Unlocking asynchronicity in continuous batching

This article explains how to achieve asynchronous GPU computation in continuous batching to eliminate CPU-GPU idle time and maximize inference throughput.

• •Synchronous batching wastes up to 24% of GPU time because CPU and GPU work sequentially rather than in parallel
• •CUDA streams enable concurrent execution by allowing independent GPU operations to run simultaneously
• •Three separate streams (H2D, compute, D2H) are needed to handle data transfer and computation independently
• •Non-default streams return CPU control immediately instead of blocking until GPU computation completes
• •Asynchronous batching allows batch preparation for the next iteration while the current batch is computing

This summary was automatically generated by AI based on the original article and may not be fully accurate.