GPU-Accelerated Multi-Dimensional Climate Analysis
Climate data naturally exists as high-dimensional tensors: spatial grids (lat, lon), temporal sequences, ensemble members, and variables. Traditional approaches process these dimensions sequentially. By leveraging tensor operations on GPUs, we achieve 25x speedup on correlation analysis and enable real-time ensemble forecasting.
Modern climate datasets like MERRA-2 contain terabytes of data across multiple dimensions:
Computing lagged correlations between predictors and streamflow across this hyperspace would take weeks on CPUs. Tensor operations reduce this to hours.
CuPy arrays enable massive parallelization of correlation computations across spatial grids and lag windows simultaneously.
Automatic dimension expansion allows elegant operations on mismatched tensor shapes without explicit loops.
Einstein summation provides concise, readable expressions for complex multi-dimensional contractions.
Lazy evaluation and chunking prevent memory overflow when processing terabyte-scale datasets.
This 3D visualization shows a climate data tensor with spatial dimensions (lat, lon) and a temporal dimension. Each layer represents a time step, with colors indicating temperature anomalies. Rotate with mouse to explore the spatiotemporal structure.
Dimensions: 25 × 25 × 48 (lat × lon × time) | Data: Temperature anomalies | GPU Operations: Parallel slice extraction
NumPy-compatible interface for GPU arrays, enabling drop-in acceleration
Labeled multi-dimensional arrays with climate-aware operations
Chunked, compressed array storage for cloud-native data access
Parallel computing with task scheduling for out-of-core operations
Fluid Tensor Analytics | Westminster, Colorado
taylor@fluidtensoranalytics.com