The application of deep learning techniques resulted in remarkable improvement of
machine learning models. In this paper provides detailed characterizations of deep learning …

Large-scale training is important to ensure high performance and accuracy of machine-
learning models. At Facebook we use many different models, including computer vision …

Machine learning sits at the core of many essential products and services at Facebook. This
paper describes the hardware and software infrastructure that supports machine learning at …

At Facebook, machine learning provides a wide range of capabilities that drive many
aspects of user experience including ranking posts, content understanding, object detection …

The widespread application of deep learning has changed the landscape of computation in
data centers. In particular, personalized recommendation for content ranking is now largely …

At-scale evaluation of new data center network innovations is becoming increasingly
intractable. This is true for testbeds, where few, if any, can afford a dedicated, full-scale …

Large cloud service providers have invested in increasingly larger datacenters to house the
computing infrastructure required to support their services. Accordingly, researchers and …

The rapid adoption of Internetenabled devices, coupled with the shift from consumerside
computing to SaaS and cloudbased systems, is accelerating the growth of largescale data …

At global user population scale, important microservices in warehouse-scale data centers
can grow to account for an enormous installed base of servers. With the end of Dennard …

In distributed DNN training, parameter servers (PS) can become performance bottlenecks
due to PS stragglers, caused by imbalanced parameter distribution, bandwidth contention …