SMDP-Based Dynamic Batching for Efficient Inference on GPU-Based Platforms
This work addresses the problem of optimizing batching for efficient and economical machine learning inference on cloud or edge platforms, which is incremental as it builds on existing SMDP and queueing models.
The paper tackles the trade-off between efficiency and latency in GPU-based inference services by modeling dynamic batching as a semi-Markov decision process (SMDP) to minimize a weighted sum of average response time and power consumption, achieving reductions in space and time complexity by 63.5% and 98%, respectively, and showing that the policies adapt well to varying traffic intensities.
In up-to-date machine learning (ML) applications on cloud or edge computing platforms, batching is an important technique for providing efficient and economical services at scale. In particular, parallel computing resources on the platforms, such as graphics processing units (GPUs), have higher computational and energy efficiency with larger batch sizes. However, larger batch sizes may also result in longer response time, and thus it requires a judicious design. This paper aims to provide a dynamic batching policy that strikes a balance between efficiency and latency. The GPU-based inference service is modeled as a batch service queue with batch-size dependent processing time. Then, the design of dynamic batching is a continuous-time average-cost problem, and is formulated as a semi-Markov decision process (SMDP) with the objective of minimizing the weighted sum of average response time and average power consumption. The optimal policy is acquired by solving an associated discrete-time Markov decision process (MDP) problem with finite state approximation and "discretization". By introducing an abstract cost to reflect the impact of "tail" states, the space complexity and the time complexity of the procedure can decrease by 63.5% and 98%, respectively. Our results show that the optimal policies potentially possess a control limit structure. Numerical results also show that SMDP-based batching policies can adapt to different traffic intensities and outperform other benchmark policies. Furthermore, the proposed solution has notable flexibility in balancing power consumption and latency.