Open Sourcing a High Performance Rust-based Ballistics Engine

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From SaaS to Open Source: The Evolution of a Ballistics Engine

When I first built Ballistics Insight, my ML-augmented ballistics calculation platform, I faced a classic engineering dilemma: how to balance performance, accuracy, and maintainability across multiple platforms. The solution came in the form of a high-performance Rust core that became the beating heart of the system. Today, I'm excited to share that journey and announce the open-sourcing of this engine as a standalone library with full FFI bindings for iOS and Android.

The Genesis: A Python Problem

The story begins with a Python Flask application serving ballistics calculations through a REST API. The initial implementation worked well enough for proof-of-concept, but as I added more sophisticated physics models—Magnus effect, Coriolis force, transonic drag corrections, gyroscopic precession—the performance limitations became apparent. A single trajectory calculation that should take milliseconds was stretching into seconds. Monte Carlo simulations with thousands of iterations were becoming impractical.

The Python implementation had another challenge: code duplication. I maintained separate implementations for atmospheric calculations, drag computations, and trajectory integration. Each time I fixed a bug or improved an algorithm, I had to ensure consistency across multiple code paths. The maintenance burden was growing exponentially with the feature set.

The Rust Revolution

The decision to rewrite the core physics engine in Rust wasn't taken lightly. I evaluated several options: optimizing the Python code with NumPy vectorization, using Cython for critical paths, or even moving to C++. Rust won for several compelling reasons:

  1. Memory Safety Without Garbage Collection: Ballistics calculations involve extensive numerical computation with predictable memory patterns. Rust's ownership system eliminated entire categories of bugs while maintaining deterministic performance.

  2. Zero-Cost Abstractions: I could write high-level, maintainable code that compiled down to assembly as efficient as hand-optimized C.

  3. Excellent FFI Story: Rust's ability to expose C-compatible interfaces meant I could integrate with any platform—Python, iOS, Android, or web via WebAssembly.

  4. Modern Tooling: Cargo, Rust's build system and package manager, made dependency management and cross-compilation straightforward.

The results were dramatic. Atmospheric calculations went from 4.5ms in Python to 0.8ms in Rust—a 5.6x improvement. Complete trajectory calculations saw 15-20x performance gains. Monte Carlo simulations that previously took minutes now completed in seconds.

Architecture: From Monolith to Modular

The closed-source Ballistics Insight platform is a sophisticated system with ML augmentations, weather integration, and a comprehensive ammunition database. It includes features like:

  • Neural network-based BC (Ballistic Coefficient) prediction
  • Regional weather model integration with ERA5, OpenWeather, and NOAA data
  • Magnus effect auto-calibration based on bullet classification
  • Yaw damping prediction using gyroscopic stability factors
  • A database of 2,000+ bullets with manufacturer specifications

For the open-source release, I took a different approach. Rather than trying to extract everything, I focused on the core physics engine—the foundation that makes everything else possible. This meant:

  1. Extracting Pure Physics: I separated the deterministic physics calculations from the ML augmentations. The open-source engine provides the fundamental ballistics math, while the SaaS platform layers intelligent corrections on top.

  2. Creating Clean Interfaces: I designed a new FFI layer from scratch, ensuring that iOS and Android developers could easily integrate the engine without understanding Rust or ballistics physics.

  3. Building Standalone Tools: The engine includes a full-featured command-line interface, making it useful for researchers, enthusiasts, and developers who need quick calculations without writing code.

The FFI Challenge: Making Rust Speak Every Language

One of my primary goals was to make the engine accessible from any platform. This meant creating robust Foreign Function Interface (FFI) bindings that could be consumed by Swift, Kotlin, Java, Python, or any language that can call C functions.

The FFI layer presented unique challenges:

#[repr(C)]
pub struct FFIBallisticInputs {
    pub muzzle_velocity: c_double,        // m/s
    pub ballistic_coefficient: c_double,
    pub mass: c_double,                   // kg
    pub diameter: c_double,               // meters
    pub drag_model: c_int,                // 0=G1, 1=G7
    pub sight_height: c_double,           // meters
    // ... many more fields
}

I had to ensure: - C-compatible memory layouts using #[repr(C)] - Safe memory management across language boundaries - Graceful error handling without exceptions - Zero-copy data transfer where possible

The result is a library that can be dropped into an iOS app as a static library, integrated into Android via JNI, or called from Python using ctypes. Each platform sees a native interface while the Rust engine handles the heavy lifting.

The Mobile Story: Binary Libraries for iOS and Android

Creating mobile bindings required careful consideration of each platform's requirements:

iOS Integration

For iOS, I compile the Rust library to a universal static library supporting both ARM64 (devices) and x86_64 (simulator). Swift developers interact with the engine through a bridging header:

let inputs = FFIBallisticInputs(
    muzzle_velocity: 823.0,
    ballistic_coefficient: 0.475,
    mass: 0.0109,
    diameter: 0.00782,
    // ...
)

let result = ballistics_calculate_trajectory(&inputs, nil, nil, 1000.0, 0.1)
defer { ballistics_free_trajectory_result(result) }

print("Max range: \(result.pointee.max_range) meters")

Android Integration

For Android, I provide pre-compiled libraries for multiple architectures (armeabi-v7a, arm64-v8a, x86, x86_64). The engine integrates seamlessly through JNI:

class BallisticsEngine {
    external fun calculateTrajectory(
        muzzleVelocity: Double,
        ballisticCoefficient: Double,
        mass: Double,
        diameter: Double,
        maxRange: Double
    ): TrajectoryResult

    companion object {
        init {
            System.loadLibrary("ballistics_engine")
        }
    }
}

Performance: The Numbers That Matter

The open-source engine achieves remarkable performance across all platforms:

  • Single Trajectory (1000m): ~5ms
  • Monte Carlo Simulation (1000 runs): ~500ms
  • BC Estimation: ~50ms
  • Zero Calculation: ~10ms

These numbers represent pure computation time on modern hardware. The engine uses RK4 (4th-order Runge-Kutta) integration by default for maximum accuracy, with an option to switch to Euler's method for even faster computation when precision requirements are relaxed.

Advanced Physics: More Than Just Parabolas

While the basic trajectory of a projectile follows a parabolic path in a vacuum, real-world ballistics is far more complex. The engine models:

Aerodynamic Effects

  • Velocity-dependent drag using standard drag functions (G1, G7) or custom curves
  • Transonic drag rise as projectiles approach the speed of sound
  • Reynolds number corrections for viscous effects at low velocities
  • Form factor adjustments based on projectile shape

Gyroscopic Phenomena

  • Spin drift from the Magnus effect on spinning projectiles
  • Precession and nutation of the projectile's axis
  • Spin decay over the flight path
  • Yaw of repose in crosswinds

Environmental Factors

  • Coriolis effect from Earth's rotation (critical for long-range shots)
  • Wind shear modeling with altitude-dependent wind variations
  • Atmospheric stratification using ICAO standard atmosphere
  • Humidity effects on air density

Stability Analysis

  • Dynamic stability calculations
  • Pitch damping coefficients through transonic regions
  • Gyroscopic stability factors
  • Transonic instability warnings

The Command Line Interface: Power at Your Fingertips

The engine includes a comprehensive CLI that rivals commercial ballistics software:

# Basic trajectory with auto-zeroing
./ballistics trajectory -v 2700 -b 0.475 -m 168 -d 0.308 \
  --auto-zero 200 --max-range 1000

# Monte Carlo simulation for load development
./ballistics monte-carlo -v 2700 -b 0.475 -m 168 -d 0.308 \
  -n 1000 --velocity-std 10 --bc-std 0.01 --target-distance 600

# Estimate BC from observed drops
./ballistics estimate-bc -v 2700 -m 168 -d 0.308 \
  --distance1 100 --drop1 0.0 --distance2 300 --drop2 0.075

The CLI supports both imperial (default) and metric units, multiple output formats (table, JSON, CSV), and can enable individual physics models as needed.

Lessons Learned: The Open Source Journey

Extracting and open-sourcing a core component from a larger system taught me valuable lessons:

  1. Clear Boundaries Matter: Separating deterministic physics from ML augmentations made the extraction cleaner and the resulting library more focused.

  2. Documentation is Code: I invested heavily in documentation, from inline Rust docs to comprehensive README examples. Good documentation dramatically increases adoption.

  3. Performance Benchmarks Build Trust: Publishing concrete performance numbers helps users understand what they're getting and sets realistic expectations.

  4. FFI Design is Critical: A well-designed FFI layer makes the difference between a library that's theoretically cross-platform and one that's actually used across platforms.

  5. Community Feedback is Gold: Early users found edge cases I never considered and suggested features that made the engine more valuable.

The Website: ballistics.rs

To support the open-source project, I created ballistics.rs, a dedicated website that serves as the central hub for documentation, downloads, and community engagement. Built as a static site hosted on Google Cloud Platform with global CDN distribution, it provides fast access to resources from anywhere in the world.

The website showcases: - Comprehensive documentation and API references - Platform-specific integration guides - Performance benchmarks and comparisons - Example code and use cases - Links to the GitHub repository and issue tracker

Looking Forward: The Future of Open Ballistics

Open-sourcing the ballistics engine is just the beginning. I'm excited about several upcoming developments:

  1. WebAssembly Support: Bringing high-performance ballistics calculations directly to web browsers.

  2. GPU Acceleration: For massive Monte Carlo simulations and trajectory optimization.

  3. Extended Drag Models: Supporting more specialized drag functions for specific projectile types.

  4. Community Contributions: I'm already seeing pull requests for new features and improvements.

  5. Educational Resources: Creating interactive visualizations and tutorials to help people understand ballistics physics.

The Business Model: Open Core Done Right

My approach follows the "open core" model. The fundamental physics engine is open source and will always remain so. The value-added features in Ballistics Insight—ML augmentations, weather integration, ammunition databases, and the web API—constitute our commercial offering.

This model benefits everyone: - Developers get a production-ready ballistics engine for their applications - Researchers have a reference implementation for ballistics algorithms - The community can contribute improvements that benefit all users - I maintain a sustainable business while giving back to the open-source ecosystem

Conclusion: Precision Through Open Collaboration

The journey from a closed-source SaaS platform to an open-source library with mobile bindings represents more than just a code release. It's a commitment to the principle that fundamental scientific calculations should be open, verifiable, and accessible to all.

By open-sourcing the ballistics engine, I'm not just sharing code—I'm inviting collaboration from developers, researchers, and enthusiasts worldwide. Whether you're building a mobile app for hunters, creating educational software for physics students, or conducting research on projectile dynamics, you now have access to a battle-tested, high-performance engine that handles the complex mathematics of ballistics.

The combination of Rust's performance and safety, comprehensive physics modeling, and carefully designed FFI bindings creates a unique resource in the ballistics software ecosystem. I'm excited to see what the community builds with it.

Visit ballistics.rs to get started, browse the documentation, or contribute to the project. The repository is available on GitHub, and I welcome issues, pull requests, and feedback.

In the world of ballistics, precision is everything. With this open-source release, I'm putting that precision in your hands.