Summary - Performance

Combine profiling, cache and move semantics to write an optimized copy function.

Summary - Performance

In this group you saw the performance tools: profiling, cache, vectorization, inline and move. All speed up the program; but only measurement gives correct use.

Now you will combine them in an inline + vectorizable loop example. You will combine inline int doubled(int x) with a cache-friendly loop; this shows how the performance tools work together.

Inline + vectorizable loop

Calling an inline function inside a loop lets the compiler do both inlining and vectorization. Example: inline int doubled(int x) { return x * 2; } called in a loop lets the compiler spread the body into the loop and process with SIMD.

Example: int a[3] = {1, 2, 3}; int s = 0; for (int i = 0; i < 3; ++i) s += a[i]; std::cout << s + doubled(5);. Here s=6, doubled(5)=10, total 16. This small example shows how inline and loops combine.

Exercise Combine inline + loop

Write inline int doubled(int x) with body return x * 2;. In main define int a[3] = {1, 2, 3};, int s = 0;; write for (int i = 0; i < 3; ++i) s += a[i];; then write std::cout << s + doubled(5);.

Show example solution

#include <iostream>

inline int doubled(int x) {
    return x * 2;
}

int main() {
    int a[3] = {1, 2, 3};
    int s = 0;
    for (int i = 0; i < 3; ++i) s += a[i];
    std::cout << s + doubled(5);
    return 0;
}

Test yourself

Which access pattern is cache-friendly?
```
for (int i = 0; i < n; ++i) sum += data[i];
```
- Random-index access
- Sequential-index access
- Only even-index access
- Strided access
Show answer

Sequential-index access — Sequential access gives spatial locality.

Complete the inline function

inline int doubled(int x) { return x * ____; }

Show answer

inline int doubled(int x) { return x * 2; }

Match each tool to its trait

profiling

Bottleneck measure
cache

Speed via locality
vectorization

SIMD parallelism
move

Resource transfer

Next lesson: What is embedded?

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