How to Make a Rust Program Run Slower Than the Age of the Universe

In typical software development, “optimization” means making programs faster, but this article flips the perspective and investigates how to deliberately make Rust programs run extremely slowly—far beyond the age of the universe—revealing deeper insights into computation, memory, and complexity.

Infinite Loop: The Simplest "Slow"

Without constraints, creating a program that never stops is trivial. The following Rust code implements the most basic infinite loop:

fn main() {
    loop {}
}

It consumes virtually no memory and performs no meaningful computation, yet it never terminates, offering little technical challenge.

Limited Memory and Mandatory Termination

Adding the constraints that the program must stop within finite time and use only limited memory makes the problem interesting. A straightforward approach uses multiple nested loops, each iterating over a huge range:

fn slow_enough() {
    for a in 0..u128::MAX {
        for b in 0..u128::MAX {
            for c in 0..u128::MAX {
                for d in 0..u128::MAX {
                    if d % 1_000_000_000 == 0 {
                        println!("{} {} {} {}", a, b, c, d);
                    }
                }
            }
        }
    }
}

This program uses four u128 loop variables (64 bytes total) and performs roughly (2^128)^4 ≈ 10^154 iterations, which would take far longer than the current age of the universe even at a billion iterations per second.

Zero‑Initialized Memory and the Turing‑Machine Model

If unlimited memory is allowed but must start zeroed and the program must eventually halt, the Turing‑machine model becomes ideal. A 5‑state Turing machine halts after about 47 million steps, while a 6‑state machine may require more than 10↑↑15 steps—numbers far beyond conventional notation.

Example of a 5‑state Turing‑machine transition table expressed with Rust‑style enums:

enum State { A, B, C, D, E, H } // H is the halt state

Manually Implementing Hyperoperations: From Addition to Tetration

Instead of relying on a Turing‑machine model, one can directly implement hyperoperations in Rust to create programs with astronomically many steps. Starting from the primitive "increment" operation, we build addition, multiplication, exponentiation, and finally tetration, each using loops and in‑place updates without efficient built‑in operators.

Increment

fn increment(acc: &mut BigUint) {
    *acc += 1u32;
}

Addition

fn add(a: u32, acc: &mut BigUint) {
    for _ in 0..a {
        *acc += 1u32;
    }
}

Multiplication

fn multiply(a: u32, acc: &mut BigUint) {
    let mut temp = BigUint::ZERO;
    for _ in 0..*acc {
        for _ in 0..a {
            temp += 1u32;
        }
    }
    *acc = temp;
}

Exponentiation

fn exponentiate(a: u32, acc: &mut BigUint) {
    let mut temp = BigUint::from(1u32);
    for _ in 0..*acc {
        let mut product = BigUint::ZERO;
        for _ in 0..temp {
            for _ in 0..a {
                product += 1u32;
            }
        }
        temp = product;
    }
    *acc = temp;
}

Tetration

fn tetrate(a: u32, acc: &mut BigUint) {
    let mut exp_acc = BigUint::from(1u32);
    for _ in 0..*acc {
        let mut mult_acc = BigUint::from(1u32);
        for _ in 0..exp_acc {
            let mut add_acc = BigUint::ZERO;
            for _ in 0..mult_acc {
                for _ in 0..a {
                    add_acc += 1u32;
                }
            }
            mult_acc = add_acc;
        }
        exp_acc = mult_acc;
    }
    *acc = exp_acc;
}

Calling tetrate(10, &mut BigUint::from(15u32)) attempts to compute 10↑↑15, a number so massive that physically performing the calculation is impossible.

Visualization and Optimization Techniques

Although these programs run extremely slowly, their visualization can be optimized to better understand their behavior:

Use SIMD instructions to accelerate pixel processing;

Leverage temporal coherence to update only changed regions;

Divide the computation into chunks, process them in parallel, and then merge results.

These tricks do not speed up the program itself but allow observation of certain execution characteristics within limited time.

Summary and Insights

From the constructions and experiments, several conclusions emerge:

Nested loops are already "slow" : Even simple multi‑level loops can make runtime exceed the universe's lifespan.

Turing machines have immense expressive power : Very few states can generate astonishing computational complexity.

Hyperoperations are effective for representing huge numbers : Tetration and higher‑order operations (pentation, hexation) help us understand and construct programs with extremely long runtimes.

Rust offers precise control over program behavior : By manually implementing low‑level operations, we can craft "slow programs" that meet specific constraints.

This exploration not only showcases Rust's capabilities but also provides a profound look into the nature of computation, runtime, memory usage, and complexity.