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# 🚀 RFT Adaptive Computing Kernel — Technical Notes (v1.0)
The **Rendered Frame Theory (RFT) Adaptive Computing Kernel** benchmarks computational self-stabilization, throughput efficiency, and coherence across **CPU, GPU, and TPU** workloads.
It integrates RFT’s harmonic feedback system (QΩ / ζ_sync) with adaptive governors to regulate:
- **Clock scaling**
- **Thermal headroom**
- **Duty-cycle performance**
- **Noise-induced computation drift**
This system demonstrates how RFT’s harmonic framework can stabilize compute throughput and coherence across different hardware and noise environments.
---
## ⚡ Core Function
Each run simulates workloads and injects synthetic noise or load imbalance.
RFT’s adaptive controller adjusts clock scale and workload timing in real time to preserve frame-level stability.
It outputs:
| Metric | Description |
|---------|--------------|
| **QΩ** | Harmonic stability of compute cycles (amplitude equilibrium). |
| **ζ_sync** | Synchronization coherence between threads/cores. |
| **items/sec** | Estimated throughput efficiency after stability correction. |
| **status** | System condition: nominal / perturbed / critical. |
---
## 🧩 Supported Profiles
| Profile | Description |
|----------|--------------|
| **CPU** | Scalar or integer workloads — stability under linear compute load. |
| **GPU** | Parallel workloads (matrix or transform) — coherence under high variance. |
| **TPU** | Tensor workloads — synchronization in large-batch inference. |
| **Mixed / I/O** | Combines memory, disk, and network delay tests for system-level drift study. |
---
## ⚙️ Internal Dynamics
- **Adaptive Governor:** Modulates internal load scaling (clock, thread, or matrix block size).
- **Noise Control:** Applies synthetic perturbation (σ = 0.00–0.30) to simulate real-world variance.
- **Micro-Benchmark:** Runs lightweight compute cycles and reports items/sec safely.
- **Feedback Loop:** Uses QΩ/ζ_sync variance as control input for next iteration (adaptive self-correction).
- **Bounded Validation:** Metrics capped to realistic operational limits.
---
## 📈 How to Use
1. Choose **Profile** → CPU / GPU / TPU / Mixed.
2. Adjust **Noise Level (σ)** and sample count.
3. Run simulation.
4. Observe:
- **items/sec** → throughput stability
- **QΩ / ζ_sync** → harmonic state
- **status** → equilibrium condition
Repeated runs at identical σ show adaptive stability improvement.
---
## 🧮 Interpretation
| Status | Description | Expected Behavior |
|---------|--------------|-------------------|
| **Nominal** | Stable equilibrium | Items/sec consistent, QΩ ≈ ζ_sync |
| **Perturbed** | Transitional adjustment | Minor drops in throughput, recovery visible |
| **Critical** | Overload or divergence | Severe drop or incoherence detected |
---
## 🔐 Verification & Rights
All adaptive logic and governing equations are protected under **RFT-IPURL v1.0** and the **Berne Convention (UK Copyright Law)**.
All performance runs are timestamped and may be SHA-512 sealed for traceable verification.
**Author:** Liam Grinstead
**Affiliation:** Rendered Frame Theory Systems (RFTSystems)
**DOI:** [https://doi.org/10.5281/zenodo.17466722](https://doi.org/10.5281/zenodo.17466722)
**License:** RFT-IPURL v1.0 — Research validation use only. |