accesses• The reference to discount[ discountType ] doesn't

change when discountLevel changes in the inner loop

for ( discountType = 0; discountType < typeCount; discountType++ ) {for ( discountLevel = 0; discountLevel < levelCount;

discountLevel++ ) {rate[ discountLevel ] = rate[ discountLevel ] *

discount[ discountType ];}

}

Code-Tuning Techniques

• Minimize Array References• Move discount [discountType] out of the inner loop so

that you'll have only one array access per execution of the outer loop

for ( discountType = 0; discountType < typeCount; discountType++ ) {thisDiscount = discount[ discountType ];for ( discountLevel = 0; discountLevel < levelCount;

discountLevel++ ) {rate[ discountLevel ] = rate[ discountLevel ] * thisDiscount;}

}

Code-Tuning Techniques• Use Caching

• Caching means saving a few values in such a way that you can retrieve the most commonly used values more easily than the less commonly used values

• You can cache the results of time-consuming computations too

public double Hypotenuse( double sideA, double sideB ) {// check to see if the triangle is already in the cacheif ( ( sideA == cachedSideA ) && ( sideB == cachedSideB ) ) { return cachedHypotenuse; }// compute new hypotenuse and cache itcachedHypotenuse = Math.sqrt( ( sideA * sideA ) + ( sideB *

sideB ) );cachedSideA = sideA;cachedSideB = sideB;return cachedHypotenuse;

}

Code-Tuning Techniques

• Expressions • Much of the work in a program is done inside

mathematical or logical expressions.• Complicated expressions tend to be expensive

• Exploit Algebraic Identities• Use algebraic identities to replace costly operations

with cheaper ones.• The following expressions are logically equivalent:

not a and not bnot (a or b)

Code-Tuning Techniques

• Initialize at Compile Time• If you're using a named constant or a magic

number in a routine call, that's a clue that you could pre-compute the number

unsigned int Log2( unsigned int x ){ return (unsigned int) ( log( x ) / log( 2 ) );}

• Replace log(2) with a constant

Code-Tuning Techniques

• Eliminate Common Subexpressions• If an expression is repeated several times, assign it

to a variable rather than re-computing in several places.

payment = loanAmount / (( 1.0 - Math.pow( 1.0 + ( interestRate / 12.0 ), -months ) ) / ( interestRate / 12.0 ) );

• interestRate / 12.0 could be a variable

Code-Tuning Techniques

• Routines• Good routine decomposition is best for well tuned

code• Small, well-defined routines save space

• Take the place of doing jobs separately in multiple places• You can re-factor code in one routine and thus improve

every routine that calls it• Small routines are relatively easy to rewrite in a low-level

language

Code-Tuning Techniques

• Routines• Rewrite Routines Inline

• Code executes “in-place” versus calling a routine• Less advantage with newer compilers and computers

• Recoding in a Low-Level Language• If you're coding in C++, the low-level language might be

assembler. • If you're coding in Python, the low-level language might

be C.

Summary of the Approach to Code Tuning 1. Develop the software by using well-designed code that's easy

to understand and modify.2. If performance is poor,

a. Save a working version of the code so that you can get back to the "last known good state."

b. Measure the system to find hot spots.c. Determine whether the weak performance comes from inadequate

design, data types, or algorithms and whether code tuning is appropriate. If code tuning isn't appropriate, go back to step 1.

d. Tune the bottleneck identified in step (c).e. Measure each improvement one at a time.f. If an improvement doesn't improve the code, revert to the code saved in

step (a). (Typically, more than half the attempted tunings will produce only a negligible improvement in performance or degrade performance.)

3. Repeat from step 2.