D-STACK: High Throughput DNN Inference by Effective Multiplexing and Spatio-Temporal Scheduling of GPUs

Hardware accelerators such as GPUs are required for real-time, low-latency inference with Deep Neural Networks (DNN). However, due to the inherent limits to the parallelism they can exploit, DNNs often under-utilize the capacity of today's high-end accelerators. Although spatial multiplexing of the GPU, leads to higher GPU utilization and higher inference throughput, there remain a number of challenges. Finding the GPU percentage for right-sizing the GPU for each DNN through profiling, determining an optimal batching of requests to balance throughput improvement while meeting application-specific deadlines 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) that enables multiple DNNs to run in the GPU concurrently. To help allocate the appropriate GPU percentage (we call it the "Knee"), we develop and validate a model that estimates the parallelism each DNN can utilize. We also develop a lightweight optimization formulation to find an efficient batch size for each DNN operating with D-STACK. We bring together our optimizations and our spatio-temporal scheduler to provide a holistic inference framework. We demonstrate its ability to provide high throughput while meeting application SLOs. We compare D-STACK with an ideal scheduler that can allocate the right GPU percentage for every DNN kernel. D-STACK gets higher than 90 percent throughput and GPU utilization compared to the ideal scheduler. We also 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 1.6X improvement in GPU utilization and up to 4X improvement in inference throughput

Cooperative Design of Machine Learning and GPU-Based Systems for Inference

Our work seeks to improve and adapt computing systems and machine learning (ML) algorithms to match each other’s requirements and capabilities. Due to the high computational demand for Deep Neural Networks (DNNs), accelerators such as GPUs are necessary to achieve low-latency inference. With GPUs getting more powerful, DNNs often fail to utilize a GPU’s parallelism fully. Understanding the GPU resources a DNN model can effectively use while still fulfilling the SLO of users allows us to run multiple DNN models concurrently by spatially sharing the GPU. Our DNN inference framework virtualizes the GPU, adapts to the DNN model, and improves the CPU-GPU coordination, to achieve a much higher aggregate inference throughput compared to other multiplexing techniques. While spatial sharing utilizes the GPU’s resources better, its static resource allocation is unsuitable for dynamic workloads. We propose a spatio-temporal scheduler that provides the right GPU resources for multiple DNNs and meets the inference tasks’ SLOs. Our spatio-temporal scheduler can run more models concurrently and achieve 4 × higher throughput than other scheduling techniques.Autotuning a DNN model customizes it to match the system’s capabilities. However, current autotuning frameworks do not consider a DNN model’s GPU demand. They produce a sub-optimally tuned model that does not provide the lowest possible latency when inferring with a smaller amount of GPU resources. Our enhanced autotuning produces a tuned model resilient to different amounts of GPU resources available at inference by targeting the appropriate GPU resources during tuning. Our framework enables concurrent autotuning of multiple models by using spatial multiplexing of the GPU, and eliminates several system overheads, thus decreasing tuning time by more than 70%.We apply our understanding of GPU to the task of localization in multi-user augmented reality (AR) applications. Our multi-user AR framework efficiently offloads AR computation to an edge server and lowers the localization time by 50%. Our implementation utilizes shared memory on the edge server to reduce the time for ’map merging’ between different users’ maps, a task that needs to be performed frequently with multi-user AR. Our approach cuts the map merging time by more than 80% compared to potential multi-user AR approaches.Our overall approach is to adapt computing resources to the algorithms that use them. It particularly benefits current ML algorithms and other applications that use these accelerators, such as AR. Our techniques can also improve the throughput of the newer generations of accelerators, which offer a significant speedup by using parallel compute engines