Tori Bowman

CSSE 375, Rose-Hulman

October 22, 2007

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Code Tuning(Chapters 25-26 of

Code Complete)

Outline

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State of 375This

Code Tuning StrategiesCode Tuning Techniques

State of 375

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TonightHW 5 due 11:55 pm

Tomorrow: part lecture/part work timeThursday:

Week one progress reportFriday:

In class work time

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Code Tuning Strategies

Overview

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Code-tuning is one method of improving a program’s performance through various techniques

Performance is loosely related to speedWhen you work on speed, you’re not working

on other quality attributes

Historical context

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1960s:Resources severely limitedNothing more important than efficiency

1970s:Computing improvesRealizing how much it hurt readability and

maintainability1980s:

Microcomputers brought efficiency issues backWaned in throughout the 1990s

2000s:Memory in embedded issues becomes the

efficiency concern

Efficiency viewpoints

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Program requirementsProgram designClass and routine designOperating-system interactionsCode compilationHardwareCode tuning

Program requirements

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Barry Boehm’s example from TRWInitial requirement: sub-second response

timeHighly complex designEstimate cost: $100 million

Further analysis:User’s happy with four-second response time

90% of the timeReduced system cost by ~$70 million

Make sure you’re solving a problem that needs to be solved

Program design

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Set overall system goalThen set subsystems, features, classes

Helps to identify troublesome subsystemsMaking the objectives explicit improves the

likelihood they’ll be achieved

Code Tuning

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Not the most effectiveNot the easiestNot always the cheapest

So…. why do it?Personal satisfaction?Rite of passage?

Pareto Principle

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Also known as the 80/20 rule80 percent of the result is achieved by 20

percent of the effort

“An Empirical Study of Fortran Programs” by Donald Knuth< 4% of a program accounts for more than

50% of it’s run-time

How does this relate to code tuning?

Let’s consider…

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Reducing the lines of code in a high-level language improves speed or the size of the resulting machine code

Which is faster?

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

end for

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

Results…

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Languagefor-Loop

TimeStraight-code time

Time Savings

Performance Ratio

Visual Basic 8.47 3.16 63% 2.5:1

Java 12.6 3.23 74% 4:1

Common sources of inefficiencies

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I/O OperationsPagingSystem CallsInterpreted languagesErrors

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Code Tuning Techniques

CotD

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Logic

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Stop testing when you know the answerShort circuit in conditionals and loopsStandard with C++ and Java

Order tests by frequencyCompare performance of similar logic

structuresSome languages case is faster than if-then-else

Substitute table lookups for complicated expressions

Loops - unswitching

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Make decisions outside of the loop when possibleie. Putting the loop inside the conditionalGood for ~20% time savings

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 ]; }}

More loops

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JammingCombine loops that operate over the same

elementsUnrolling

Reduce the amount of loop housekeepingMinimize work inside of the loop

Assign complicated pointer expressions to a well-named variable

More loops cont.

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Sentinel valuesSimplifying compound tests

Nested loops:Put busiest loop on the inside

Strength reductionReplace expensive operations (multiplication)

with cheaper operations (addition)

Data transformations

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Use integers rather than floating-point numbers

Use the fewest array dimensions possibleMinimize array referencesUse supplementary indexesUse caching

Expressions

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Exploit algebraic identitiesnot a and not b vs. not (a or b)

Strength reductionInitialize at compile time

More in the book…

Read the book for…

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RoutinesRecoding in a low-level languageChecklistsAdditional resources

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Questions?