Code Tuning and Optimizations

When and How to Improve Code Performance?

Ivaylo BratoevTelerik

Corporationwww.telerik.com

Actual vs Perceived Performance

Example: “Vista's file copy performance is noticeably worse than Windows XP” – false: Vista uses algorithm that perform

better in most cases.

Explorer waits 12 seconds before providing a copy duration estimate, which certainly provides no sense of smooth progress.

The copy dialog is not dismissed until the write-behind thread has committed the data to disk, which means the copy is slowest at the end.

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Is performance really a priority

Performance improvements can reduce readability and complexity

“premature optimization is the root of all evil” - Donald Knuth

“More computing sins are committed in the name of efficiency (without necessarily achieving it) than for any other single reason - including blind stupidity.” - W.A. Wulf

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How to Improve Performance

Program requirements Software cost vs performance

System design performance-oriented architecture

with resource goals for individual subsystems, features, and classes.

Class and method design data types and algorithms

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How to Improve Performance

External Interactions Operating System

External devices – printers, network, internet

Code Compilation / Code Execution Compiler optimizations

Hardware very often the cheapest way

Code Tuning5

Introduction to Code Tuning

Modifying correct code to make it run more efficiently

Not the most effective/cheapest way to improve performance

20% of a program’s methods consume 80% of its execution time.

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Code Tuning Myths Reducing the lines of code in a high-level language improves the speed or size of the resulting machine code – false!

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for i = 1 to 10 a[ i ] = iend for

a[ 1 ] = 1a[ 2 ] = 2a[ 3 ] = 3a[ 4 ] = 4a[ 5 ] = 5a[ 6 ] = 6a[ 7 ] = 7a[ 8 ] = 8a[ 9 ] = 9a[ 10 ] = 10

vs

Code Tuning Myths A fast program is just as important as a correct one – false!

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Code Tuning Myths Certain operations are probably faster or smaller than others – false! Always measure performance!

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Code Tuning Myths You should optimize as you go – false! It is hard to identify bottlenecks

before a program is completely working

Focus on optimization detracts from other program objectives

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When to tune Use a high-quality design.

Make the program right.

Make it modular and easily modifiable

When it’s complete and correct, check the performance.

Consider compiler optimizations Measure Write clean code that’s easy to understand and modify. 11

Measurement Measure to find bottlenecks Measurements need to be precise Measurements need to be repeatable

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Optimize in iterations Measure improvement after each optimization

If optimization does not improve performance – revert it

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Code Tuning Techniques Stop Testing When You Know the Answer

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if ( 5 < x ) and ( y < 10 ) then ...

if ( 5 < x ) then if ( y < 10 ) then ...

negativeInputFound = False;for ( i = 0; i < iCount; i++ ) { if ( input[ i ] < 0 ) { negativeInputFound = True; }} add a

break

Code Tuning Techniques Order Tests by Frequency

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Select char Case "+", "=" ProcessMathSymbol(char) Case "0" To "9" ProcessDigit(char) Case ",", ".", "!", "?" ProcessPunctuation(char) Case " " ProcessSpace(char) Case "A" To "Z", "a" To "z“ ProcessAlpha(char) Case Else ProcessError(char)End Select

Select char Case "A" To "Z", "a" To "z“ ProcessAlpha(char) Case " " ProcessSpace(char) Case ",", ".", "!", "?" ProcessPunctuation(char) Case "0" To "9" ProcessDigit(char) Case "+", "=" ProcessMathSymbol(char) Case Else ProcessError(char)End Select

Code Tuning Techniques Unswitching loops

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for ( i = 0; i < count; i++ ) { if ( sumType == SUMTYPE_NET ) { netSum = netSum + amount[ i ]; } else { grossSum = grossSum + amount[ i ]; }}

if ( sumType == SUMTYPE_NET ) { for ( i = 0; i < count; i++ ) { netSum = netSum + amount[ i ]; }}else { for ( i = 0; i < count; i++ ) { grossSum = grossSum + amount[ i ]; } }

Code Tuning Techniques Minimizing the work inside loops

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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 Initialize at Compile Time

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const double Log2 = 0.69314718055994529;

Code Tuning Techniques Use Lazy Evaluation

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public int getSize() { if(size == null) { size = the_series.size(); } return size; }

Code Tuning Techniques

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Use caching

double Hypotenuse(double sideA, double sideB) { return Math.sqrt((sideA*sideA) + (sideB*sideB));}

Code Tuning Techniques Use caching (continued)

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private double cachedHypotenuse = 0;private double cachedSideA = 0;private double cachedSideB = 0;

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;}

Code Tuning and Optimizations

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