## Diagram: Real-time Correction System for Mis-predicted Experts in Hybrid CPU/GPU Architecture ### Overview The diagram illustrates a dynamic resource management system for handling expert computations across CPU and GPU resources. It shows workflows for: - Prefetching experts to CPU - Offloading experts to GPU - Real-time correction of mis-predicted experts - Predictive locality-aware expert caching - MoE (Mixture of Experts) computation ### Components/Axes **Legend (bottom-left):** - Red: Active Experts (on GPU) - Orange: Idle Experts (on CPU) - White: Empty Experts **Key Components:** 1. **CPU Section (left):** - Experts e11-e14 - Prefetching workflow to High/Low-Priority Queues 2. **GPU Section (right):** - MoE Compute block - Active experts e22, e23 (red) - Idle experts e21, e24 (orange) 3. **Queues:** - High-Priority Queues (top-center): Handle real-time corrections - Low-Priority Queues (bottom-center): Manage predictive caching 4. **Flow Arrows:** - Blue: Offload/prefetch operations - Red: Mis-prediction correction path - Black: Actual computation paths ### Detailed Analysis **CPU Expert States:** - Active (orange): e11, e12 - Idle (white): e13, e14 **GPU Expert States:** - Active (red): e22, e23 - Idle (orange): e21, e24 **Queue Contents:** - High-Priority Queues: - Contains e23 (red) and e24 (orange) - Shows "Offload unwanted e23" and "Fetch missed e24" operations - Low-Priority Queues: - Contains e11, e12, e21 (orange) - Shows predictive caching of e22, e14, e13 **MoE Compute Block:** - Actual computation shows: - e22, e23 (red) as active - e21, e24 (orange) as idle - Input tokens t1, t2 with mis-prediction marker (×) **Routing Path:** - Pre-scheduled experts based on predicted routing path - Shows mis-prediction in expert 2 (e2) ### Key Observations 1. **Resource Utilization:** - GPU actively uses 50% of experts (e22, e23) - CPU actively uses 50% of experts (e11, e12) 2. **Dynamic Adjustment:** - System corrects mis-predictions in real-time (e23 offload, e24 fetch) - Predictive caching pre-schedules experts but requires correction 3. **Priority Handling:** - High-priority queues handle critical corrections - Low-priority queues manage bulk prefetching 4. **Latency Management:** - Prefetching reduces GPU idle time - Real-time correction adds overhead but maintains accuracy ### Interpretation This system demonstrates a sophisticated approach to hybrid computing resource management: 1. **Predictive Caching:** The low-priority queues pre-schedule experts based on predicted routing paths, reducing initial latency but requiring real-time adjustments when mis-predictions occur. 2. **Priority Queuing:** The separation into high/low-priority queues ensures critical corrections (like expert e23 offload) get immediate attention while bulk operations (e11-e14 prefetch) proceed in lower priority. 3. **Hybrid Optimization:** The system effectively balances CPU/GPU workloads by: - Keeping 50% of experts active on each device - Dynamically redistributing experts based on actual computation needs - Maintaining 25% empty experts for potential overflow 4. **Error Handling:** The red × mis-prediction marker and correction arrows show the system's ability to adapt to prediction errors, though this introduces additional overhead. The architecture suggests a trade-off between prediction accuracy and computational efficiency, with the real-time correction mechanism acting as a safety net for imperfect predictions. The 50/50 active/expert distribution across CPU/GPU indicates a balanced workload distribution strategy, while the 25% empty expert buffer provides headroom for unexpected computation demands.