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mcising

High-performance Ising model Monte Carlo simulation with a Rust core.

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mcising is a Python library for Monte Carlo simulation of Ising spin systems. It supports 5 lattice geometries, J1-J2-J3 frustrated magnetism with external fields, 3 Monte Carlo algorithms, 3 execution modes (including parallel tempering), and adaptive thermalization. The performance-critical core is written in Rust via PyO3.

Performance

On one core of an Apple M4 (10 cores: 4 performance + 6 efficiency), mcising performs 351M Metropolis spin updates per second on a 32×32 square lattice at Tc — 140.4× faster than pure Python and 15.0× faster than a NumPy checkerboard implementation of the same update, and 2.4× faster than peapods on a matched workload (energy recorded every sweep on both sides).

mcising 0.29.0 (commit 2e3548a), Python 3.12.11, measured 2026-09-01; medians of repeated runs. Regenerate with uv run --group benchmark python benchmarks/run_all.py --write-docs.

Implementation Lattice Timed sweeps Spin updates/s mcising is
Pure Python 32×32 200 2M 140.4×
NumPy (checkerboard) 32×32 1,000 23M 15.0×
mcising (Rust) 32×32 10,000 351M —
NumPy (checkerboard) 128×128 200 69M 4.7×
mcising (Rust) 128×128 1,000 324M —

Single-spin-flip Metropolis on the square lattice at Tc, one thread, Apple M4 (10 cores: 4 performance + 6 efficiency). Pure Python is a plain loop with precomputed neighbour and Boltzmann tables; the NumPy implementation updates the two checkerboard sublattices as whole arrays. Spin updates/s counts attempted flips; timed sweeps exclude 100 warm-up sweeps.

Matched physics against peapods 0.2.0 (Rust/PyO3): the same Hamiltonian (H = −J Σ s_i s_j, J = 1, energies per site, bond counted once), the same Metropolis sweep (a sequential scan with one attempt per site), the same temperature (Tc), 5,000 thermalization sweeps, then 100,000 timed sweeps with the energy recorded every sweep on both sides, one thread, 3 seeds per side, Apple M4 (10 cores: 4 performance + 6 efficiency). peapods reports +Σ J s_i s_j / N, so its sign is flipped before comparison. A row is published only when the two mean energies agree within 0.5 %.

Lattice E/site mcising E/site peapods Δ Sweeps/s mcising Sweeps/s peapods mcising is
Square 32×32 -1.4325 ± 0.0018 -1.4346 ± 0.0007 0.15 % 249,160 104,786 2.4×
Triangular 32×32 -2.0305 ± 0.0012 -2.0320 ± 0.0014 0.07 % 203,737 86,013 2.4×
Cubic 16×16×16 -1.0346 ± 0.0010 -1.0347 ± 0.0002 0.01 % 34,196 15,677 2.2×

Wolff and Swendsen-Wang are not compared: peapods interleaves cluster updates with Metropolis sweeps rather than running cluster-only sweeps, so no peapods workload matches an mcising cluster sweep.

Every number above is written by benchmarks/run_all.py from the committed benchmarks/results.json, and the test suite fails if the two drift apart. Regenerate with uv run --group benchmark python benchmarks/run_all.py --write-docs (a few minutes), or re-render the committed results with --from-json benchmarks/results.json --write-docs. The performance page adds the per-lattice, cluster-algorithm, scaling and parallel-execution tables.

mcising also supports features not available in peapods: J2/J3 coupling, external magnetic field, honeycomb lattice, 1D chain, and parallel tempering.

Features

  • 5 lattice geometries -- square, triangular, honeycomb (2-sublattice), cubic (3D), chain (1D)
  • 3 MC algorithms -- Metropolis, Wolff cluster, Swendsen-Wang cluster
  • 3 execution modes -- sequential cool-down, independent parallel (Rayon), parallel tempering with replica exchange
  • J1-J2-J3 frustrated magnetism -- nearest, next-nearest, and third-nearest-neighbor couplings
  • External magnetic field -- h coupling, compatible with all lattices
  • 15 Metropolis strategies -- auto-selected lookup tables optimized per coupling combination
  • Adaptive thermalization -- MSER equilibration detection + Sokal autocorrelation estimation
  • Correlation functions -- spin-spin correlation and correlation length
  • HDF5 output with crash-safe incremental checkpointing
  • Rich CLI with progress bars, benchmarking, and structured output
  • Fully reproducible -- deterministic RNG (Xoshiro256**), same seed = same results

Installation

pip install mcising

# with plotting (matplotlib) and/or DataFrame export (pandas):
pip install 'mcising[plot]'
pip install 'mcising[plot,dataframe]'

For development (requires Rust toolchain):

git clone https://github.com/bcivitcioglu/mcising.git
cd mcising
uv sync
uv run maturin develop

Quick Start

Python API

from mcising import Simulation, SimulationConfig, LatticeConfig, LatticeType

config = SimulationConfig(
    lattice=LatticeConfig(size=32, j1=1.0),
    temperatures=(3.0, 2.269, 1.5),
    n_sweeps=1000,
    seed=42,
)

sim = Simulation(config)
results = sim.run()

# Access results per temperature
for T in results.temperatures:
    print(f"T={T:.3f}: <E>={results.energy[T].mean():.4f}, "
          f"<|M|>={abs(results.magnetization[T]).mean():.4f}")

Multiple Lattice Types

from mcising import LatticeType

# Triangular lattice with J1-J2 frustration
config = SimulationConfig(
    lattice=LatticeConfig(
        lattice_type=LatticeType.TRIANGULAR,
        size=32,
        j1=1.0,
        j2=0.5,
    ),
    temperatures=(4.0, 3.641, 2.0),
    n_sweeps=1000,
)

# Also available: HONEYCOMB, CUBIC, CHAIN

Parallel Execution

from mcising import ExecutionMode

# Independent: each temperature runs in parallel (uses all CPU cores)
config = SimulationConfig(
    lattice=LatticeConfig(size=32),
    temperatures=(3.0, 2.5, 2.269, 2.0, 1.5),
    n_sweeps=1000,
    mode=ExecutionMode.INDEPENDENT,  # one temperature per core
)

# Parallel Tempering: parallel + replica swap for better sampling
config = SimulationConfig(
    lattice=LatticeConfig(size=32),
    temperatures=(3.0, 2.5, 2.269, 2.0, 1.5),
    n_sweeps=1000,
    mode=ExecutionMode.PARALLEL_TEMPERING,
)

Adaptive Mode

For large lattices near the critical temperature, enable adaptive measurement to automatically determine thermalization length and measurement spacing:

from mcising import AdaptiveConfig

config = SimulationConfig(
    lattice=LatticeConfig(size=64),
    temperatures=(3.0, 2.269, 1.5),
    adaptive=AdaptiveConfig(enabled=True, min_independent_samples=200),
    seed=42,
)

results = Simulation(config).run()

# Inspect diagnostics
for T in results.temperatures:
    diag = results.adaptive_diagnostics[T]
    print(f"T={T:.3f}: tau_int={diag.tau_int:.1f}, "
          f"interval={diag.measurement_interval}")

CLI

# Basic run
mcising run -L 32 --seed 42 -o results.h5

# Triangular lattice with parallel tempering
mcising run -L 32 --lattice triangular --mode parallel_tempering

# Independent parallel execution (uses all CPU cores)
mcising run -L 32 --mode independent -T 3.0 -T 2.269 -T 1.5

# Adaptive mode
mcising run -L 64 --adaptive --min-samples 200 --seed 42

# With checkpointing (crash-safe)
mcising run -L 32 --checkpoint sim.h5

# Resume interrupted run
mcising run -L 32 --checkpoint sim.h5 --resume

# Benchmark performance across all lattices and algorithms
mcising benchmark

# Show info
mcising info

Saving Results

from mcising import save_hdf5, load_hdf5, save_json_summary

# HDF5 (full data)
save_hdf5(results, "results.h5")
loaded = load_hdf5("results.h5")

# JSON summary (statistics only)
save_json_summary(results, "summary.json")

Architecture

mcising/
├── rust/src/              # Rust core (compiled to mcising._core)
│   ├── algorithm/         # MC algorithms (Metropolis, Wolff, Swendsen-Wang)
│   ├── autocorrelation.rs # MSER + Sokal windowing
│   ├── lattice/           # Lattice geometries (square, triangular, honeycomb, cubic, chain)
│   ├── observables.rs     # Energy, magnetization, correlation
│   ├── parallel.rs        # Rayon-parallelized execution (independent + parallel tempering)
│   └── simulation.rs      # PyO3 boundary (IsingSimulation)
├── python/mcising/        # Python package
│   ├── simulation.py      # High-level Simulation class
│   ├── config.py          # Frozen dataclass configs
│   ├── io.py              # HDF5/JSON I/O
│   ├── plotting.py        # Matplotlib visualization
│   └── cli.py             # Typer CLI
├── tests/                 # 401 tests (141 Rust + 260 Python)
└── benchmarks/            # Reproducible performance comparisons

License

This project is licensed under the MIT License.

Project links

Data verified by PyPI on Sep 1, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Newer release available (1.0.0)

1 maintainer

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Avatar for bcivitcioglu from gravatar.com bcivitcioglu

Credits

Author: Burak Civitcioglu

GitHub Statistics

Data verified by PyPI on Sep 1, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python >=3.10

Provides Extra

dataframe plot

Tags

ising-model monte-carlo statistical-mechanics physics simulation frustrated-magnetism

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Release history Release notifications | RSS feed

1.0.0

21 files

0.30.0

21 files

This release

0.29.0 This release

21 files

0.28.0

21 files

0.27.0

21 files

0.26.0

21 files

0.25.0

21 files

0.24.0

21 files

0.23.0

21 files

0.22.0

21 files

0.21.0

1 file

0.20.0

1 file

0.19.0

1 file

0.18.0

1 file

0.13

2 files

0.12

2 files

0.11

2 files

0.1

2 files

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