Accelerating joint species distribution modeling with Hmsc-HPC: A 1000x faster GPU deployment

Abstract

Joint Species Distribution Modelling (JSDM) is a powerful and increasingly widely used statistical methodology in biodiversity modelling, enabling researchers to assess and predict the joint distribution of species across space and time. However, JSDM can be computationally intensive and even prohibitive, especially for large datasets and sophisticated model structures. To address computational limitations of JSDM, we expanded one widely used JSDM framework, Hmsc-R, by developing a Graphical Processing Unit (GPU) -compatible implementation of its model fitting algorithm. While our augmented framework retains the original user interface in R, its new computational core is coded in Python and dominantly uses TensorFlow library. This enhancement primarily targets to enable leveraging high-performance computing resources effectively, though it also accelerates model fitting with consumer-level machines. This upgrade is designed to leverage high-performance computing resources more effectively. We evaluated the performance of the proposed implementation across diverse model configurations and dataset sizes. Our results indicate significant model fitting speed-up compared to the existing Hmsc-R package across most models. Notably, for the largest datasets, we achieved >1000 times speed-ups. This GPU-compatible enhancement boosts the scalability of Hmsc-R package by several orders of magnitude, reaching a significantly higher level. It opens promising opportunities for modeling extensive and intricate datasets, enabling better-informed conservation strategies, environmental management, and climate change adaptation planning. Author summaryOur study addresses the computational challenges associated with Joint Species Distribution Modelling (JSDM), a critical statistical methodology for understanding species distributions in biodiversity research. Despite its utility, JSDM often faces computational limitations, particularly for large datasets. To overcome this hurdle, we enhance the widely used Hmsc-R framework by introducing a GPU-compatible implementation of its model fitting algorithm. Our upgraded framework, while retaining the user-friendly R interface, leverages Python and TensorFlow for its computational core, enabling efficient utilization of high-performance computing resources. Through extensive evaluation across diverse model configurations and dataset sizes, we demonstrate substantial speed-ups compared to the original Hmsc-R package, with over 1000 times speed-ups observed for the largest datasets. This GPU-compatible enhancement significantly improves the scalability of JSDM, enabling the analysis of extensive and complex biodiversity datasets. Our work has far-reaching implications for informing conservation strategies, environmental management, and climate change adaptation planning by facilitating more efficient and accurate biodiversity modeling, ultimately contributing to better-informed decision-making in ecological research and practice.