hermeneutics! Extending the NVI (Normalize, Visualize, Iterate) framework

Proposal:  Hermeneutics! Extending the NVI (Normalize, Visualize, Iterate) framework with a hermeneutic approach to assist GPU-based systems for CoreWeave and Core Scientific users, utilizing Equitus.us SmartFabric (KGNN), offers powerful advantages.

Here's how hermeneutics, through NVI, can add significant value:

Context: The Unique Challenges of GPU-Based Systems

GPU-based systems, common in CoreWeave (cloud GPUs for AI/ML, rendering) and Core Scientific (blockchain, AI, high-performance compute), present unique challenges:

• Massive Parallelism: Extremely high data throughput and concurrent operations.

• Complex Interdependencies: GPU, CPU, memory, interconnects (NVLink, InfiniBand), storage, and network all tightly coupled.

• Application-Specific Performance: Optimal configuration and performance tuning are highly dependent on the specific AI model, rendering task, or blockchain workload.

• Resource Contention: Efficient scheduling and allocation are critical for cost-effectiveness and performance.

• Thermal and Power Management: Critical for sustained high performance.

Hermeneutics provides the framework to interpret these complexities, rather than just collect data.

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1. Normalize: Contextualizing GPU Data for Meaningful Interpretation

For GPU-based systems, normalization with a hermeneutic lens means going beyond raw metrics to imbue data with context and intended meaning.

• GPU-Specific Telemetry & Context:

  • Beyond Raw Stats: Instead of just normalizing GPU utilization or memory usage, hermeneutics guides the integration of contextual data: the specific AI model running (e.g., LLM, vision model), the dataset size, the epoch number, the rendering engine, or the blockchain algorithm.

  • Interconnect Status: Normalizing NVLink, PCIe, or InfiniBand metrics alongside application data provides a holistic view.

  • Power & Thermal Profiles: Integrating power draw, temperature, and fan speed with workload type.

• Application-Aware Baselines: Hermeneutics helps define "normal" not just statistically, but meaningfully for a given application. For instance, normalizing a certain GPU memory pattern as "normal for BERT training" vs. "abnormal for Stable Diffusion rendering."

• Multi-Cloud/Multi-Cluster Integration (CoreWeave): Equitus.us SmartFabric (KGNN) normalizes data across different GPU clusters or even hybrid cloud deployments, ensuring consistent interpretation regardless of the physical location or specific hardware generation.

  • Value Add: Users gain a "linguistic" understanding of their GPU system's state. PowerGraph doesn't just show data; it presents contextually rich information, making it easier to compare performance, identify subtle shifts, and understand the implications of different operational parameters.

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2. Visualize: Unveiling Hidden GPU Interactions and Performance Narratives

This is where PowerGraph, informed by hermeneutics, translates complex normalized GPU data into intuitive, actionable visual stories.

• GPU Topology & Data Flow Graphs: Visualize the full data path: from CPU to GPU, across NVLink, through network interfaces to storage. Hermeneutics helps design these graphs to highlight bottlenecks or unexpected detours.

• Example: A visualization showing data moving inefficiently between GPUs via PCIe instead of NVLink, revealing a configuration issues