Member-only story
SIMD: Supercharging C++ with Hardware Optimization
This article is open to everyone, non-members can access it via this link
If you’ve ever tried optimizing high-performance C++ code, you’ve probably come across SIMD (Single Instruction, Multiple Data). It’s a key technique that allows your CPU to process multiple data points in parallel, dramatically speeding up tasks like numerical computations, graphics processing, and even financial modelling.
SIMD is also a common topic in interviews, especially in high-throughput engineering fields like fintech. If you’re working with real-time market data, risk modeling, or large-scale analytics, understanding SIMD can help you write low-latency, high-performance code.
What’s SIMD and Why Should You Care?
Imagine you have two large arrays and need to add them element by element. A naïve approach would process them sequentially:
- Add the first pair of numbers
- Add the second pair
- Add the third pair
With SIMD, the CPU can load multiple elements at once and perform the addition in parallel using a single instruction. This means fewer CPU cycles and much better performance.
For industries like fintech, where processing vast amounts of market data in real time is critical, SIMD helps reduce latency and improve throughput significantly.
How Modern CPUs Support SIMD
Most modern processors come with built-in SIMD capabilities, exposed through vector registers and specialized instruction sets:
- x86: MMX, SSE, AVX, AVX-512
- ARM: NEON
- PowerPC: AltiVec
For example, AVX (Advanced Vector Extensions) on x86 allows operations on 256-bit registers, meaning you can process eight 32-bit floats or four 64-bit doubles at once.
If you’re not using SIMD, you’re not fully utilizing your CPU’s potential.
Why SIMD is Useful
- Performance Boost — It’s much faster than scalar operations, especially for large datasets
- Energy Efficiency — Fewer instructions executed means less…
