Deep learning (DL) has demonstrated its remarkable success in a wide variety of fields. The
development of a DL model is a time-consuming and resource-intensive procedure. Hence …

With the sustained technological advances in machine learning (ML) and the availability of
massive datasets recently, tech companies are deploying large ML-as-a-Service (MLaaS) …

Deep learning (DL) shows its prosperity in a wide variety of fields. The development of a DL
model is a time-consuming and resource-intensive procedure. Hence, dedicated GPU …

It is a challenging task to train large DNN models on sophisticated GPU platforms with
diversified interconnect capabilities. Recently, pipelined training has been proposed as an …

Modern GPU datacenters are critical for delivering Deep Learning (DL) models and services
in both the research community and industry. When operating a datacenter, optimization of …

Systems for training massive deep learning models (billions of parameters) today assume
and require specialized" hyperclusters": hundreds or thousands of GPUs wired with …

To accelerate the training of Deep Learning (DL) models, clusters of machines equipped
with hardware accelerators such as GPUs are leveraged to reduce execution time. State-of …

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 …

Understanding the GPU utilization of Deep Learning (DL) workloads is important for
enhancing resource-efficiency and cost-benefit decision making for DL frameworks in the …

Edge computing is increasingly used for Artificial Intelligence (AI) purposes to meet latency,
privacy, and energy challenges. Convolutional Neural networks (CNN) are more frequently …