cusna

GPU-accelerated simulation and estimation of stochastic actor-oriented models (SAOM / RSiena) and ERGMs.

cusna moves the simulation loop of stochastic actor-oriented models (SAOM — the model family of RSiena) and exponential random graph models (ERGM) onto the GPU. One CUDA thread block simulates one chain; adjacency lives in bitmasks, so triadic change statistics reduce to a bitwise AND and a population count, and the counter-based Philox4x32-10 RNG makes every run bit-reproducible. A multithreaded numba backend mirrors the CUDA kernel exactly and runs everywhere, so the package is fully functional on CPU-only machines (the GPU is an accelerator, not a requirement).

The library offers two complementary paths:

• an acceleration backend for RSiena — a fast, bit-exact simulator that drives the trusted siena07() estimator through its FRAN hook, so RSiena's convergence machinery is inherited unchanged; and
• a compatible standalone estimator — a batched method-of-moments (Robbins–Monro) estimator, the fast path for well-conditioned models.

Installation

pip install cusna              # CPU backend (numpy + numba), works everywhere
pip install "cusna[gpu]"       # + CuPy with the bundled CUDA 12 runtime

The [gpu] extra pulls cupy-cuda12x[ctk], which JIT-compiles the kernels with NVRTC — no separate CUDA toolkit installation is required, only an NVIDIA driver. On a machine without an NVIDIA GPU, install the base package and use backend="cpu".

Quickstart

This runs on any machine (CPU backend, no GPU needed):

import numpy as np
from cusna.saom.data import SaomData
from cusna.saom.stats import EffectSpec
from cusna.saom.mom import MomEstimator

rng = np.random.default_rng(0)

# A tiny two-wave directed network panel (n = 20 actors).
w1 = (rng.random((20, 20)) < 0.15).astype(np.uint8)
w2 = w1.copy()
flip = rng.random((20, 20)) < 0.10           # some ties change between waves
w2[flip] = 1 - w2[flip]
np.fill_diagonal(w1, 0); np.fill_diagonal(w2, 0)

data = SaomData([w1, w2])
effects = [EffectSpec("density"), EffectSpec("recip"), EffectSpec("transTrip")]

est = MomEstimator(data, effects=effects, conditional=True, backend="cpu")
res = est.estimate()
print(res.summary())          # estimated parameters, standard errors, t-conv

Switch to the GPU by passing backend="gpu" (requires cusna[gpu] and an NVIDIA GPU). To force the CPU path on a GPU machine, set the environment variable CUSNA_NO_GPU=1.

Driving RSiena from R

cusna can back the unmodified siena07() estimator via reticulate, so R users get GPU-accelerated simulation without forking RSiena:

source("r/cusnaFRAN.R")
alg$FRAN <- make_cusna_fran(waves, effect_names, cov, conditional, proto_dir, python)
siena07(alg, data = dat, effects = eff, useCluster = FALSE)

A dedicated R package (wrapping this engine through reticulate, in the spirit of tensorflow/keras/greta) is in preparation; see the repository for the current bridge.

What works

SAOM method-of-moments estimation (conditional, unconditional and coevolution), a broad catalog of network and behavior effects (including the gwesp/gwdsp families, user-defined interactions and cross-network effects), creation/endowment effects, network–behavior and multi-network coevolution, missing data, structural zeros/ones, MaxDegree, multiple actor and dyadic covariates, composition change, symmetric networks, sienaGOF-style goodness of fit, and a drop-in siena07() backend. On the ERGM side: a TNT sampler (batch of independent chains, exact ergm proposal protocol) and an MPLE change-statistic kernel. Full MCMC-MLE for ERGMs remains the domain of ergm.

Every capability is validated against RSiena 1.6.6 and ergm 4.12: 23 automated comparisons across five public longitudinal datasets (s50, van de Bunt, Glasgow, Knecht, COW alliances), synthetic panels and three classic ERGM benchmarks. Targets match to machine precision; simulated statistic distributions are equivalent; estimates agree within a fraction of a standard error.

Throughput on an RTX 3060 Ti reaches ~34k full-period simulations per second on the classic s50 dataset (n = 50) — one to two orders of magnitude over single-core RSiena, with the kernel becoming memory-bound around n ≈ 1000.

Documentation & source

• Source, examples and validation harness: https://github.com/artemmaltsev74-techcom/cusna
• API reference: docs/api.md

License

MIT. cusna contains no RSiena or ergm code; it re-implements the models and validates its outputs against those packages.