Show HN: A Soup-inspired runtime for a real fruit-fly connectome =)

A developer released soup-connectome v0.1.0, a portable runtime that executes sparse biological connectomes across CPU, CUDA, WebGPU, and WASM using deterministic fixed-point LIF semantics, with the Janelia male fruit-fly connectome (MaleCNS) as its first dataset target. The project's measured MaleCNS .scx artifact contains 211,577 neurons and 24,678,466 edges, and its standard 4-timestep streamed run completed in 221.537098 seconds on CPU and 266.778847 seconds on Python WebGPU, both producing spike counts of [1, 0, 0, 0]. The author states the work is infrastructure rather than a game demo, focused on graph storage, streaming, backend parity, and device planning, and does not claim biological or scientific validation.

Inspired by Soup https://github.com/MakazhanAlpamys/Soup Portable runtime for executing sparse biological connectomes across CPU, CUDA, WebGPU, and WASM. v0.1.0 https://github.com/MakazhanAlpamys/soup-connectome/tree/v0.1.0 · MaleCNS https://male-cns.janelia.org/ · Browser runtime /MakazhanAlpamys/soup-connectome/blob/main/web/README.md soup-connectome turns a sparse connectome into a portable .scx artifact and executes it with deterministic fixed-point LIF semantics. The first dataset target is MaleCNS https://male-cns.janelia.org/ , the male fruit-fly connectome released by Janelia. Built with ideas from Soup. This is an independent infrastructure experiment that carries Soup's streamed sparse-graph approach into a real biological connectome runtime. Soup is the architectural inspiration, not a bundled runtime dependency or a claim of official endorsement. This is infrastructure, not a game demo: the project focuses on graph storage, streaming, backend parity, and explicit device planning. It does not claim biological validation. | Capability | Status | Evidence | |---|---|---| | CPU resident + streamed runtime | measured | Full test suite and MaleCNS smoke | | CUDA resident + streamed backend | implemented | Optional; full-scale CUDA performance not tested | | Python WebGPU resident + streamed backend | measured | Example parity and MaleCNS smoke | | WASM CPU streaming runtime | measured | Generated web/node bindings and parity fixture | | MaleCNS .scx artifact | measured | 211,577 neurons, 24,678,466 edges | | Biological/scientific validation | not tested | LIF parameters are runtime configuration | measured on the local host · 211,577 neurons · 24,678,466 edges · three streamed CSR blocks The reproducible standard run uses threshold=20000 , reset=0 , decay shifts= 2 , refractory steps=2 , --timesteps 4 , --seed-neuron 0 , and --seed-potential 30000 . | Backend | Residency | Timesteps | Wall time | Spike counts | |---|---|---|---|---| | CPU | streamed | 4 | 221.537098 s — measured | 1, 0, 0, 0 | | Python WebGPU | streamed | 4 | 266.778847 s — measured | 1, 0, 0, 0 | python scripts/benchmark malecns.py --device cpu --residency streamed --timesteps 4 python scripts/benchmark malecns.py --device webgpu --residency streamed --timesteps 4 These numbers are reproducibility evidence for the runtime, not a throughput claim or biological calibration. Additional one-timestep smoke and synthetic propagation results are recorded below. Additional validation runs | Run | Result | |---|---| | Full-scale, one timestep, zero initial spikes, CPU | 2.245442 s , 0 spikes — measured | | Full-scale, one timestep, zero initial spikes, Python WebGPU | 2.695649 s , 0 spikes — measured | | Full-scale, two-timestep active synthetic stress, CPU | 130.364592 s , spike counts 1, 319 — measured | | Same active synthetic stress, Python WebGPU | 128.487143 s , spike counts 1, 319 — measured | The active stress configuration uses threshold=1 , reset=0 , decay shifts= 31 , and refractory steps=0 ; it is a propagation test, not a biological calibration or representative throughput benchmark. Large sparse graphs create a different deployment problem from a conventional dense neural model. The runtime keeps neuron state and the delay line resident while streaming sparse source blocks, so graph residency is an explicit axis: MaleCNS Feather files │ ▼ local adapter + sign mapping │ ▼ portable .scx artifact │ ├── CPU resident / streamed ├── CUDA resident / streamed ├── Python WebGPU resident / streamed └── browser WebGPU streamed / WASM CPU fallback Streaming is a memory-shape and portability feature. It is not automatically a throughput guarantee: the current streamed accelerator paths validate and transfer one active source block at a time and do not use a prefetch cache. Install the core package and development/data extras: python -m pip install -e ". dev,data " Optional backends: python -m pip install -e ". dev,data,cuda " python -m pip install -e ". dev,data,webgpu " Run the deterministic fixture: soup-connectome run --dataset example --device cpu soup-connectome run --dataset example --device cpu --residency streamed soup-connectome run --dataset example --device webgpu --residency streamed soup-connectome plan --dataset example --device cpu Explicit cuda and webgpu never silently fall back to CPU. auto resolves to CPU by design. The adapter is local-only and never downloads data automatically. It requires: - connection weights; - body annotations; - body neurotransmitters. For the verified MaleCNS v1.0 download, the measured columns are: | File | Columns | |---|---| | weights | body pre , body post , weight | | annotations | bodyId , type , somaSide | | neurotransmitters | body , consensus nt | Inspect local schemas first: soup-connectome inspect --file path/to/body-annotations.feather soup-connectome inspect --file path/to/body-neurotransmitters.feather The first artifact uses the curated annotation node filter and this explicit sign mapping: soup-connectome convert \ --weights data/male-cns/connectome-weights-male-cns-v1.0-minconf-0.5.feather \ --annotations data/male-cns/body-annotations-male-cns-v1.0-minconf-0.5.feather \ --neurotransmitters data/male-cns/body-neurotransmitters-male-cns-v1.0.feather \ --output artifacts/male-cns-v1.0-annotated.scx \ --annotation-id bodyId \ --annotation-type type \ --annotation-side somaSide \ --neurotransmitter-id body \ --neurotransmitter-name consensus nt \ --sign-mapping '{"acetylcholine": 1, "gaba": -1, "glutamate": 1}' \ --exclude-neurotransmitter unclear \ --exclude-neurotransmitter dopamine \ --exclude-neurotransmitter histamine \ --exclude-neurotransmitter octopamine \ --exclude-neurotransmitter serotonin \ --exclude-neurotransmitter unknown \ --scope full \ --node-filter annotations The local download measured 151,856,684 weight rows, 211,577 annotation rows, 1,835,518 neurotransmitter rows, and 1,109,008,094 bytes across the three Feather files. These are measurements for this exact download, not universal hardware requirements. Raw data, generated artifacts, WASM build outputs, and browser test results are intentionally ignored by Git. The backend-independent simulation contract uses: - signed int32 membrane potentials; - signed int16 synaptic impulses; - positive integer timestep delays; - checked arithmetic with rejected overflow; - arithmetic-shift leak: V := V - V k . These are representation choices, not measured biological constants. python -m pytest -q python -m ruff check . python -m ruff format --check . cd web npm test npm run test:e2e cd wasm cargo fmt --check cargo test cargo check --target wasm32-unknown-unknown The repository uses three evidence labels: - measured — produced by an actual local run or benchmark; - estimated — a design calculation or planning assumption; - not tested — no local evidence yet. - No biological calibration or scientific fidelity claim is made. - Morphology and EM-volume simulation are out of scope for the current phase. - Automatic MaleCNS downloads and a networked data pipeline are not included. - Full-scale active stress is measured for two timesteps with a synthetic configuration; longer runs on the Python reference path are not tested . - Browser compatibility across GPU vendors and operating systems is not tested . src/soup connectome/ runtime, graph format, adapters, backends, CLI scripts/ reproducible full-scale benchmark entry point tests/ Python contract and parity tests web/src/ browser WebGPU and WASM streaming host web/wasm/ wasm-bindgen CPU runtime web/test/ browser parity fixture docs/ project documentation