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Cimba Python

Fast discrete event simulation for Python

Cimba Python is a Python interface to Cimba, a multithreaded discrete event simulation engine written in C and assembly.

It is designed for Python simulation models that need more speed than pure Python event scheduling can usually provide. In the included M/M/1 benchmark, Cimba Python runs about 24.5–31.3x faster than SimPy after its one-time Numba compile, while keeping model code in Python.

On an AMD Ryzen 7 9700X under WSL Ubuntu 24.04, averaged over 10 runs:

Benchmark SimPy Cimba Python Cimba C
Single core, single trial 2.856 s 0.117 s 0.078 s
Multicore, 100 trials 38.723 s 1.238 s 0.800 s

The benchmark data and charts are in benchmark/AMD_Ryzen_7_9700X_WSL.ods.

Install

pip install cimba

or with uv:

uv add cimba

Python 3.13 or newer is required. The Linux, Windows, and macOS wheels embed our fork of Cimba 3.0.0-RC2, so you do not need to install Cimba separately. Native macOS wheels are available for Apple Silicon. Numba does not currently publish the required llvmlite wheels for Intel Macs.

The bindings manage the strict native object lifecycle required since Cimba 3.0.0 RC1: initialize before use, terminate after use, and destroy allocated objects. This includes temporary statistics objects and spawned processes. Abandoned trials also release the bindings' spawned-process registry before the worker runs another trial. Python models do not need manual lifecycle calls.

What is it?

Cimba Python gives Python models access to Cimba's native simulation engine through the cimba.sim API: processes, event queues, buffers, queues, stores, priority queues, resources, resource pools, conditions, timers, events, logging helpers, and experiment tables. Random distributions live in cimba.random.

What does the code look like?

import cimba.sim as sim
import cimba.random as random


class MM1(sim.Model):
    utilization: sim.Param
    avg_queue_length: sim.Output
    queue: sim.Queue

    @sim.process
    def arrival(self: "MM1"):
        while True:
            sim.hold(random.exponential(1.0 / self.utilization))
            self.queue.put(1)

    @sim.process
    def service(self: "MM1"):
        while True:
            self.queue.get(1)
            sim.hold(random.exponential(1.0))

    @sim.collect
    def collect_stats(self: "MM1"):
        self.avg_queue_length = self.queue.mean_level()


model = MM1("MM1")

exp = model.experiment(
    utilization=0.75,
    replications=100,
    duration=1000.0,
    warmup=100.0,
    seed=123,
)
exp.run()

print(exp.results.avg_queue_length.mean())

More examples, tutorials, background notes, and the API reference are in the documentation.

Development

From a fresh clone:

uv sync
uv run pytest

License

Cimba Python is licensed under Apache-2.0. See LICENSE and NOTICE.

The bundled Cimba C library is also Apache-2.0 licensed. See subprojects/cimba/NOTICE for attribution.

Project links

Data verified by PyPI on Sep 6, 2026
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Key dates

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Credits

Author: Francisco Barragán Castro

GitHub Statistics

Data verified by PyPI on Sep 6, 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

Apache-2.0
View SPDX License List

Requires

Python >=3.13

Provides Extra

plot

Tags

simulation discrete-event-simulation cimba numba

Classifiers

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