Making your program faster

This page is about your program. Performance is about Khora’s own HTTP server: what it answers, measured how, against what. That page runs commands from the compiler’s repository, so a toolchain you installed cannot run them.

There is no sampling profiler. Nothing in the toolchain will point at a line and tell you it is hot. What there is, in the order you should reach for it:

1. Build with --release before believing any number

khora build . --release

The default profile is debug: unoptimized, with debug information, which is what a crash you are about to read wants. khora test and khora bench have no flag of their own and read KHORA_PROFILE instead:

KHORA_PROFILE=release khora bench .

A bench run in the default profile says so in its output, because a microbenchmark of unoptimized code is a measurement of the wrong program.

How much release buys you depends on where the time goes, and the relationship is not the one you might expect. A small integer loop differs by roughly a factor of two between profiles. But the better your program gets, the less the optimizer has left to find: one word-frequency counter measured 1.45× between profiles in its first form, 1.12× after the hot loop moved into a Map, and 1.09× once it streamed. Work done inside the standard library is already compiled; only your own code changes between profiles.

2. Time a whole operation with khora bench

A bench block times a body and reports a distribution, not a single number:

bench "parsing a page of input" {
  let _ = parse(sample);
}

khora bench . --filter parsing

--filter takes a substring, so one name runs one benchmark. This is the right tool when you can call the thing you care about directly.

Put each benchmark where you can find it again. Spreading bench blocks across files is fine, and so is keeping them beside the code they measure.

3. Time stages inside a real program with the clock

khora bench measures something you can call in isolation. When the question is “which stage of this pipeline is slow”, the answer is a capability you already have:

import std::clock::{Clock};
import std::core::{print};

pub fn main() -> Int {
  with { clock: Clock::real() } {
    let start = clock.monotonic_millis();
    let parsed = parse(input);
    let after_parse = clock.monotonic_millis();
    let counted = count(parsed);
    let after_count = clock.monotonic_millis();

    print("parse ${after_parse - start}ms, count ${after_count - after_parse}ms");
    0
  }
}

monotonic_millis is the one to use for durations. It only ever moves forward, so it cannot be pulled backwards by a clock adjustment mid-measurement — which a wall clock can, and then your timing is negative.

This is manual instrumentation, and it is the technique rather than a workaround: with no profiler, narrowing by hand is how you find the stage that costs you. Time coarse stages first, then subdivide the one that dominates.

4. Count allocations when the time is not in arithmetic

A program can be slow because it is allocating, and that does not look like a hot loop. The runtime can tell you how many objects are live, through an extern declaration — it is not a std function, so you declare the symbol yourself:

extern fn khora_live_count() -> Int;

test "counting words allocates a constant number of objects" {
  let before = khora_live_count();
  let _ = count(sample);
  assert_that(khora_live_count() == before, "the count leaked objects");
}

Khora’s own standard-library suite uses exactly this to pin down allocation behavior — see tests/std-suite/src/vector.kh for the shape, where it asserts that a vector of numbers allocates a constant number of objects however many numbers go in.

An operation whose allocation grows with its input, when it should not, is usually a value being rebuilt rather than updated. String concatenation in a loop is the common one: each + builds a new string, so a loop over n items copies n² characters. That is the shape behind most “it got slower than it should have” surprises.

What is not here

• No sampling profiler, so no flamegraph and no “this line is 40% of your runtime”. Sections 3 and 4 are how you find that out.
• No allocation profiler. live_count() gives you a number at a point, not a breakdown by type or site.
• khora build emits DWARF on Linux, so an external tool such as perf has symbols to work with. Nothing in CI exercises that, so treat it as something that may work rather than a supported workflow, the same caveat Debugging gives for a debugger.

What to measure first

In order, because each one is cheaper than the next and rules more out:

1. Is it built --release? A factor of two hides a lot.
2. Does the work grow faster than the input? Double the input and time it. A doubling that quadruples the time is an accidentally-quadratic operation, and no amount of constant-factor tuning will save it.
3. Which stage? Coarse monotonic_millis brackets, then subdivide.
4. Is it allocating per item where it should allocate once?