Low-Level C++ Programming Tutorial
POD data structures, array operations, x86 inline assembly, vectorization, and SIMD with x86 intrinsics.
Introduction ▼
Work through examples and then complete each exercise directly in Compiler Explorer. Keep optimizations enabled (`-O2`) and inspect generated behavior as you move from scalar code to SIMD.
Section 1: POD Data Structures ▼
Plain Old Data (POD) types are contiguous, trivial, and efficient for low-level memory access.
Exercise
Complete `subtract_vectors` and verify the return value is `12`.
struct Vector3 {
float x, y, z;
};
Vector3 subtract_vectors(Vector3 a, Vector3 b) {
Vector3 result;
result.x = a.x - b.x;
result.y = a.y - b.y;
result.z = a.z - b.z;
return result;
}Section 2: Array Operations ▼
Manual array loops enable predictable memory access and can be optimized aggressively by the compiler.
Example
Exercise
Complete `max_array` so the program returns `4`.
const int SIZE = 4;
float max_array(float arr[SIZE]) {
float max_val = arr[0];
for (int i = 1; i < SIZE; i++) {
if (arr[i] > max_val) {
max_val = arr[i];
}
}
return max_val;
}Section 3: x86 Intrinsics ▼
Inline assembly exposes direct x86 instructions for precise control in critical hot paths.
Example
Exercise
Fill in the missing assembly line so `subtract(5, 3)` returns `2`.
int subtract(int a, int b) {
int result;
__asm__ (
"movl %1, %%eax;"
"subl %2, %%eax;"
"movl %%eax, %0;"
: "=r"(result)
: "r"(a), "r"(b)
: "%eax"
);
return result;
}Section 4: Vectorization with Arrays ▼
Auto-vectorization relies on clean loops and explicit compiler hints like `#pragma omp simd`.
Example
Exercise
Add the vectorization pragma in `dot_product` so the program returns `30`.
const int SIZE = 4;
float dot_product(float a[SIZE], float b[SIZE]) {
float sum = 0.0;
#pragma omp simd
for (int i = 0; i < SIZE; i++) {
sum += a[i] * b[i];
}
return sum;
}Section 5: SIMD with x86 Intrinsics ▼
SIMD intrinsics (`<xmmintrin.h>`) map directly to vector instructions such as `_mm_add_ps` and `_mm_mul_ps`.
Example
Exercise
Complete `_mm_mul_ps` usage so the output sum is `30`.
#include <xmmintrin.h>
void multiply_arrays_simd(float* a, float* b, float* result, int n) {
for (int i = 0; i < n; i += 4) {
__m128 va = _mm_load_ps(&a[i]);
__m128 vb = _mm_load_ps(&b[i]);
__m128 vr = _mm_mul_ps(va, vb);
_mm_store_ps(&result[i], vr);
}
}