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PhAIL runs four models (OpenPI/pi0.5, GR00T, ACT, SmolVLA) on bin-to-bin order picking – one of the most common warehouse operations. Same robot (Franka FR3), same objects, hundreds of blind runs. The operator doesn't know which model is running.

Best model: 64 UPH. Human teleoperating the same robot: 330. Human by hand: 1,300+.

Everything is public – every run with synced video and telemetry, the fine-tuning dataset, training scripts. The leaderboard is open for submissions.

Happy to answer questions about methodology, the models, or what we observed.

[1] Vision-Language-Action: https://en.wikipedia.org/wiki/Vision-language-action_model

On a newest version, I attempted to download newest yt-dlp only to be warned of "Suspicious Download". No explanation what that means was provided.

https://xcancel.com/Fried_rice/status/2038894956459290963

On my 14-core/28-thread i9-7940x, forkrun achieves:

* 200,000+ batch dispatches/sec (vs ~500 for GNU Parallel)

* ~95–99% CPU utilization across all 28 logical cores, even when the workload is non-existant (bash no-ops / `:`) (vs ~6% for GNU Parallel). These benchmarks are intentionally worst-case (near-zero work per task) because they measure the capability of the parallelization framework itself, not how much work an external tool can do.

* Typically 50×–400× faster on real high-frequency low-latency workloads (vs GNU Parallel)

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. Each numa node only claims work that is already born-local on its node. Stealing from other nodes is permitted under some conditions when no local work exists.

* SIMD scanning: per-node indexers/scanners use AVX2/NEON to find line boundaries (delimiters) at speeds approaching 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, increase throughput and reduce latency at every stage.

In its fastest (-b) mode (fixed-size batches, minimal processing), it can exceed 1B lines/sec.

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). Trying it is literally two commands:

    . frun.bash    
    frun shell_func_or_cmd < inputs

For benchmarking scripts and results, see the BENCHMARKS dir in the GitHub repo

For an architecture deep-dive, see the DOCS dir in the GitHub repo

Happy to answer questions.