cargo
Rust package manager (36 subcommands).
pigeons-spi
Pigeons.jl implements **Strong Parallelism Invariance (SPI)**: identical results regardless of number of parallel workers. This is the pattern Gay.jl adopts for deterministic color generation.
Full skill instructions
name: pigeons-spi
description: Pigeons.jl parallel tempering MCMC with Strong Parallelism Invariance (SPI) via SplittableRandoms
version: 1.0.0
trit: +1 # PLUS - generates samples, produces output
Pigeons.jl implements Strong Parallelism Invariance (SPI): identical results regardless of number of parallel workers. This is the pattern Gay.jl adopts for deterministic color generation.
Key Insight: same seed → same samples → same colors
The core innovation: determinism via splittable random streams.
using Pigeons
using SplittableRandoms
# SPI: same seed produces identical results on 1, 4, or 100 workers
rng = SplittableRandom(1069) # Our seed from AGENTS.md
# Split creates independent streams with deterministic relationship
rng1, rng2 = split(rng)
rng_a, rng_b, rng_c = split(rng1, 3) # 3-way split for GF(3) triads
using Pigeons
# Basic parallel tempering
pt = pigeons(
target = my_target,
n_chains = 10, # Number of temperature chains
n_rounds = 12, # 2^12 = 4096 samples per chain
seed = 1069, # SPI: reproducible across workers
explorer = SliceSampler() # or AutoMALA()
)
# Get samples
samples = Chains(pt)
| Explorer | Use Case |
|---|---|
SliceSampler() | General purpose, robust |
AutoMALA() | High-dimensional, auto-tuned HMC |
Mix(SliceSampler(), AutoMALA()) | Combine strategies |
using LogDensityProblems
struct MyTarget
dim::Int
data::Vector{Float64}
end
# Required interface
LogDensityProblems.dimension(t::MyTarget) = t.dim
LogDensityProblems.logdensity(t::MyTarget, x) = -sum((x .- t.data).^2) / 2
# Optional: better initialization
Pigeons.initialization(t::MyTarget, rng, _) = randn(rng, t.dim)
# Optional: extract samples
Pigeons.extract_sample(state, _) = copy(state.x)
Both packages share the SplittableRandoms pattern:
using Gay
using Pigeons
using SplittableRandoms
# Same seed, deterministic across both packages
seed = 1069
Gay.seed!(seed)
# Pigeons MCMC with colored diagnostics
pt = pigeons(target = my_target, seed = seed, n_rounds = 10)
# Color each chain with Gay.jl
for (i, chain) in enumerate(1:pt.n_chains)
hex = Gay.color_at(seed, i)
trit = Gay.trit_at(seed, i) # GF(3): -1, 0, +1
println("Chain $i: $hex (trit=$trit)")
end
# GF(3) conservation: sum of trits ≡ 0 (mod 3)
function colored_pt_summary(pt, seed)
println("Parallel Tempering with Gay.jl Colors")
println("=" ^ 40)
for round in 1:pt.n_rounds
hex = Gay.color_at(seed, round)
logZ = stepping_stone(pt, round)
println("Round $round: $hex | log(Z) ≈ $(round(logZ, digits=3))")
end
end
Comrade uses Pigeons for Event Horizon Telescope black hole imaging:
using Comrade
using Pigeons
# Black hole image model
model = ComradeModel(data, prior)
# Run parallel tempering MCMC
chain = sample(model, Pigeons.PT(
n_chains = 16,
n_rounds = 14,
explorer = AutoMALA()
), seed = 1069)
# Posterior images with deterministic colors
for (i, img) in enumerate(posterior_images(chain))
color = Gay.color_at(1069, i)
# Apply color to visualization
end
Pigeons.jl as PLUS (+1) in the triad:
| Trit | Role | Package |
|---|---|---|
| -1 (MINUS) | Consumes/validates | DynamicPPL priors |
| 0 (ERGODIC) | Coordinates | Pigeons controller |
| +1 (PLUS) | Generates output | Pigeons samples |
# Conservation: samples generated (PLUS) must balance constraints (MINUS)
n_samples = 2^12
n_constraints = length(prior_bounds)
@assert mod(n_samples - n_constraints + 0, 3) == 0 # GF(3) conserved
# Install
julia -e 'using Pkg; Pkg.add("Pigeons")'
# Run with colored output
julia --project=@Gay -e '
using Pigeons, Gay
Gay.seed!(1069)
pt = pigeons(target = toy_mvn_target(3), n_rounds = 8, seed = 1069)
for i in 1:8
println("Round $i: $(Gay.color_at(1069, i))")
end
'
# Verify SPI (same results on different worker counts)
julia -p 4 -e '
using Pigeons
pt1 = pigeons(target = toy_mvn_target(3), seed = 42, n_workers = 1)
pt4 = pigeons(target = toy_mvn_target(3), seed = 42, n_workers = 4)
@assert stepping_stone(pt1) ≈ stepping_stone(pt4) # SPI verified
'
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