Serving Heterogeneous Machine Learning Models on Multi-GPU Servers with Spatio-Temporal Sharing

Seungbeom Choi, Sunho Lee, Yeonjae Kim, Jongse Park, Youngjin Kwon, and Jaehyuk Huh, "Serving Heterogeneous Machine Learning Models on Multi-GPU Servers with Spatio-Temporal Sharing", the 2022 USENIX Annual Technical Conference ( USENIX ATC ), July 2022

Paper Slide

As machine learning (ML) techniques are applied to a widening range of applications, high throughput ML inference serving has become critical for online services. Such ML inference servers with multiple GPUs pose new challenges in the scheduler design. First, they must provide a bounded latency for each request to support a consistent service-level objective (SLO). Second, they must be able to serve multiple heterogeneous ML models in a system, as cloud-based consolidation improves system utilization. To address the two requirements of ML inference servers, this paper proposes a new inference scheduling framework for multi-model ML inference servers. The paper shows that with SLO constraints, GPUs with growing parallelism are not fully utilized for ML inference tasks. To maximize the resource efficiency of GPUs, a key mechanism proposed in this paper is to exploit hardware support for spatial partitioning of GPU resources. With spatio-temporal sharing, a new abstraction layer of GPU resources is created with configurable GPU resources. The scheduler assigns requests to virtual GPUs, called gpulets, with the most effective amount of resources. The scheduler explores the three-dimensional search space with different batch sizes, temporal sharing,and spatial sharing efficiently. To minimize the cost for cloud-based inference servers, the framework auto-scales the required number of GPUs for a given workload. To consider the potential interference overheads when two ML tasks are running concurrently by spatially sharing a GPU, the scheduling decision is made with an interference prediction model. Our prototype implementation proves that the proposed spatio-temporal scheduling enhances throughput by 61.7% on average compared to the prior temporal scheduler, while satisfying SLOs.