Generally when I want to run something with so much parallelism I just write a small Go program instead, and let Go's runtime handle the scheduling. It works remarkably well and there's no execve() overhead too
I guess I've never really used parallel for anything that was bound by the dispatch speed of parallel itself. I've always use parallel for running stuff like ffmpeg in a folder of 200+ videos, and the speed in which parallel decides to queue up the jobs is going to be very thoroughly eaten by the cost of ffmpeg itself.
Still, worth a shot.
I have to ask, was this vibe-coded though? I ask because I see multiple em dashes in your description here, and a lot of no X, no Y... notation that Codex seems to be fond of.
ETA: Not vibe coded, I see stuff from four years ago...my mistake!
Have you ever run GNU Parallel on a powerful machine just to find one core pegged at 100% while the rest sit mostly idle?
I hit that wall...so I built forkrun.
forkrun is a self-tuning, drop-in replacement for GNU Parallel (and xargs -P) designed for high-frequency, low-latency shell workloads on modern and NUMA hardware (e.g., log processing, text transforms, HPC data prep pipelines).
On my 14-core/28-thread i9-7940x it achieves:
- 200,000+ batch dispatches/sec (vs ~500 for GNU Parallel)
- ~95–99% CPU utilization across all 28 logical cores (vs ~6% for GNU Parallel)
- Typically 50×–400× faster on real high-frequency low-latency workloads (vs GNU Parallel)
These benchmarks are intentionally worst-case (near-zero work per task), where dispatch overhead dominates. This is exactly the regime where GNU Parallel and similar tools struggle — and where forkrun is designed to perform.
A few of the techniques that make this possible:
- Born-local NUMA: stdin is splice()'d into a shared memfd, then pages are placed on the target NUMA node via set_mempolicy(MPOL_BIND) before any worker touches them, making the memfd NUMA-spliced.
- SIMD scanning: per-node indexers use AVX2/NEON to find line boundaries at memory bandwidth and publish byte-offsets and line-counts into per-node lock-free rings.
- Lock-free claiming: workers claim batches with a single atomic_fetch_add — no locks, no CAS retry loops; contention is reduced to a single atomic on one cache line.
- Memory management: a background thread uses fallocate(PUNCH_HOLE) to reclaim space without breaking the logical offset system.
…and that’s just the surface. The implementation uses many additional systems-level techniques (phase-aware tail handling, adaptive batching, early-flush detection, etc.) to eliminate overhead at every stage.
In its fastest (-b) mode (fixed-size batches, minimal processing), it can exceed 1B lines/sec. In typical streaming workloads it's often 50×–400× faster than GNU Parallel.
forkrun ships as a single bash file with an embedded, self-extracting C extension — no Perl, no Python, no install, full native support for parallelizing arbitrary shell functions. The binary is built in public GitHub Actions so you can trace it back to CI (see the GitHub "Blame" on the line containing the base64 embeddings).
I’m not a parallels kind of user but I can appreciate your craft and know how rewarding these odysseys can be :)
What was the biggest “aha” moment when you worked how things interlock or you needed to make both change an and b at the same time, as either on their own slowed it down? Etc. And what is the single biggest impacting design choice?
And if you’re objective, what could be done to other tools to make them competitive?
Generally when I want to run something with so much parallelism I just write a small Go program instead, and let Go's runtime handle the scheduling. It works remarkably well and there's no execve() overhead too
dang and u did all that without a 10 year journey
I guess I've never really used parallel for anything that was bound by the dispatch speed of parallel itself. I've always use parallel for running stuff like ffmpeg in a folder of 200+ videos, and the speed in which parallel decides to queue up the jobs is going to be very thoroughly eaten by the cost of ffmpeg itself.
Still, worth a shot.
I have to ask, was this vibe-coded though? I ask because I see multiple em dashes in your description here, and a lot of no X, no Y... notation that Codex seems to be fond of.
ETA: Not vibe coded, I see stuff from four years ago...my mistake!
Hi HN,
Have you ever run GNU Parallel on a powerful machine just to find one core pegged at 100% while the rest sit mostly idle?
I hit that wall...so I built forkrun.
forkrun is a self-tuning, drop-in replacement for GNU Parallel (and xargs -P) designed for high-frequency, low-latency shell workloads on modern and NUMA hardware (e.g., log processing, text transforms, HPC data prep pipelines).
On my 14-core/28-thread i9-7940x it achieves:
- 200,000+ batch dispatches/sec (vs ~500 for GNU Parallel)
- ~95–99% CPU utilization across all 28 logical cores (vs ~6% for GNU Parallel)
- Typically 50×–400× faster on real high-frequency low-latency workloads (vs GNU Parallel)
These benchmarks are intentionally worst-case (near-zero work per task), where dispatch overhead dominates. This is exactly the regime where GNU Parallel and similar tools struggle — and where forkrun is designed to perform.
A few of the techniques that make this possible:
- Born-local NUMA: stdin is splice()'d into a shared memfd, then pages are placed on the target NUMA node via set_mempolicy(MPOL_BIND) before any worker touches them, making the memfd NUMA-spliced.
- SIMD scanning: per-node indexers use AVX2/NEON to find line boundaries at memory bandwidth and publish byte-offsets and line-counts into per-node lock-free rings.
- Lock-free claiming: workers claim batches with a single atomic_fetch_add — no locks, no CAS retry loops; contention is reduced to a single atomic on one cache line.
- Memory management: a background thread uses fallocate(PUNCH_HOLE) to reclaim space without breaking the logical offset system.
…and that’s just the surface. The implementation uses many additional systems-level techniques (phase-aware tail handling, adaptive batching, early-flush detection, etc.) to eliminate overhead at every stage.
In its fastest (-b) mode (fixed-size batches, minimal processing), it can exceed 1B lines/sec. In typical streaming workloads it's often 50×–400× faster than GNU Parallel.
forkrun ships as a single bash file with an embedded, self-extracting C extension — no Perl, no Python, no install, full native support for parallelizing arbitrary shell functions. The binary is built in public GitHub Actions so you can trace it back to CI (see the GitHub "Blame" on the line containing the base64 embeddings).
- Benchmarking scripts and raw results: https://github.com/jkool702/forkrun/blob/main/BENCHMARKS
- Architecture deep-dive: https://github.com/jkool702/forkrun/blob/main/DOCS
- Repo: https://github.com/jkool702/forkrun
Trying it is literally two commands:
Happy to answer questions.Thanks for making and thanks for sharing :)
I’m not a parallels kind of user but I can appreciate your craft and know how rewarding these odysseys can be :)
What was the biggest “aha” moment when you worked how things interlock or you needed to make both change an and b at the same time, as either on their own slowed it down? Etc. And what is the single biggest impacting design choice?
And if you’re objective, what could be done to other tools to make them competitive?
I am using a 9950x3D processor and didn't see any slow-down nor cpu sitting idle, I suggest you read the man-pages more clearly :P
I know this is obviously sarcasm and it made me laugh but I'm pretty sad HN couldn't catch it.
>Have you ever run GNU Parallel on a powerful machine just to find one core pegged at 100% while the rest sit mostly idle?
Yes, to my extreme frustration. Thank you, I'm installing this right now while I read the rest of your comment.
Please don't support only curl for installation. There are many package registries you can use; e.g., https://github.com/aquaproj/aqua-registry
Why the hell do you curl ? Additionally, why do you advertise it when you just had uploaded it? Nobody should install something that new...