Low-Level Optimization with Zig

Summary

2025-06-07 # Optimizations with Zig "Beware of the Turing tar-pit in which everything is possibile, but nothing of interest is easy." - Alan Perlis 1982 What is of interest to you? Many things, I am certain. One such topic that I am constantly intrigued by is program optimization. Whether you are looking to compute the largest fibonacci number in one second, or creating the fastest financial transaction database ever written, or even just rewriting something in rust, you likely know how rewarding optimization can be. Optimization separates the weak from the fast. Optimization isn't a thing of the past. Advances in technology shape the form of our programs, but don't negate the need to optimize. Well optimized programs save money, enable higher-tier scaling opportunities, and preserve system simplicity. Would you rather spend thousands to run shoddy code on autoscaling cloud infrastructure, or write better code, letting you use a handful of mid-tier servers at reduced latency and cost? In this article I aim to explain the concept of low-level optimization, and why Zig is particularly well suited for it. If you enjoy what you read, please consider supporting me :) Trust the compiler? Some people would say "trust the compiler, it knows best." It sure does, for most low-level situations! Optimizing compilers have come a long ways. Increased system resources and advances in IR transformations have enabled compiler backends like LLVM to deliver impressive results. Compilers are complicated beasts. The best optimizing backends will still generate sub-par code in some cases. In fact, even state-of-art compilers will break language specifications (Clang assumes that all loops without side effects will terminate). It is up to us to give our compilers as much information as possible, and verify that the compiler is functioning correctly. In the case of most low-level languages, you can generally massage your code until the compiler realizes it can apply a certain transform. In...