The widespread application of deep learning has changed the landscape of
computation in the data center. In particular, personalized recommendation for
content ranking is now largely accomplished leveraging deep neural networks.
However, despite the importance of these models and the amount of compute
cycles they consume, relatively little research attention has been devoted to
systems for recommendation. To facilitate research and to advance the
understanding of these workloads, this paper presents a set of real-world,
production-scale DNNs for personalized recommendation coupled with relevant
performance metrics for evaluation. In addition to releasing a set of
open-source workloads, we conduct in-depth analysis that underpins future
system design and optimization for at-scale recommendation: Inference latency
varies by 60% across three Intel server generations, batching and co-location
of inferences can drastically improve latency-bounded throughput, and the
diverse composition of recommendation models leads to different optimization
strategies.

这篇论文介绍了一组用于个性化推荐的实际生产规模的深度神经网络，以及与此相关的性能度量，以促进研究和推动未来规模推荐的系统设计和优化，文中分析了推理延迟、批处理和推理的协同作用、推荐模型的不同优化策略。

Facebook 基于 DNN 的个性化推荐系统的体系结构影响

The Architectural Implications of Facebook's DNN-based Personalized  Recommendation

Because the choice and tuning of the optimizer affects the speed, and
ultimately the performance of deep learning, there is significant past and
recent research in this area. Yet, perhaps surprisingly, there is no generally
agreed-upon protocol for the quantitative and reproducible evaluation of
optimization strategies for deep learning. We suggest routines and benchmarks
for stochastic optimization, with special focus on the unique aspects of deep
learning, such as stochasticity, tunability and generalization. As the primary
contribution, we present DeepOBS, a Python package of deep learning
optimization benchmarks. The package addresses key challenges in the
quantitative assessment of stochastic optimizers, and automates most steps of
benchmarking. The library includes a wide and extensible set of ready-to-use
realistic optimization problems, such as training Residual Networks for image
classification on ImageNet or character-level language prediction models, as
well as popular classics like MNIST and CIFAR-10. The package also provides
realistic baseline results for the most popular optimizers on these test
problems, ensuring a fair comparison to the competition when benchmarking new
optimizers, and without having to run costly experiments. It comes with output
back-ends that directly produce LaTeX code for inclusion in academic
publications. It supports TensorFlow and is available open source.

提出一个用于深度学习优化基准的 Python 包 DeepOBS，该包包括广泛和可扩展的真实优化问题集，以及最流行的优化器的基准结果，支持 TensorFlow，并提供直接生成 LaTeX 代码的输出后端。