A breakthrough protocol architecture for ultra-low-latency, high-bandwidth interconnects powering AI superclusters and quantum simulation networks.
This protocol is the backbone of next-generation computation — beyond TCP/IP, beyond RDMA. It enables microsecond-scale data propagation, predictive routing, and hardware-level orchestration across AI/ML, HPC, and quantum-hybrid clusters.
- Ultra-Low Latency: Microsecond-scale data propagation
- Predictive Routing: ML-enhanced path optimization
- Hardware-Level Orchestration: Direct hardware signature mapping
- Fault Tolerance: Auto self-healing interconnect clusters
- Zero-Copy Buffers: Memory-efficient data transfer simulation
- Quantum-Aware: Support for QPU entanglement message routing
pip install hyper-fabric-interconnectfrom hyperfabric import HyperFabricProtocol, NodeSignature
# Initialize the protocol
protocol = HyperFabricProtocol()
# Register a virtual node
node = NodeSignature(
node_id="gpu-cluster-01",
hardware_type="nvidia-h100",
bandwidth_gbps=400,
latency_ns=100
)
protocol.register_node(node)
# Send data with predictive routing
await protocol.send_data(
source="gpu-cluster-01",
destination="qpu-fabric-02",
data=large_tensor,
priority="ultra_high"
)# Ping fabric nodes
hfabric ping gpu-cluster-01
# View topology
hfabric topo --visualize
# Run diagnostics
hfabric diagnose --fullFull documentation is available at GitHub Pages
- AI Supercluster Communication: Synchronizing transformer model shards across distributed GPUs
- Quantum-Enhanced AI: Routing QPU entanglement messages for hybrid classical-quantum computation
- HPC Workloads: Ultra-low latency scientific simulation data exchange
- Edge Computing: Adaptive cyber-physical compute swarm coordination
Krishna Bajpai
Email: [email protected]
GitHub: @krish567366
This project is licensed under the MIT License - see the LICENSE file for details.
Contributions are welcome! Please feel free to submit a Pull Request.