HFLabs System Architecture
High-assurance architectural principles, memory hierarchies, graph representations, and execution substrates.
HFLabs System Architecture
The HFLabs platform provides high-assurance, sub-millisecond vector cognition, swarm metaheuristics, and hardware acceleration in a unified .NET 10 environment without external cloud dependencies.
Three-Division Architecture
The codebase is organized into three specialized engineering divisions:
┌─────────────────────────────────────────────────────────────────────────┐
│ HFLabs System Architecture │
├───────────────────────────┬─────────────────────────┬───────────────────┤
│ 1. Cognition & Swarms │ 2. ML & Acceleration │ 3. Hardware & L0 │
│ • Hypertensor GraphRAG │ • AutoMLApp Node Graph │ • LevelZero.NET │
│ • HyperSwarm Suite │ • ML Suite (HPO/KGE/LLM)│ • Kernel Catalog │
│ • SwarmDev Pipeline │ • Accelerator Engine │ • Poplar.NET │
│ • FastMCP 65+ Tool Server │ • IPU SQL Query Accel │ • ABIGen Codegen │
└───────────────────────────┴─────────────────────────┴───────────────────┘Shared Operating Models
1. In-Memory Graph & Telemetry Model
Every product in the suite shares a common graph substrate and telemetry bus. Nodes, edges, tensors, and execution metrics flow through typed interfaces (IKnowledgeGraph, ITelemetrySidecar, PlanEnvelope), allowing cross-product composition.
2. Dual Parallelism Patterns
To optimize for different hardware architectures, HFLabs enforces explicit parallel models:
- SPMD (Single Program, Multiple Data): Applied across Intel GPUs and SIMD vector lanes via LevelZero.NET and TensorPrimitives.
- BSP (Bulk-Synchronous Parallel): Applied on Graphcore IPUs, dividing computation into strict compute-exchange-synchronize supersteps.
Hardware Tiering Hierarchy
Tier 0: CPU SIMD Vector Registers (AVX-512 / Neon) [Baseline, 100% available]
Tier 1: Intel Level Zero GPUs (Iris Xe, Arc, Meteor Lake, Data Center GPU Max)
Tier 2: Graphcore IPU Tiles (Colossus MK2 GC200 via Poplar SDK)
Tier 3: Distributed Network Nodes via gRPC PlanEnvelope & RDMA fabricTelemetry Sidecars
Rather than injecting intrusive profiling hooks into hot compute loops, HFLabs uses sidecar observer graphs. These passive observers monitor execution time, memory pressure, and bandwidth, predicting bottlenecks and suggesting offload reallocations without halting the primary computation.