A Bag of Tricks for Scaling CPU-based Deep FFMs to more than 300m Predictions per Second
<|reference_start|>A Bag of Tricks for Scaling CPU-based Deep FFMs to more than 300m Predictions per Second: Field-aware Factorization Machines (FFMs) have emerged as a powerful model for click-through rate prediction, particularly excelling in capturing complex feature interactions. In this work, we present an in-depth analysis of our in-house, Rust-based Deep FFM implementation, and detail its deployment on a CPU-only, multi-data-center scale. We overview key optimizations devised for both training and inference, demonstrated by previously unpublished benchmark results in efficient model search and online training. Further, we detail an in-house weight quantization that resulted in more than an order of magnitude reduction in bandwidth footprint related to weight transfers across data-centres. We disclose the engine and associated techniques under an open-source license to contribute to the broader machine learning community. This paper showcases one of the first successful CPU-only deployments of Deep FFMs at such scale, marking a significant stride in practical, low-footprint click-through rate prediction methodologies.<|reference_end|>
@article{škrlj2024a,
    title={A Bag of Tricks for Scaling CPU-based Deep FFMs to more than 300m
  Predictions per Second},
    author={Blav{z} v{S}krlj, Benjamin Ben-Shalom, Grega Gav{s}perv{s}iv{c},
  Adi Schwartz, Ramzi Hoseisi, Naama Ziporin, Davorin Kopiv{c}, Andrav{z}
  Tori},
    journal={arXiv preprint arXiv:2407.10115},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10115},
    primaryClass={cs.LG cs.AI cs.IR}
}