PJHkorea/adiabatic-silicon-aging-guard

Experimental 3-tier silicon failure-fencing engine isolating volatile NaN/Inf bleeding via runtime CPython method table interception and timing-frozen JAX shard_map topologies. - PJHkorea/adiabatic...

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🛡️ adiabatic-silicon-aging-guard (PoC Whitepaper) This repository contains the foundational architectural blueprint and experimental Proof of Concept (PoC) for an Adiabatic Silicon Aging & Thermal Degradation Failure-Fencing Engine. This project represents an exploratory attempt to isolate volatile NaN/±∞ algebraic bleeding inside hyperscale accelerator clusters (simulated up to 10⁵ GPUs boundaries) without triggering unexpected XLA compiler cache re-evaluation loops or host-side synchronization stalls. By bridging runtime electromigration sensor register bits with multi-axis jax.experimental.shard_map topologies and inline single-clock PTX predicate switches, we investigate feasibility methods for dynamically mutating tensor address layouts adiabatically (entropy-preserving node shifts) under simulated hardware aging failures up to an 85% localized hardware blackout threshold.

🌊 Architectural Philosophy: Thermodynamic Entropy vs. Immutable Compilation Graphs In hyper-distributed AI training infrastructures, the primary bottleneck governing system lifespan is no longer isolated power grids, but Silicon Aging (Electromigration) and Thermal Drift within sub-2nm process nodes. As streaming multiprocessors (SM) operate under continuous high-occupancy float operations, individual execution blocks or High-Bandwidth Memory (HBM) lanes inevitably encounter timing violations, spawning catastrophic 1-bit NaN leakage that can contaminate the entire automatic differentiation pipeline. Traditional cluster-level failover solutions (such as SLURM or PyTorch TorchElastic) often rely on catastrophic interruption: throwing a host-side signal, tearing down the MPI mesh, destroying the CUDA contexts, reclaiming memory buffers, and reading a multi-gigabyte disk checkpoint to execute an Ahead-of-Time (AOT) re-compilation. This legacy routine induces an expensive Recompilation Stall and severe power-grid thermal spikes. The adiabatic-silicon-aging-guard project proposes a theoretical paradigm to mitigate this:

Adiabatic Wavefront Shifting (Experimental): We model localized hardware core failures not as discrete system crashes, but as a continuous fluidic degradation field. Using localized warp-level crossbar registers, this PoC explores how live numerical manifolds can be translated away from fading nodes smoothly with minimal loss of computational state. Timing-Frozen Memory Virtualization: Instead of mutating the static XLA tracer shape layout during failure events, the runtime engine enforces an immutable compilation boundary. High-level graphs remain frozen inside the instruction registers, while the underlying raw 64-bit VRAM pointers are dynamically masked using algebraic primitives to minimize runtime overhead.

🧬 Triple-Layer Sundered Control Plane (Proposed Architecture) To decouple physical semiconductor degradation (thermal/electromigration) from the computation graph, this PoC explores a 3-tier, strictly fenced structure designed to isolate failures without full-system interruption:

Layer 1: Bare-Metal Silicon Intercept Kernel (aging_guard_core.cu)

Executes at the hardware register level to evaluate low-overhead telemetry. Uses __ballot_sync for warp-synchronous telemetry aggregation and inline selp.b32 PTX assembly for predicate-driven, branchless register muting. Investigates mechanisms to mask degraded channels (e.g., setting to 0.0f) while shifting active workloads to healthy lanes to mitigate hardware stalls.

Layer 1.5: Asynchronous Lifecycle Capsule Fence (aging_bridge_wrapper.cpp)

Manages the interface boundary between raw hardware registers and higher-level runtimes. Explores minimal-overhead pointer manipulation, atomic memory alignment, and RAII hardware fences with Python GIL release to mitigate host-side garbage collection (GC) noise.

Layer 2: Multi-Node Dynamic Shape Insulation Tower (aging_dynamic_adapter.py)

Handles cluster-wide coordination and macro-level graph stabilization. Implements experimental static graph freezing using power-of-two memory buckets. Utilizes algebraic vacuum masking (e.g., rigid int32 bit-aligned -1000000000 scaling, abstractly referenced as -1e9) to suppress failed node inputs, aiming to eliminate host-side type-promotion overhead and prevent catastrophic re-compilation loops.

📐 Technical Highlights

Memory Alignment: Enforces strict hardware data alignment using alignas(32) structures for AgingTelemetryCell to optimize memory subsystem throughput. Theoretical Formulation: Proposes an Adiabatic Gradient Combine Equation to model zero-copy, entropy-preserving communication boundaries directly at the simulated silicon layer.

📂 Repository Directory Structure & Component Matrix This repository implements the 3-tier failure-fencing architecture through the following experimental components: adiabatic-silicon-aging-guard/ ├── setup.py # Automated compiler builder for NVCC/GCC static binary compilation ├── aging_fabric_config.py # Global environment orchestrator & aging bucket specification layout ├── aging_guard_core.cu # [Layer 1] Bare-metal 1-bit predicate register MUX kernel ├── aging_bridge_wrapper.cpp # [Layer 1.5] Asynchronous GIL-release & DLPack zero-overhead pointer capsule fence ├── aging_dynamic_adapter.py # [Layer 2] Offline static graph freezing adapter via power-of-two memory buckets ├── aging_fng_orchestrator.py # [Layer 2] jax.experimental.shard_map-driven adiabatic manifold governor ├── aging_monkey_patch.py # Runtime instrumentation hook for production-grade Transformer layer interception └── test_aging_pipeline.py # Simulated benchmark suite under high-stress semiconductor thermal/aging degradation

🔬 Core Implementation Breakdown

setup.py: Automates cross-compilation boundaries between native CUDA extensions and host-side execution environments. aging_guard_core.cu & aging_bridge_wrapper.cpp: Establish the low-level interception boundary, bridging hardware-level warp synchronization directly into pythonic lifecycles. aging_fng_orchestrator.py: Investigates the runtime feasibility of moving high-dimensional live numerical tensors across simulated fading nodes without re-triggering expensive XLA compilation passes.

graph TD %% 노드 스타일 정의 classDef framework fill:#2A2A2A,stroke:#4A4A4A,stroke-width:2px,color:#FFFFFF; classDef layer2 fill:#1E293B,stroke:#38BDF8,stroke-width:2px,color:#E2E8F0; classDef layer15 fill:#111827,stroke:#A855F7,stroke-width:2px,color:#E2E8F0; classDef layer1 fill:#31100F,stroke:#EF4444,stroke-width:2px,color:#FCA5A5; classDef binary fill:#14532D,stroke:#22C55E,stroke-width:2px,color:#BBF7D0;

%% 프레임워크 계층 FW["🛡️ Commercial Framework Layer (Llama-3 / DeepSeek-V4 Backbone Rails)"]:::framework

%% 레이어 2 (런타임 하이재커 및 어댑터) subgraph L2 ["Layer 2: Python Runtime & Shape Management"] MP["🪡 aging_monkey_patch.py (Runtime Hyper-Jacker Factory)"]:::layer2 DA["📦 aging_dynamic_adapter.py (Shape Insulation Adapter) • Power-of-Two Static Buckets (64 ~ 4096) • Algebraic Vacuum Masking (0.0f / -1e9)"]:::layer2 end

%% 레이어 1.5 (C++ 브릿지) subgraph L15 ["Layer 1.5: Native Bridge"] BW["🪐 aging_bridge_wrapper.cpp (C++ PyBind11 / DLPack Capsule Fence) • Native Python GIL Release Mechanism • Warp-Synchronous Stream Wait Barrier"]:::layer15 end

%% 레이어 1 (베어메탈 CUDA 커널) subgraph L1 ["Layer 1: Bare-Metal Silicon Intercept"] GC["🛡️ aging_guard_core.cu (Silicon Intercept MUX Kernel) • 32-Bit Ballot Aggregation (__ballot_sync) • 1-Clock Branchless Prediction MUX (selp.f32) • Burgers' Spatial Laplacian Viscosity Damping"]:::layer1 end

%% 컴파일 결과물 BI["⚙️ Fused Static HLO Binary Executable (0% Graph Break / No-recompile Pass)"]:::binary

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PJHkorea/adiabatic-silicon-aging-guard: Experimental 3-tier silicon failure-fencing engine isolating volatile NaN/Inf bleeding via runtime C

PJHkorea/adiabatic-silicon-aging-guard

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