CudaMon: An R Package to Monitor NVIDIA GPUs, Showcased by Monitoring a GPU-accelerated Single-cell Analysis Workflow in R

2026-05-13

AI Summary

This study addresses the lack of native GPU monitoring tools in R for computational biology, where users traditionally rely on external command-line utilities. To bridge this gap, we introduce CudaMon, an R package that leverages the NVIDIA Management Library (NVML) interface to provide real-time monitoring of GPU utilization, memory usage, temperature, and power consumption. CudaMon seamlessly integrates data export and visualization capabilities into standard R workflows, significantly enhancing the efficiency and reproducibility of GPU-accelerated analyses. In a large-scale single-cell RNA-seq analysis involving millions of brain cells, the tool successfully identified stages—such as PCA and UMAP—where GPU utilization exceeded 90%, while also uncovering performance bottlenecks during data management phases.

Abstract

NVIDIA GPUs have recently started to be used in computational biology, yet R users lack integrated GPU monitoring tools, forcing reliance on external utilities like nvidia-smi. We introduce CudaMon, an R package providing real-time monitoring of GPU utilization, memory, temperature, and power draw via NVML, along with data export and visualization utilities. Monitoring a GPU-accelerated single-cell RNA-seq pipeline (1M brain cells, RAPIDS workflow) shows that compute-intensive steps (PCA, UMAP, t-SNE) exceed 90% GPU utilization, while data management phases reveal bottlenecks. CudaMon facilitates resource optimization, performance debugging, and reproducibility for GPU-accelerated R workflows.

Problem

• GPU monitoring
• R package
• computational biology
• NVIDIA GPUs
• single-cell analysis

Innovation

• CudaMon
• GPU monitoring
• R package
• single-cell RNA-seq
• NVML