D-STACK: high throughput DNN inference by effective multiplexing and spatio-temporal scheduling of GPUs

Abstract

Hardware accelerators such as GPUs are required for real-time, low latency inference with Deep Neural Networks (DNN). Providing inference services in the cloud can be resource intensive, and effectively utilizing accelerators in the cloud is important. Spatial multiplexing of the GPU, while limiting the GPU resources (GPU%) to each DNN to the right amount, leads to higher GPU utilization and higher inference throughput. Right-sizing the GPU for each DNN the optimal batching of requests to balance throughput and service level objectives (SLOs), and maximizing throughput by appropriately scheduling DNNs are still significant challenges. This paper introduces a dynamic and fair spatio-temporal scheduler (D-STACK) for multiple DNNs to run in the GPU concurrently. We develop and validate a model that estimates the parallelism each DNN can utilize and a lightweight optimization formulation to find an efficient batch size for each DNN. Our holistic inference framework provides high throughput while meeting application SLOs. We compare D-STACK with other GPU multiplexing and scheduling methods (e.g., NVIDIA Triton, Clipper, Nexus), using popular DNN models. Our controlled experiments with multiplexing several popular DNN models achieve up to 1.6× improvement in GPU utilization and up to 4× improvement in inference throughput.