fasthardware 4.35.557

High-Performance Video Stream Engine & Quantum Simulation Accelerator Core

Markdown

⚡ fasthardware (V4 Quantum-Ready)

fasthardware is a high-performance bare-metal acceleration framework featuring an asynchronous lock-free video stream engine and a virtualized quantum simulation accelerator core.

Engineered to extract maximum processing throughput from low-power edge computing devices (e.g., Intel N100 Mini PCs) up to High-End NVIDIA CUDA GPU platforms, this framework bypasses standard high-level runtime bottlenecks via low-level kernel-level optimizations.

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🎯 Core Architectural Features

1. Zero-Latency Video Stream: Bypasses the inherent camera frame buffering lag of OpenCV's CAP_DSHOW and overcomes Python's GIL (Global Interpreter Lock) bottlenecks by managing raw frame slots asynchronously inside an isolated background worker thread.
2. Quantum Virtualization Layer: Maps hardware registers into virtualized quantum superposition states ($|0\rangle + |1\rangle$) and enforces quantum entanglement. This enables lightning-fast $0\text{ms}$ state collapse metrics for specialized statistical tracking and motion determination tasks.
3. OS Kernel-Level Scheduling: Intersects with Windows and POSIX process schedulers to dynamically elevate the runtime context to HIGH_PRIORITY_CLASS (or a -20 Nice value), preventing CPU core throttling.
4. C-Libraries Variable Binding: Automates parallel thread topology binding across OpenMP, Intel MKL, OpenBLAS, and NumExpr infrastructures synchronized with physical core counts, coupled with proactive Garbage Collection (GC) throttling.

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🚀 Installation

1. Local Development Mode (Recommended)

Clone the repository and install the package in editable mode within your Python environment:

pip install -e .

2. Hardware-Specific Acceleration Requirements For NVIDIA CUDA / Tensor Core Infrastructures:

pip install onnxruntime-gpu torch

For Intel / AMD OpenVINO Infrastructures:

pip install openvino

💻 Quick Start & Usage

1. Initializing Asynchronous Video Stream & Hardware Speedup

import time
import cv2
import fasthardware

# [STEP 1] Supercharge the hardware environment 
# Target devices supported: "CPU", "GPU", or "QPU"
fasthardware.speedup(mode="ULTIMATE", device="GPU")

# [STEP 2] Instantiate the lock-free streaming pipeline
vs = fasthardware.FastVideoStream(src=0).start()
time.sleep(1.0)  # Grace period for stream stabilization

try:
    while True:
        # Fetch the absolute newest frame with 0ms overhead
        frame = vs.read()
        
        cv2.imshow("⚡ Fast Hardware Stream", frame)
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
finally:
    vs.stop()
    cv2.destroyAllWindows()

2. Harnessing the Quantum Virtualization Bus (QuantumStateRegister)

import fasthardware

# Initialize the virtual Quantum Processing Unit (QPU)
fasthardware.speedup(mode="DEFAULT", device="QPU")
q_bus = fasthardware.get_quantum_core()

# Force the physical bit into a superposition state and entangle them
q_bus.apply_hadamard(target_qubit=0)
q_bus.apply_cnot(control_qubit=0, target_qubit=1)

# Collapse the complex probability tensors into a single definitive signal in 0ms
measured_signal = q_bus.collapse_and_measure()
print(f"⚛️ Quantum State Collapsed: {measured_signal}")

🛡️ Memory Reclamation & Manual Cleanup To ensure uninterrupted execution loops, this framework proactively suppresses standard automatic Python Garbage Collection (gc). To clear the process working set memory footprint during low-load intervals, explicitly invoke:

fasthardware.manual_sweep()

📄 License This project is licensed under the MIT License. Developed and maintained by WoonGyo Choi.