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) …

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 …

Training machine learning models in parallel is an increasingly important workload. We
accelerate distributed parallel training by designing a communication primitive that uses a …

The rise of large language models (LLMs) has enabled LLM-based applications (aka AI
agents or co-pilots), a new software paradigm that combines the strength of LLM and …

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 …

Large tech companies are piling up a massive number of GPUs in their server fleets to run
diverse machine learning (ML) workloads. However, these expensive devices often suffer …

Pollux improves scheduling performance in deep learning (DL) clusters by adaptively co-
optimizing inter-dependent factors both at the per-job level and at the cluster-wide level …