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Tips for Optimizing C/C++ Code

1. Remember Ahmdal’s Law: Speedup =

time

old

time

new

(1−func

cost

)+func

cost

/func

speedup

• Where func

cost

is the percentage of the program runtime used by the function func, and func

speedup

the factor by which you speedup the function.

• Thus, if you optimize the function T riangleIntersect(), which is 40% of the runtime, so that it runs

twice as fast, your program will run 25% faster (

(1−0.4)+0.4/2

0.8

= 1.25).

• This means infrequently used code (e.g., the scene loader) probably should be optimized little (if at all).

• This is often phrased as: “make the common case fast and the rare case correct.”

2. Code for correctness ﬁrst, then optimize!

• This does not mean write a fully functional ray tracer for 8 weeks, then optimize for 8 weeks!

• Perform optimizations on your ray tracer in multiple steps.

• Write for correctness, then if you know the function will be called frequently, perform obvious optimiza-

tions.

• Then proﬁle to ﬁnd bottlenecks, and remove the bottlenecks (by optimization or by improving the al-

gorithm). Often improving the algorithm drastically changes the bottleneck – perhaps to a function you

might not expect. This is a good reason to perform obvious optimizations on all functions you know will

be frequently used.

3. People I know who write very efﬁcient code say they spend at least twice as long optimizing code as they spend

writing code.

4. Jumps/branches are expensive. Minimize their use whenever possible.

• Function calls require two jumps, in addition to stack memory manipulation.

• Prefer iteration over recursion.

• Use inline functions for short functions to eliminate function overhead.

• Move loops inside function calls (e.g., change for(i=0;i<100;i++) DoSomething(); into DoSomething()

{ for(i=0;i<100;i++) { ... } } ).

• Long if...else if...else if...else if... chains require lots of jumps for cases near the end of the chain (in

addition to testing each condition). If possible, convert to a switch statement, which the compiler some-

times optimizes into a table lookup with a single jump. If a switch statement is not possible, put the most

common clauses at the beginning of the if chain.

5. Think about the order of array indices.

• Two and higher dimensional arrays are still stored in one dimensional memory. This means (for C/C++

arrays) array[i][j] and array[i][j+1] are adjacent to each other, whereas array[i][j] and array[i+1][j]

may be arbitrarily far apart.

• Accessing data in a more-or-less sequential fashion, as stored in physical memory, can dramatically speed

up your code (sometimes by an order of magnitude, or more)!

• When modern CPUs load data from main memory into processor cache, they fetch more than a single

value. Instead they fetch a block of memory containing the requested data and adjacent data (a cache

line). This means after array[i][j] is in the CPU cache, array[i][j+1] has a good chance of already being

in cache, whereas array[i+1][j] is likely to still be in main memory.

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Résumé du contenu

Page 1

Tips for Optimizing C/C++ Code1. Remember Ahmdal’s Law: Speedup =timeoldtimenew=1(1−funccost)+funccost/funcspeedup• Where funccostis the percentage of

Page 2

6. Think about instruction-level-parallelism.• Even though many applications still rely on single threaded execution, modern CPUs already have asigniﬁ

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• In other instances, lookup tables may be quite useful. For GPU programming, table lookups are oftenpreferred for complex functions.16. For most clas

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• Make sure all data structures are aligned to cache line boundaries. (If both your data structure and a cacheline is 128 bytes, you will still have p

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