Unlocking asynchronicity in continuous batching
AI Summary
This article explores how separating CPU and GPU workloads during continuous batching in LLM inference eliminates idle gaps and boosts performance. By default, continuous batching is synchronous, causing the CPU and GPU to wait for one another and wasting nearly 24% of total runtime. By leveraging CUDA streams, non-blocking memory transfers, and explicit synchronization events, engineers can overlap CPU batch preparation for batch N+1 while the GPU computes batch N. This asynchronous approach saturates the GPU work queue, increasing GPU active time from 76.0% to 99.4% and delivering a 22% overall speedup without requiring new kernel or model changes.
Key Takeaways
- Synchronous continuous batching forces the CPU and GPU to take turns, resulting in idle gaps that account for nearly 24% of total runtime.
- CUDA streams and non-blocking memory transfers allow CPU batch preparation and GPU execution to run concurrently.
- Careful management of double-buffering and CUDA graphs prevents race conditions while maintaining high GPU utilization.
Why it matters
Inference hardware like the H200 is expensive, and optimizing utilization directly reduces operational costs for large-scale deployments. By removing CPU-GPU synchronization bottlenecks, engineering teams can achieve state-of-the-art throughput for long-context generation tasks like reinforcement learning without modifying underlying model architectures.
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