SIMD heavily supports fast vector search, which is one of the reasons I find vector databases so interesting.

As someone with a background in 3D mathematics, I know that the geometry behind high-dimensional vector search is the same. Dot products, cosine similarity, and Euclidean distance aren’t abstract—they’re the same tools I used for transforms, projections, and movement in 3D space, primarily in C/++.

What makes modern vector search performant at scale is hardware-level parallelism.

Vector databases rely heavily on SIMD (Single Instruction, Multiple Data) to quickly compute vector distances and similarities. Instead of calculating one multiplication at a time in a loop:

for (int i = 0; i < 128; i++) { sum += a[i] * b[i]; }

SIMD instructions (e.g., AVX2 or AVX-512) compute multiple multiplications and additions per instruction, often 8 or 16 at a time, using wide registers on modern x86 CPUs.

This is a primary reason these systems can do real-time vector search across millions or even billions of embeddings.

Most vector DBs use: • Hand-written SIMD kernels with intrinsics like _mm256_fmadd_ps • Compiler vectorisation using flags like -O3 –mavx2 • CPU feature detection to adapt at runtime • GPU acceleration where needed, via CUDA • ARM NEON support on Apple Silicon or Graviton

Understanding these systems means understanding how high-dimensional math maps to low-level compute. That’s why I’m fascinated by this, it blends deep math with raw systems performance.

If you care about performance, it’s worth understanding the fundamentals of SIMD, x86 architecture, and even a bit of assembly. These are the tools that turn abstract math into production-scale infrastructure.