The predominance of double-precision arithmetic in scientific applications imposes severe computational costs, challenging the sustainability of High-Performance Computing (HPC).
This work investigates the impact of the reduced precision paradigm as a strategy to enable Green HPC.
The main objective is to analyze the consequences of numerical precision reduction (such as the transition from Float64 to Float32) on computational performance, encompassing execution time and memory usage, and on the accuracy of scientific results.
Since validating these strategies solely through theory is insufficient, a practical case study was conducted using high-performance geophysical simulations from the open-source Fatiando a Terra ecosystem.
The research was operationalized by modifying data types (dtypes) in matrix operations performed by libraries such as NumPy and Numba, leveraging hardware optimizations in modern architectures.
The methodology evaluates the trade-off between efficiency and scientific validity on two fronts: (1) controlled benchmarking experiments on potential field algorithms, and (2) a real-world application involving massive geospatial data for hyperparameter optimization.
Results demonstrate that, while precision reduction offers significant performance gains, its feasibility is strictly governed by the numerical resilience of the underlying mathematical formulation, especially by the presence of regularization.
This study contributes to computational geophysics, particularly in the domain of potential fields, by establishing empirical guidelines for the safe adoption of low-precision arithmetic in solving inverse problems and spatial interpolation, preventing catastrophic cancellations in critical Earth modeling workflows.