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deepmind.google·6d ago
AI Engineering
Hugging Face Blog·May 14·5 min read
Unlocking asynchronicity in continuous batching
TL;DR
Synchronous batching leads to inefficiencies in GPU utilization during inference, as the CPU and GPU wait on each other. Asynchronous batching was developed to allow CPU batch preparation and GPU computation to occur simultaneously.
✦ Why It Matters
Engineers can implement asynchronous batching to maximize GPU efficiency and reduce inference costs.
Key Takeaways
- Implement asynchronous batching to reduce idle time in inference.
- Use CUDA streams to manage concurrent CPU and GPU operations.
- Avoid using default streams to maintain concurrency.
- Utilize memory pools to manage multiple input/output buffers efficiently.
How It Works
Asynchronous batching allows the CPU to prepare the next batch while the GPU processes the current one. This is achieved using CUDA streams, which enable concurrent execution of operations.
By recording events in the streams, the system ensures that operations occur in the correct order without blocking the CPU. This method effectively eliminates idle time, maximizing GPU utilization.