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

Anis Ur Rahman1, Gleb Tikhonov2, Jari Oksanen2

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Summary
This summary is machine-generated.

We accelerated Joint Species Distribution Models (JSDMs) using GPU computing, significantly reducing analysis time for large ecological datasets. This makes complex biodiversity data more accessible for ecological inference and prediction.

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Area of Science:

  • Ecology
  • Computational Biology
  • Statistical Modeling

Background:

  • Joint Species Distribution Modelling (JSDM) is crucial for community ecology, linking data to theory and improving predictions.
  • Fitting JSDMs to large datasets is computationally intensive, limiting their application.
  • Existing scalable methods struggle with complex JSDM structures like spatial dependencies.

Purpose of the Study:

  • To enhance the scalability of JSDM fitting algorithms.
  • To leverage high-performance computing (HPC) resources, specifically GPUs, for JSDM.
  • To maintain the user-friendliness of existing JSDM frameworks.

Main Methods:

  • Developed a GPU-compatible implementation of the Hmsc R-package's model-fitting algorithm.
  • Utilized Python and the TensorFlow library for the GPU porting.
  • Evaluated performance across diverse model configurations and dataset sizes.

Main Results:

  • Achieved significant speed-ups in model fitting compared to the CPU-based Hmsc R-package.
  • Demonstrated speed-ups exceeding 1000 times for the largest datasets.
  • Confirmed the effectiveness of GPU acceleration for previously CPU-bound JSDM software.

Conclusions:

  • GPU porting substantially enhances JSDM fitting speed and scalability.
  • This advancement facilitates the analysis of large, complex biodiversity datasets.
  • Enables more effective utilization of biodiversity data for ecological inference and prediction.