Actually the thing that might need that would be make -j. Maybe it would make sense to make a bsd or gnu make version that integrates those optimizations. I am actually running a lot of stuff in cygwin and there is huge fork penalties on windows, so my hope would be to actually get some real payloads faster...

So...yes, the execve overhead is real. BUT there's still a lot you can accomplish with pure bash builtins (which don't have the execve overhead). And, if you're open to rewriting things (which would probably be required to some extent if you were to make something intended for shell to run in Go) you can port whatever you need to run into a bash builtin and bypass the execve overhead that way. In fact, doing that is EXACTLY what forkrun does, and is a big part of why it is so fast.

10 years represents going from

    maxJobs=$(nprocs)
    while read -r nn; do
      code_to_parallelize "$nn"
      (( $(jobs -p | wc -l) > maxJobs )) && wait -n
    done < inputs

to a NUMA-Aware Contention-Free Dynamically-Auto-Tuning Bash-Native Streaming Parallelization Engine. I dare say 10 years is about the norm for going from "beginner" to "PhD-level" work.

    code_to_parallelize "$nn"

    code_to_parallelize "$nn" &

Yeah, sorry; I'm a little on-edge because of all the low-effort LLM stuff I've seen in Show HN.

FWIW, I actually did play with Forkrun now, and it seems pretty neat!

> don't turn into 20 pages of technical writing

Given the expertise at display and the 10yr on/off journey in building Forkrun, I'm sure there's folks like me who'd be glad to read those 20 pages!

Thanks for sharing your work.

parallel works fine so long as the time per job is on the order of seconds or longer.

Let me give you an example of a "worst-case" scenario for parallel. Start by making a file on a tmpfs with 10 million newlines

    yes $'\n' | head -n 10000000 > /tmp/f1

So, now lets see how long it takes parallel to push all these lines through a no-op. This measures the pure "overhead of distributing 10 million lines in batches". Ill set it to use all my cpu cores (`-j $(nproc)`) and to use multiple lines per batch (`-m`).

    time { parallel -j $(nproc) -m : <f1; }

    real    2m51.062s
    user    2m52.191s
    sys     0m6.800s

Average CPU utalization here (on my 14c/28t i9-7940x) is CPU time / real time

    (172.191 + 6.8) / 171.062 = 1.0463516152 CPUs utalized

Note that there is 1 process that is pegged at 100% usage the entire time that isnt doing any "work" in terms of processing lines - its just distributing lines to workers. If we assume that thread averaged about 0.98 cores utalized, it means that throughout the run it managed to keep around 0.066 out of 28 CPUs saturated with actual work.

Now let's try with frun

    . ./frun.bash
    time { frun : <f1; }

    real    0m0.559s
    user    0m10.409s
    sys     0m0.201s

CPU utilization is

    ( 10.409 + .201 ) / .559 = 18.9803220036 CPUs utalized

Lets compare the wall clock times

    171.062 / 0.559 = 306x speedup

Interestingly, if we look at the ratio of CPU utilization (spent on real work):

    18.9803220036 / 0.066 = 287x more CPU usage doing actual work

which gives a pretty straightforward story - forkrun is 300x faster here because it is utilizing 300x more CPU for actually doing work.

This regime of "high frequency low latency tasks" - millions or billions of tasks that make milliseconds or microseconds each - is the regime where forkrun excels and tools like parallel fall apart.

Side note: if I bump it to 100 million newlines:

    time { frun : <f1; }

    real    0m4.212s
    user    1m52.397s
    sys     0m1.019s

CPU utilization:

    ( 112.397 + 1.019 ) / 4.212 = 26.9268 CPUs utalized

which on a 14c/28t CPU doing no-ops...isnt bad.

So, in forkruns development there have been a few "AHA!" moments. Most of them were accompanied by a full re-write (current forkrun is v3).

The 1st AHA, and the basis for the original forkrun, was that you could eliminate a HUGE amount of the overhead of parallelizing things in shell in you use persistent workers and have them run things for you in a loop and distribute data to them. This is why the project is called "forkrun" - its short for "first you FORK, then you RUN".

The 2nd AHA, which spawned forkrun v2, was that you could distribute work without a central coordinator thread (which inevitably becomes the bottleneck). forkrun v2 did this by having 1 process dump data into a tmpfile on a ramdisk, then all the workers read from this file using a shared file descriptor and a lightweight pipe-based lock: write a newline into a shared anonymous pipe, read from pipe to acquire lock, write newline back to pipe to release it. FIFO naturally queues up waiters. This version actually worked really well, but it was a "serial read, parallel execute" design. Furthermore, the time it took to acquire and release a lock meant the design topped out at ~7 million lines per second. Nothing would make it faster, since that was the locking overhead.

The 3rd AHA was that I could make a very fast (SIMD-accellerated) delimiter scanner, post the byte offsets where lines (or batches of lines) started in the global data file, and then workers could claim batches and read data in parallel, making the design fully "parallel read + parallel execute"

The 4th AHA was regarding NUMA. it was "instead of reactively re-shuffling data between nodes, just put it on the right node to begin with". Furthermore, determine the "right node" using real-time backpressure from the nodes with a 3 chunk buffer to ensure the nodes are always fed with data. This one didn't need a rewrite, but is why forkrun scales SO WELL with NUMA.

> And if you’re objective, what could be done to other tools to make them competitive?

I wanted to reply separately to this bit, because I needed a bit of time to think about and respond to it.

To be frank, parallel optimizes for "breadth of features" and has, for example, the ability to coordinate distributed computing over ssh. But it fundamentally assumes that the workload itself will take dramatically longer than the coordination.

To really be competitive in "high-frequency low-latency workloads", where you have millions of inputs and each only takes microseconds, you would need a complete rewrite with an entirely different way of thinking.

Let me drop a few numbers just to drive this point home. Parallel is capable of batching and distributing around 500 batches of work a second. forkrun, in its "pass arguments via quoted cmdline args" mode is capable of batching and distributing around 10,000 batches a second. This is mostly limited by how fast bash can assemble long strings of quoted arguments to pass via the command line. In forkrun's `-s` mode, which bypasses bash entirely and splices data directly to the stdin of what you are parallelizing, forkrun is capable of batching and distributing over 200,000 batches a second.

The biggest architectural hurdle most existing tools have that makes it impossible to achieve forkrun's batch distribution rate is that almost all use a central distributor thread that forks each individual call (which is very expensive) and that is ALWAYS the bottleneck in high-frequency low-latency workloads. Pushing past this requires moving to a persistent worker model without a central coordinator. This alone necessitates a complete rewrite for basically all the existing tools.

That said, forkrun takes it so much further:

* It uses a SIMD-accelerated delimiter scanner + lock-free async-io to allow for workers to not only execute in parallel but to read inputs to run in parallel.

* It doesn't just use a standard "lock-free" design with CAS retry loops everywhere - it treats the problem like a physical pipeline of data flow and structurally eliminates contention between workers. The literal only "contention" is a single atomic on a single cache line - namely when a worker claims a batch by running `atomic_fetch_add` on a global monotonically increasing index (`read_idx`).

* It doesn't use heuristics - it uses a proper closed-loop control system. There is a 3-stage ramp-up (saturate workers -> geometric ramp -> backpressure-guided PID) to dynamically determine the batch size and the number of workers that works extremely well for all input types with 0 manual tuning.

* It keeps complexity in the slow path. Claiming a batch of lines literally just involves reading a couple shared mmap'ed vars and an `atomic_fetch_add` op in the fast path, which is why it can break 1 billion lines a second. The complexity is all so the slow path degrades gracefully, which is where it smartly trades latency for throughput (but only when throughput is limited by stdin to begin with).

* It treats NUMA as 1st class and chooses the "obvious in hindsight" path to just put data on the correct NUMA node from the very start instead of re-shuffling it between nodes reactively later.

I could go on, but the TL;DR is: to be competitive, other tools would really need to try and solve the "high-frequency low-latency stream parallelization" problem from first principles like forkrun did.

How did it work for you?

I appreciate the high praise re: forkrun.

forkrun's NUMA approach is really largely based on the idea that, as you said, "real workloads mostly wait for memory". The waiting for memory gets worse in NUMA because accessing memory from a different chiplet or a different socket requires accessing data that is physically farther from the CPU and thus has higher latency. forkrun takes a somewhat unique approach in dealing with this: instead of taking data in, putting it somewhere, and reshuffling it around based on demand, forkrun immediately puts it on the correct numa node's memory when it comes in. This creates a NUMA-striped global data memfd. on NUMA forkrun duplicates most of its machinery (indexer+scanner+worker pool) per node, and each node's machinery is only offered chunks from the global data memfd that are already on node-local memory.

This directly aims to solve (or at least reduce the effect from) "CPUs waiting for memory" on NUMA systems, where the wait (if memory has to cross sockets) can be substantial.

Theres no "install" - you just need to source the `frun.bash` file. Downloading frun.bash and sourcing it works just fine. directly sourcing a curl stream that grabs frun.bash from the git repo is just an alternate approach. It is not "required" by any means.

I know this is obviously sarcasm and it made me laugh but I'm pretty sad HN couldn't catch it.

curl isnt required - you just need to source the `frun.bash` file. Downloading frun.bash and sourcing it works just fine. directly sourcing a curl stream that grabs frun.bash from the github repo is just an alternate approach. It is not "required" by any means.

Setting -j doesn't seem to help any. Rush still does this faster (160ms vs. 250ms for frun)