The demand for artificial intelligence has grown significantly over the past decade, and this
growth has been fueled by advances in machine learning techniques and the ability to …

Smarter applications are making better use of the insights gleaned from data, having an
impact on every industry and research discipline. At the core of this revolution lies the tools …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

Loosely-coupled and light-weight microservices running in containers are replacing
monolithic applications gradually. Understanding the characteristics of microservices is …

Generative large language model (LLM) applications are growing rapidly, leading to large-
scale deployments of expensive and power-hungry GPUs. Our characterization of LLM …

We report on experiences with Swift congestion control in Google datacenters. Swift targets
an end-to-end delay by using AIMD control, with pacing under extreme congestion. With …

Modern machine learning algorithms are increasingly computationally demanding, requiring
specialized hardware and distributed computation to achieve high performance in a …

Efficiently scheduling data processing jobs on distributed compute clusters requires complex
algorithms. Current systems use simple, generalized heuristics and ignore workload …

The next generation of AI applications will continuously interact with the environment and
learn from these interactions. These applications impose new and demanding systems …

We present SCENIC, a computational method for simultaneous gene regulatory network
reconstruction and cell-state identification from single-cell RNA-seq data (http://scenic …