Time-of-flight secondary ion mass spectrometry (ToF-SIMS) produces complex, information-rich, high-dimensional chemical data sets that can be challenging to analyze and interpret. Computational methods for dimensionality reduction offer effective pathways for addressing these issues. This work offers guidance, exemplars, and benchmarking options for optimizing machine learning computation that are specifically relevant to the investigation of large scale ToF-SIMS data sets and generally applicable to other machine learning applications. The guidance is formed around the self-organizing map with relational perspective mapping (SOM-RPM) MATLAB toolbox developed by our group, as a practical example of optimization, computation speed up, and related efficiency improvements. Optimization approaches considered include processor selection and methods of deployment, and cleanup of code to reduce duplicate calculations. This work explores the practical trade-offs of using double precision floating point arithmetic on execution speeds, in particular, for parallel calculations on the GPU, in comparison with single precision. The interaction between the inherent spectral and signal-to-noise characteristics of the ToF-SIMS data and the floating-point precision—in terms of machine learning model quality and convergence—is considered. An illustrative case study of a mineral thin section is presented using ToF-SIMS and SOM-RPM for the rapid identification of regions of interest to guide subsequent high-resolution scans. We have documented speed improvements (of code execution time) up to two orders of magnitude in our SOM-RPM toolbox. These improvements not only reduce computational latency but also open a feasible trajectory for real-time machine learning deployments in surface analysis workflows.
Oslinker et al. (2026) studied this question.