Pushing the Performance Envelope of DNN-based Recommendation Systems Inference on GPUs

Personalized recommendation is a ubiquitous application on the internet, with many industries and hyperscalers extensively leveraging Deep Learning Recommendation Models (DLRMs) for their personalization needs (like ad serving or movie suggestions). With growing model and dataset sizes pushing computation and memory requirements, GPUs are being increasingly preferred for executing DLRM inference. However, serving newer DLRMs, while meeting acceptable latencies, continues to remain challenging, making traditional deployments increasingly more GPU-hungry, resulting in higher inference serving costs. In this paper, we show that the embedding stage continues to be the primary bottleneck in the GPU inference pipeline, leading up to a 3.2x embedding-only performance slowdown. To thoroughly grasp the problem, we conduct a detailed microarchitecture characterization and highlight the presence of low occupancy in the standard embedding kernels. By leveraging direct compiler optimizations, we achieve optimal occupancy, pushing the performance by up to 53%. Yet, long memory latency stalls continue to exist. To tackle this challenge, we propose specialized plug-and-play-based software prefetching and L2 pinning techniques, which help in hiding and decreasing the latencies. Further, we propose combining them, as they complement each other. Experimental evaluations using A100 GPUs with large models and datasets show that our proposed techniques improve performance by up to 103% for the embedding stage, and up to 77% for the overall DLRM inference pipeline.

GPU Cluster Scheduling for Network-Sensitive Deep Learning

We propose a novel GPU-cluster scheduler for distributed DL (DDL) workloads that enables proximity based consolidation of GPU resources based on the DDL jobs' sensitivities to the anticipated communication-network delays. Our scheduler consists of three major components: (i) a classical delay scheduling algorithm to facilitate job placement and consolidation; (ii) a network-sensitive job preemption strategy; and (iii) an "auto-tuner" mechanism to optimize delay timers for effective delay scheduling. Additionally, to enable a cost-effective methodology for large-scale experiments, we develop a data-driven DDL cluster simulation platform. Employing the simulation platform we compare against several state-of-the-art alternatives on real-world workload traces to demonstrate the benefits of our design. Our scheduler can provide improvement of up to 69% in end-to-end Makespan for training all jobs compared to the prevailing consolidation-based scheduling methods, while reducing the average job completion time by up to 83% and minimizing the communication overheads by up to 98% under congested networking conditions.

Analysis of Distributed Deep Learning in the Cloud

We aim to resolve this problem by introducing a comprehensive distributed deep learning (DDL) profiler, which can determine the various execution "stalls" that DDL suffers from while running on a public cloud. We have implemented the profiler by extending prior work to additionally estimate two types of communication stalls - interconnect and network stalls. We train popular DNN models using the profiler to characterize various AWS GPU instances and list their advantages and shortcomings for users to make an informed decision. We observe that the more expensive GPU instances may not be the most performant for all DNN models and AWS may sub-optimally allocate hardware interconnect resources. Specifically, the intra-machine interconnect can introduce communication overheads up to 90% of DNN training time and network-connected instances can suffer from up to 5x slowdown compared to training on a single instance. Further, we model the impact of DNN macroscopic features such as the number of layers and the number of gradients on communication stalls. Finally, we propose a measurement-based recommendation model for users to lower their public cloud monetary costs for DDL, given a time budget.

Cocktail: Leveraging Ensemble Learning for Optimized Model Serving in Public Cloud

With a growing demand for adopting ML models for a varietyof application services, it is vital that the frameworks servingthese models are capable of delivering highly accurate predic-tions with minimal latency along with reduced deploymentcosts in a public cloud environment. Despite high latency,prior works in this domain are crucially limited by the accu-racy offered by individual models. Intuitively, model ensem-bling can address the accuracy gap by intelligently combiningdifferent models in parallel. However, selecting the appro-priate models dynamically at runtime to meet the desiredaccuracy with low latency at minimal deployment cost is anontrivial problem. Towards this, we proposeCocktail, a costeffective ensembling-based model serving framework.Cock-tailcomprises of two key components: (i) a dynamic modelselection framework, which reduces the number of modelsin the ensemble, while satisfying the accuracy and latencyrequirements; (ii) an adaptive resource management (RM)framework that employs a distributed proactive autoscalingpolicy combined with importance sampling, to efficiently allo-cate resources for the models. The RM framework leveragestransient virtual machine (VM) instances to reduce the de-ployment cost in a public cloud. A prototype implementationofCocktailon the AWS EC2 platform and exhaustive evalua-tions using a variety of workloads demonstrate thatCocktailcan reduce deployment cost by 1.45x, while providing 2xreduction in latency and satisfying the target accuracy for upto 96% of the requests, when compared to state-of-the-artmodel-serving frameworks.