@conference {Gupta2020b, title = {DeepRecSys: A System for Optimizing End-To-End At-scale Neural Recommendation Inference}, year = {2020}, publisher = {The 47th IEEE/ACM International Symposium on Computer Architecture (ISCA 2020)}, organization = {The 47th IEEE/ACM International Symposium on Computer Architecture (ISCA 2020)}, abstract = {Neural personalized recommendation is the corner-stone of a wide collection of cloud services and products, constituting significant compute demand of the cloud infrastructure. Thus, improving the execution efficiency of neural recommendation directly translates into infrastructure capacity saving. In this paper, we devise a novel end-to-end modeling infrastructure, DeepRecInfra, that adopts an algorithm and system co-design methodology to custom-design systems for recommendation use cases. Leveraging the insights from the recommendation characterization, a new dynamic scheduler, DeepRecSched, is proposed to maximize latency-bounded throughput by taking into account characteristics of inference query size and arrival patterns, recommendation model architectures, and underlying hardware systems. By doing so, system throughput is doubled across the eight industry-representative recommendation models. Finally, design, deployment, and evaluation in at-scale production datacenter shows over 30\% latency reduction across a wide variety of recommendation models running on hundreds of machines.}, keywords = {deep learning, Recommendation}, url = {https://conferences.computer.org/isca/pdfs/ISCA2020-4QlDegUf3fKiwUXfV0KdCm/466100a982/466100a982.pdf}, author = {Udit Gupta and Hsia, Samuel and Saraph, Vikram and Xiaodong Wang and Brandon Reagen and Gu-Yeon Wei and Hsien-Hsin S. Lee and Carole-Jean Wu and David Brooks} }