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

TL;DR

CudaMon is an R package that enables real-time GPU monitoring for NVIDIA GPUs, aiding optimization and debugging of GPU-accelerated R workflows in computational biology.

Contribution

It introduces CudaMon, the first integrated R package for real-time GPU monitoring, visualization, and data export tailored for GPU-accelerated R workflows.

Findings

GPU compute steps exceed 90% utilization during analysis

Data management phases reveal bottlenecks

CudaMon helps optimize resource usage and debug performance

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.