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The Kokkos EcoSystem: Comprehensive Performance Portability For High Performance Computing

Trott, Christian·Berger-Vergiat, Luc·Poliakoff, David Zoeller·Rajamanickam, Sivasankaran·Lebrun-Grandie, Damien·Madsen, Jonathan·Gligoric, Milos·Al Awar, Nader·Shipman, Galen·Womeldorff, Geoffrey
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Abstract

State of the art Engineering and Science codes have grown in complexity dramatically over the last two decades. As a consequence application teams have adopted more sophisticated development strategies, leveraging third party libraries, deploying comprehensive testing and using advanced debugging and profiling tools. In todays environment of diverse hardware platforms, these applications also desire performance portability - avoiding the need to duplicate work for various platforms - which makes it necessary that these tools and libraries also work across the various systems. The Kokkos EcoSystem provides that portable software stack. Based on the Kokkos Core Programming Model, the EcoSystem provides math libraries, interoperability capabilities with Python and Fortran, and Tools for analysing, debugging, and optimizing applications. In this paper we will provide an overview of the components, discuss some specific use cases, and highlight how co-designing these components enables a more developer friendly experience.

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BibTeXRIS

Trott, Christian, Berger-Vergiat, Luc, Poliakoff, David Zoeller, Rajamanickam, Sivasankaran, Lebrun-Grandie, Damien, Madsen, Jonathan, Gligoric, Milos, Al Awar, Nader, Shipman, Galen, Womeldorff, Geoffrey. 2021-09-02. The Kokkos EcoSystem: Comprehensive Performance Portability For High Performance Computing. https://doi.org/10.1109/mcse.2021.3098509

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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.

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