Accelerating joint species distribution modelling with Hmsc-HPC by GPU porting

• Published in: PLoS computational biology Vol. 20; no. 9; p. e1011914
• Main Authors: Rahman, Anis Ur, Tikhonov, Gleb, Oksanen, Jari, Rossi, Tuomas, Ovaskainen, Otso
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 03.09.2024
• Subjects: Algorithms; Analysis; Computational Biology - methods; Computer Graphics; Distribution; Electronic data processing; Geospatial data; Graphics coprocessors; Humans; Machine Learning; Mathematical models; Methods; Models, Biological; Models, Statistical; Software; Species; Finland
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1011914

Joint species distribution modelling (JSDM) is a widely used statistical method that analyzes combined patterns of all species in a community, linking empirical data to ecological theory and enhancing community-wide prediction tasks. However, fitting JSDMs to large datasets is often computationally demanding and time-consuming. Recent studies have introduced new statistical and machine learning techniques to provide more scalable fitting algorithms, but extending these to complex JSDM structures that account for spatial dependencies or multi-level sampling designs remains challenging. In this study, we aim to enhance JSDM scalability by leveraging high-performance computing (HPC) resources for an existing fitting method. Our work focuses on the Hmsc R-package, a widely used JSDM framework that supports the integration of various dataset types into a single comprehensive model. We developed a GPU-compatible implementation of its model-fitting algorithm using Python and the TensorFlow library. Despite these changes, our enhanced framework retains the original user interface of the Hmsc R-package. We evaluated the performance of the proposed implementation across various model configurations and dataset sizes. Our results show a significant increase in model fitting speed for most models compared to the baseline Hmsc R-package. For the largest datasets, we achieved speed-ups of over 1000 times, demonstrating the substantial potential of GPU porting for previously CPU-bound JSDM software. This advancement opens promising opportunities for better utilizing the rapidly accumulating new biodiversity data resources for inference and prediction.