1. Code Tuning Strategies and Techniques CS480 – Software Engineering II Azusa Pacific University Dr. Sheldon X. Liang

2. 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

3. 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

4. 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

5. 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

6. 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(); }}

7. 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

8. 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

9. Iteration Multiple techniques may be necessary Cumulative effect of tuning

10. 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...

11. 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 Else ProcessError( inputCharacter ) End Select

12. Code-Tuning Techniques Compare Performance of Similar Logic Structures Test from case statement versus if-then-else logic. Languagecaseif-then- else Time Savings Performance Ratio C#0.2600.330-27%1:1 Java2.560.46082%6:1 Visual Basic 0.2601.00258%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.

13. 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; }

14. 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 – 1 commission( i ) = (i + 1) * revenue * baseCommission * discount Next incrementalCommission = revenue * baseCommission * discount cumulativeCommission = incrementalCommission For i = 0 to saleCount – 1 commission( i ) = cumulativeCommission cumulativeCommission = cumulativeCommission + incrementalCommission Next

15. 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

16. 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 ]; }

17. 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; }

18. 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 cache if ( ( sideA == cachedSideA ) && ( sideB == cachedSideB ) ) { return cachedHypotenuse; } // compute new hypotenuse and cache it cachedHypotenuse = Math.sqrt( ( sideA * sideA ) + ( sideB * sideB ) ); cachedSideA = sideA; cachedSideB = sideB; return cachedHypotenuse; }

19. 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 b not (a or b)

20. 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

21. 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

22. 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

23. 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.

24. 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.