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Found 4 papers, 0 papers with code
Building a Performance Model for Deep Learning Recommendation Model Training on GPUs
We show that our general performance model not only achieves low prediction error on DLRM, which has highly customized configurations and is dominated by multiple factors but also yields comparable accuracy on other compute-bound ML models targeted by most previous methods.
Supporting Massive DLRM Inference Through Software Defined Memory
Deep Learning Recommendation Models (DLRM) are widespread, account for a considerable data center footprint, and grow by more than 1. 5x per year.
Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale
We share in this paper our search strategies to adapt reference recommendation models to low-precision hardware, our optimization of low-precision compute kernels, and the design and development of tool chain so as to maintain our models' accuracy throughout their lifespan during which topic trends and users' interests inevitably evolve.
Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems
Large-scale training is important to ensure high performance and accuracy of machine-learning models.
Distributed, Parallel, and Cluster Computing 68T05, 68M10 H.3.3; I.2.6; C.2.1
