Genesis-GAL: Multiplatform Core Architecture (C++, Kotlin, Python) for CPU Thermal Optimization & Jitter Mitigation

Hi everyone!

I want to share the v1.0.0 Alpha release of Génesis-GAL (Project PX-GEN-2026-ALPHA), an open-source multiplatform ecosystem designed to mitigate OS scheduling jitter, optimize thread alignment, and reduce CPU thermal noise.

🏛️ Ecosystem Architecture

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  Windows Tuner (C++): Modifies process affinity in real-time using native Windows APIs (kernel32.dll) to isolate core execution from background OS noise, injecting a dynamic temporal absorption buffer. In benchmark testing on Intel desktop environments, it successfully stabilized effective base frequencies at 0.79 GHz (763 MHz) with clean core voltages around 1.157V, maintaining core temperatures strictly between 34°C - 36°C.


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  Android Resonance (Kotlin & C++ JNI): Deploys an indestructible foreground background service (START_STICKY) interacting with the Linux Kernel via sched_setaffinity() to lock execution loops onto the primary efficiency core (ARM Core 0). This approach managed to stabilize chassis temperatures down to 27°C.


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  Simulation Layer (Python): Uses ctypes for high-resolution bus coherence telemetry monitoring.


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  Mathematical Validation: The technical documentation includes structural implementations of the Allan Variance to mathematically evaluate system clock stability.

⚠️ System Note: Because this engine operates on a microsecond cronometric scale, high-speed solid-state storage (SSD / M.2 NVMe) is strictly required to prevent I/O bottlenecks.

📦 Open Source Release

The project is fully open-source under the MIT License. You can check out the source code, review the architecture, or download the latest binaries directly from my repository:

👉 GitHub Repository: JUANCULAJAY/Genesis-GAL-Core-Architecture

I would love to get your feedback on this low-level thread alignment approach, architectural improvements, or your own experiences utilizing Allan Variance for evaluating software runtime stability.

Let's discuss down in the comments! 🚀