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 …

The computing demand for massive applications has led to the ubiquitous deployment of
computing power. This trend results in the urgent need for higher-level computing resource …

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) …

Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that
enables in-situ model training and testing on edge data. Despite having the same end goals …

High-demand LLM inference services (eg, ChatGPT and BARD) support a wide range of
requests from short chat conversations to long document reading. To ensure that all client …

Despite existing work in machine learning inference serving, ease-of-use and cost efficiency
remain challenges at large scales. Developers must manually search through thousands of …

Large Language Models (LLMs) have presented impressive performance across several
transformative tasks. However, it is non-trivial to efficiently utilize large-scale cluster …

Specialized accelerators such as GPUs, TPUs, FPGAs, and custom ASICs have been
increasingly deployed to train deep learning models. These accelerators exhibit …

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 …

In public clouds, users must manually select a datacenter region to upload their ML training
data and launch ML training workloads in the same region to ensure data and computation …