RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing

Citation:

Liu Ke, Udit Gupta, Carole-Jean Wu, Benjamin Cho, Mark Hempstead, Brandon Reagen, Xuan Zhang, David Brooks, Vikas Chandra, Utku Diril, Amin Firoozshahian, Kim Hazelwood, Bill Jia, Hsien-Hsin Lee, Meng Li, Bert Maher, Dheevatsa Mudigere, Maxim Naumov, Martin Schatz, Mikhail Smelyanskiy, and Xiaodong Wang. 5/30/2020. “RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing.” In . The 47th IEEE/ACM International Symposium on Computer Architecture (ISCA 2020). Publisher's Version

Abstract:

Personalized recommendation systems leverage deep learning models and account for the majority of data center AI cycles. Their performance is dominated by memory-bound sparse embedding operations with unique irregular memory access patterns that pose a fundamental challenge to accelerate. This paper proposes a lightweight, commodity DRAM compliant, near-memory processing solution to accelerate personalized recommendation inference. The in-depth characterization of production-grade recommendation models shows that embedding operations with high model-, operator- and data-level parallelism lead to memory bandwidth saturation, limiting recommendation inference performance. We propose RecNMP which provides a scalable solution to improve system throughput, supporting a broad range of sparse embedding models. RecNMP is specifically tailored to production environments with heavy co-location of operators on a single server. Several hardware/software co-optimization techniques such as memory-side caching, table-aware packet scheduling, and hot entry profiling are studied, resulting in up to 9.8x memory latency speedup over a highly-optimized baseline. Overall, RecNMP offers 4.2x throughput improvement and 45.8% memory energy savings.
See also: RecSys
Last updated on 04/20/2022