A Revolutionary Approach to Low-Energy, High-Performance Computation
MatrixFlow is a GPU-accelerated framework that replaces traditional binary logic with embedding-based computation in high-dimensional matrices. By harnessing low-energy states and extreme parallelism on NVIDIA GPUs, MatrixFlow delivers energy-efficient, ultra-fast simulations of CPU, memory, DSP, I/O and custom hardware—paving the way for a new era of software-defined “chips” built entirely from matrix operations.
- Embedding-Based Logic
Perform logic gates (AND, OR, XOR, etc.) directly within dense matrix embeddings—no transistors or branch instructions required. - Low-Energy Matrix Flow
Exploit matrix energy minima to guide computation, reducing wasted operations and power draw. - GPU Acceleration
Leverage CUDA-enabled parallelism (via CuPy, PyTorch or similar) to scale effortlessly from 8×8 up to 16 384×16 384 matrices. - Quantum-Inspired
Draw inspiration from quantum amplitude embeddings to minimize energy usage while maximizing computational throughput. - Modular Architecture
Plug-and-play submodules for CPU emulation, memory management, RAID-style disk simulation, DSP pipelines, RF modem logic and more.
- Python 3.7 or higher
- CUDA Toolkit 11.0+
- NVIDIA GPU (e.g. GeForce RTX 3060)
- CuPy (or PyTorch with CUDA support)
- NumPy, SciPy
git clone https://github.com/pirateal/MatrixFlow.git
cd MatrixFlow
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install --upgrade pip
pip install -r requirements.txt
▶️ Running a Quick Demo
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# CPU emulator test
python sandbox/templates/cpu_emulator_test.py
# Tensor-clock oscillator demo
jupyter notebook examples/tensor_clock_demo/demo.ipynb
🔨 Basic Usage
python
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import numpy as np
from matrixflow import MatrixCompute
# Define two 2×2 matrices for AND-gate simulation
A = np.array([[1, 0],
[1, 1]])
B = np.array([[1, 1],
[0, 1]])
mc = MatrixCompute(A, B)
result = mc.and_operation()
print("AND result:\n", result)
📈 Achievements to Date
Embedding-Based Logic Gates
Validated AND, OR, XOR at scales up to 16 384×16 384 matrices.
Full 8080 CPU Emulation
All ALU ops, memory reads/writes and carry-propagation handled via matrix transforms.
Matrix-Based PACTOR Modem & SDR
RF data paths simulated in parallel, supporting FT8/JT65/WSPR modes.
Matrix-Oscillator Clock
Deterministic, matrix-driven “clock” with sub-nanosecond precision benchmarks.
Matrix RAID Engine
RAID0/RAID1 via tiled matrix-disk images and GPU-accelerated parity logic.
🌌 Vision & Roadmap
Dynamic Flow Control
Real-time adaptation of matrix energy landscapes to optimize runtime efficiency.
Advanced Embeddings
Explore tensor decompositions and quantum-inspired state encodings.
Hardware Integration
Partner with FPGA/ASIC vendors to craft custom tensor-core-style silicon.
AI-Assisted Ecosystem
Auto-generated docs, example sandboxes and CI-driven index to keep MatrixFlow evolving.
🤝 Contributing
We welcome all kinds of contributions—whether it’s new matrix modules, performance optimizations, docs or example notebooks.
Fork the repo & create a descriptive branch
Adhere to PEP 8 and existing code style
Add front-matter metadata to any new docs
Write tests for new features
Open a PR referencing related issues
See CONTRIBUTING.md for full details.
📜 License
MatrixFlow is released under the MIT License. See LICENSE for details.
✉️ Contact & Acknowledgments
Questions or ideas? Open an issue or discussion on GitHub.
Thanks to the open-source ecosystem—CuPy, PyTorch, NumPy, SciPy—and to everyone pushing the frontiers of computation.
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Embedding-Based GPU Computation • Low-Energy Matrix Flow • Software-Defined Chips
MatrixFlow: Reimagining computation at the intersection of mathematics, physics and next-gen hardware.