# LOVE4ALL GPU SWARM - Prior Art Deposit **DOI: 10.5281/zenodo.18625293**https://zenodo.org/records/18625293 ## Title**LOVE4ALL: Peer-to-Peer Cross-Platform GPU Resource Pooling System with End-to-End Encryption for Community Computing** ## AuthorOchej, Stephane (@leDoctorBeat) x Christophe CornicORCID: 0009-0000-8201-5537 ## DateFebruary 12, 2026 ## Abstract This document establishes prior art for LOVE4ALL GPU Swarm, a peer-to-peer distributed computing system enabling automatic discovery, pooling, and sharing of heterogeneous GPU resources across a local area network (LAN) without centralized infrastructure. The system introduces the following novel combination of features: 1. **Zero-Configuration P2P GPU Discovery**: Automatic detection of computing nodes via UDP broadcast (port 47020) with WebSocket fallback and ARP table scanning, requiring no central server or manual configuration. 2. **Cross-Platform Heterogeneous GPU Pooling**: Unified resource pool combining NVIDIA CUDA GPUs, Apple Silicon MPS (M1/M2/M3), AMD GPUs, Intel integrated graphics, and CPU-only nodes within a single managed swarm, regardless of operating system (Windows, macOS, Linux). 3. **User-Controlled Resource Allocation**: Real-time slider-based interface allowing each node operator to define the percentage of GPU, CPU, and RAM resources shared with the swarm, from minimal contribution to full dedication (e.g., headless Raspberry Pi nodes contributing 100%). 4. **End-to-End Encrypted Resource Sharing**: Automatic TLS certificate generation (RSA 2048-bit, self-signed) for all peer-to-peer WebSocket connections, with graceful fallback for compatibility. 5. **Cryptographic Output Verification**: SHA-256 signatures on all resource metrics and allocation decisions, providing an auditable trail of swarm operations. 6. **Scalable Architecture**: Designed for communities, SMEs, enterprises, and research institutions to pool existing heterogeneous hardware into a shared compute cluster, eliminating cloud dependency and reducing GPU infrastructure costs by leveraging idle local resources. ## Technical Architecture ### Network Layer- UDP broadcast discovery (5-second intervals)- P2P WebSocket connections (TLS 1.2+)- Heartbeat monitoring (10-second intervals, 30-second timeout)- LAN scan fallback (ARP table + port probing) ### GPU Detection Layer- NVIDIA: pynvml / nvidia-smi- Apple Silicon: torch.backends.mps + system_profiler- AMD: rocm-smi (Linux) + WMI Win32_VideoController (Windows)- Intel HD/UHD/Iris/Arc: WMI Win32_VideoController (Windows)- CPU fallback: psutil ### Resource Management Layer- GPU Allocation Manager: task-to-GPU mapping with memory tracking- GPU Memory Pool: per-GPU memory management with task-level granularity- GPU Load Balancer: least-loaded allocation with automatic migration suggestions- GPU Monitor: cross-platform metrics collection (utilization, temperature, power) ### Application Layer- Tkinter GUI with real-time node cards- Background operation mode (system tray)- Single-instance protection (port binding)- Batch installer for all dependencies ## Claims of Novelty The following combination has not been previously demonstrated in a single integrated system: 1. **LAN-first P2P architecture** for GPU sharing (existing solutions: cloud-based marketplaces like Vast.ai, or centralized frameworks like Ray requiring manual cluster setup) 2. **Heterogeneous GPU unification** across NVIDIA CUDA, Apple MPS, AMD ROCm, and Intel iGPU within a single auto-discovered swarm (existing solutions handle single vendor only) 3. **GUI-based user-controlled allocation sliders** for resource sharing at individual, community, and enterprise scale (existing solutions: CLI-only or fixed allocation) 4. **Zero-configuration encrypted deployment** requiring only a batch file execution, enabling non-technical users and IT departments to deploy instantly (existing solutions require SSH keys, cluster configuration, or cloud accounts) 5. **Cryptographic verification of resource metrics** via SHA-256 signatures for audit compliance ## Prior Art Context This work is part of LOVE4ALL ecosystem, which includes:- LACF structural transfer / "Chérie-iser" (DOI: 10.5281/zenodo.18622921) - Transfert structurel de principes éthiques immuables entre modèles linguistiques- LACF stress test (DOI: 10.5281/zenodo.18620682)- AOED (Alignement Ontologique par Equations de Dialogues) methodology- Cherie OS distributed AI operating system ## Rights All rights reserved. This document establishes prior art and date of invention.Reproduction requires written consent from the author. ## Keywords P2P GPU sharing, distributed computing, cross-platform GPU pooling, LAN resource sharing, heterogeneous GPU swarm, community computing, end-to-end encryption, zero-configuration deployment, LOVE4ALL, KoR, prior art

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