Engineering Papers⌕ Search

DOE OSTI · 1827323

PINE: Photonic Integrated Networked Energy efficient datacenters

Abstract

We review the motivation, goals, and achievements of the Photonic Integrated Networked Energy efficient datacenter (PINE) project, which is part of the Advanced Research Projects Agency–Energy (ARPA-E) ENergy-efficient Light-wave Integrated Technology Enabling Networks that Enhance Dataprocessing (ENLITENED) program. Here, the PINE program leverages the unique features of photonic technologies to enable alternative mega-datacenters and high-performance computing (HPC) system architectures that deliver more substantial energy efficiency improvements than can be achieved through link energy efficiency alone. In phase 1 of the program, the PINE system architecture demonstrated an average factor of 2.2× improvement in transactions/joule across a diverse set of HPC and datacenter applications. In phase 2, PINE will demonstrate an aggressive 1.0 pJ/bit total link budget with high-bandwidth-density dense wavelength-division multiplexing (DWDM) links to enable additional 2.5× or more efficiency gains through deep resource disaggregation.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Glick, Madeleine, Abrams, Nathan C., Cheng, Qixiang, Teh, Min Yee, Hung, Yu-Han, Jimenez, Oscar, Liu, Songtao, Okawachi, Yoshitomo, Meng, Xiang, Johansson, Leif, Ghobadi, Manya, Dennison, Larry, Michelogiannakis, George, Shalf, John, Liu, Alan, Bowers, John, Gaeta, Alex, Lipson, Michal, Bergman, Keren. 2020-11-30. PINE: Photonic Integrated Networked Energy efficient datacenters. https://doi.org/10.1364/jocn.402788

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗