Goroutines Are Not Significantly Smaller Than Threads

The most commonly cited drawback of OS-level threads is that they use a lot of RAM. This is not true on Linux.

Let’s compare memory footprint of 10_000 Linux threads with 10_000 goroutines. We spawn 10k workers, which sleep for about 10 seconds, waking up every 10 milliseconds. Each worker is staggered by a pseudorandom delay up to 200 milliseconds to avoid the thundering herd problem.

use std::{thread, time::Duration};

fn main() {
    let mut threads = Vec::new();
    for i in 0u32..10_000 {
        let t = thread::spawn(move || {
            let bad_hash = i.wrapping_mul(2654435761) % 200_000;
            thread::sleep(Duration::from_micros(bad_hash as u64));
            for _ in 0..1000 {

    for t in threads {
package main

import (

func main() {
    var wg sync.WaitGroup
    for i := uint32(0); i < 10_000; i++ {
        i := i
        go func() {
            defer wg.Done()
            bad_hash := (i * 2654435761) % 200_000
            time.Sleep(time.Duration(bad_hash) * time.Microsecond)
            for j := 0; j < 1000; j++ {
                time.Sleep(10 * time.Millisecond)

We use time utility to measure memory usage:

command time --format 'real %es\nuser %Us\nsys  %Ss\nrss  %Mk' "$@"

The results:

λ rustc main.rs -C opt-level=3 && ./t ./main
real 10.35s
user 4.96s
sys  16.06s
rss  94472k

λ go build main.go && ./t ./main
real 10.92s
user 13.30s
sys  0.55s
rss  34924k

A thread is only 3 times as large as a goroutine. Absolute numbers are also significant: 10k threads require only 100 megabytes of overhead. If the application does 10k concurrent things, 100mb might be negligible.

Note that it is wrong to use this benchmark to compare performance of threads and goroutines. The workload is representative for measuring absolute memory overhead, but is not representative for time overhead.

That being said, it is possible to explain why threads need 21 seconds of CPU time while goroutines need only 14. Go runtime spawns a thread per CPU-core, and tries hard to keep each goroutine tied to specific thread (and, by extension, CPU). Threads by default migrate between CPUs, which incurs synchronization overhead. Pinning threads to cores in a round-robin fashion removes this overhead:

$ cargo build --release && ./t ./target/release/main --pin-to-core
    Finished release [optimized] target(s) in 0.00s
real 10.36s
user 3.01s
sys  9.08s
rss  94856k

The total CPU time now is approximately the same, but the distribution is different. On this workload, goroutine scheduler spends roughly the same amount of cycles in the userspace that the thread scheduler spends in the kernel.

Code for the benchmarks is available here: matklad/10k_linux_threads.