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.

翻译：NVIDIA GPU最近开始被用于计算生物学领域，然而R用户缺乏集成的GPU监控工具，这迫使他们依赖nvidia-smi等外部实用程序。我们推出了CudaMon，这是一个通过NVML提供GPU利用率、内存、温度和功耗实时监控的R包，同时具备数据导出和可视化功能。通过对一个GPU加速的单细胞RNA-seq流程（100万个脑细胞，RAPIDS工作流程）进行监控，结果表明计算密集型步骤（PCA、UMAP、t-SNE）的GPU利用率超过90%，而数据管理阶段则暴露了瓶颈。CudaMon有助于优化资源、进行性能调试并增强GPU加速R工作流程的可重复性。