ktop: Unified GPU/CPU Monitor for Hybrid Workloads

A new command-line monitoring tool called ktop has emerged to address a persistent gap in system observability: tracking GPU and CPU resources in a single interface. Released as an open-source project, ktop provides real-time visibility into heterogeneous computing environments where workloads span both traditional processors and graphics accelerators.

Overview

Modern machine learning pipelines, rendering workflows, and scientific computing tasks routinely split work between CPUs and GPUs. Yet standard monitoring tools remain siloed. The top command shows CPU processes, nvidia-smi displays NVIDIA GPU metrics, and AMD users turn to rocm-smi. Engineers running hybrid workloads have historically juggled multiple terminal windows or written custom scripts to correlate resource usage across compute types.

ktop consolidates this fragmented monitoring landscape into a single TUI (text user interface). The tool displays CPU utilization, memory consumption, GPU usage, VRAM allocation, and process-level breakdowns in one view. It supports NVIDIA GPUs through NVML (NVIDIA Management Library), AMD GPUs via ROCm, and Intel GPUs through Level Zero APIs.

The project lives at https://github.com/vladkens/ktop and ships as a standalone binary with minimal dependencies. Installation requires no root privileges, making it suitable for shared research clusters and cloud instances where users lack administrative access.

Technical Details

ktop queries hardware metrics through vendor-specific libraries rather than parsing command output. For NVIDIA devices, it links against the NVML C library to retrieve GPU clock speeds, temperature, power draw, and per-process memory allocations. AMD support relies on ROCm’s SMI library, while Intel GPU monitoring uses the oneAPI Level Zero interface.

The CPU monitoring component reads from /proc/stat, /proc/meminfo, and per-process directories in /proc/[pid]/ on Linux systems. This approach mirrors how traditional monitoring tools work but adds correlation logic to match processes consuming both CPU and GPU resources.

# Install via cargo
cargo install ktop

# Run with GPU monitoring enabled
ktop --gpu

# Filter to specific processes
ktop --filter python

The tool refreshes metrics at configurable intervals, defaulting to one-second updates. A process tree view shows parent-child relationships, helping identify which training script spawned GPU-hungry worker processes. Color-coded bars indicate utilization levels, with red highlighting processes exceeding 80% of available resources.

ktop handles multi-GPU systems by displaying per-device breakdowns. On a machine with four A100 GPUs, the interface shows individual utilization for each accelerator alongside which processes occupy each device. This proves particularly valuable for debugging load imbalancing in distributed training setups.

Practical Impact

Data scientists training large language models benefit from seeing whether bottlenecks stem from CPU preprocessing or GPU computation. A model training run showing 100% GPU utilization but only 20% CPU usage suggests data loading pipelines need optimization. Conversely, maxed-out CPUs with idle GPUs point to preprocessing bottlenecks.

The unified view simplifies capacity planning. Infrastructure teams can identify underutilized GPUs that could handle additional workloads or spot memory leaks before they crash training jobs. One research lab reported catching a VRAM leak in a custom CUDA kernel within minutes using ktop, whereas their previous monitoring setup required correlating timestamps across separate logs.

Rendering studios running Blender or Unreal Engine workloads use ktop to balance scene complexity against available hardware. A scene that saturates GPU memory but leaves CPU cores idle might benefit from baking certain effects on the CPU instead.

The tool’s lightweight footprint matters in production environments. Unlike web-based monitoring dashboards, ktop consumes negligible resources—typically under 50MB of RAM and less than 1% CPU. This makes it suitable for running continuously on training servers without impacting model performance.

Outlook

Future development roadmap includes support for Apple Silicon’s unified memory architecture, where GPU and CPU share the same RAM pool. The current version treats these as separate resources, which doesn’t reflect how M-series chips actually allocate memory.

Integration with container runtimes represents another expansion area. Kubernetes clusters running GPU-accelerated pods would benefit from namespace-aware monitoring that shows resource consumption per deployment rather than just per process.

The project maintainers are exploring historical metrics storage, allowing users to replay resource usage patterns from completed training runs. This would enable post-mortem analysis of failed jobs without requiring continuous external logging infrastructure.

As AI workloads increasingly combine CPU preprocessing, GPU training, and specialized accelerators like TPUs, unified monitoring tools will become essential infrastructure. ktop demonstrates that effective observability doesn’t require complex dashboards—sometimes a well-designed terminal interface provides exactly the visibility engineers need.