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Nsight-Compute

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Nsight Compute

Type: Technology Tags: CUDA, NVIDIA, GPU, Profiling, Performance Analysis, Development Tools, CUDA Toolkit Related: Nsight-Developer-Tools, Nsight-Copilot, ComputeEval, Nsight-JupyterLab-Extension, Nsight-Python, Nsight-Systems, Nsight-Deep-Learning-Designer, Nsight-Graphics, Nsight-Integration, Nsight-Visual-Studio-Code-Edition, Nsight-Visual-Studio-Edition, Nsight-Eclipse-Plugins, NVTX, NVCC, CUDA-Python, cuda-bindings, cuTile, CUDA-GDB, Compute-Sanitizer Sources: NVIDIA official documentation (docs.nvidia.com/cuda), https://docs.nvidia.com/cuda/cutile-python/quickstart.html, https://nvidia.github.io/cuda-python/cuda-bindings/latest/overview.html, https://docs.nvidia.com/nsight-python/index.html, https://developer.nvidia.com/nsight-dl-designer, https://docs.nvidia.com/nsight-developer-tools/index.html, https://docs.nvidia.com/nsight-vs-integration/getting-started/index.html, https://developer.nvidia.com/tools-overview/nsight-compute/get-started Last Updated: 2026-04-29

Summary

Nsight Compute is NVIDIA’s next-generation interactive GPU kernel profiler for CUDA applications, providing detailed per-kernel performance metrics, hardware counter data, and guided analysis for identifying and resolving performance bottlenecks. It supports both an interactive GUI and a command-line interface (ncu), and provides roofline analysis, memory throughput breakdowns, and warp execution efficiency metrics to guide kernel optimization.

Detail

Purpose

Writing a correct GPU kernel is only half the challenge — writing a fast one requires understanding how the kernel utilizes hardware resources (compute throughput, memory bandwidth, occupancy). Nsight Compute provides the detailed hardware-level data needed to understand why a kernel is slow and what to fix, guiding developers from “it works” to “it runs optimally.”

Key Features

  • Interactive kernel profiler with GUI (Nsight Compute IDE) and command-line tool (ncu)
  • Nsight Copilot preview in the standalone Nsight Compute 2026.1 download for CUDA-aware profiler guidance
  • Detailed hardware performance metrics: compute throughput, memory bandwidth, L1/L2/HBM utilization
  • Warp execution efficiency and occupancy analysis
  • Roofline performance model visualization
  • Source-level attribution: maps performance data back to CUDA source lines and PTX instructions
  • Guided analysis with automated bottleneck identification and recommendations
  • API debugging: inspect CUDA API calls and their overhead
  • Comparison view for before/after optimization analysis
  • Multi-GPU and multi-stream profiling
  • Supports CUDA 10.0+

Use Cases

  • Identifying memory-bound vs. compute-bound kernel bottlenecks
  • Optimizing occupancy and warp efficiency
  • Understanding cache hit rates and memory access patterns
  • Guided optimization for HPC and deep learning kernels
  • Validating performance improvements after code changes
  • Performance regression testing

Hardware Requirements

  • NVIDIA GPU with CUDA support (Volta and later for full feature set)
  • CUDA Toolkit installed
  • Available on Linux, Windows, and macOS (with GUI or CLI)

Language Bindings

  • Command-line tool (ncu) — works on any compiled CUDA binary
  • Python API for automated analysis scripting
  • GUI application (cross-platform)

Connections

  • Nsight-Systems — Nsight Systems provides application-level profiling; Nsight Compute provides kernel-level deep dive
  • Nsight-Copilot - preview integration in Nsight Compute for CUDA-aware guidance on profiler findings.
  • ComputeEval - can use ncu mode to collect detailed performance metrics for generated CUDA solutions.
  • Nsight-Python — Python automation layer can collect Nsight Compute-style kernel metrics.
  • Nsight-JupyterLab-Extension - JupyterLab workflow for launching Nsight Compute profiling on notebook cells.
  • NVTX - annotation API whose ranges can focus Nsight Compute profiling.
  • Nsight-Deep-Learning-Designer - adjacent Nsight IDE for ONNX model design and inference profiling with TensorRT/ONNX Runtime.
  • Nsight-Graphics - graphics debugger/profiler counterpart for frame capture, GPU Trace, shaders, and ray tracing.
  • Nsight-Integration - Visual Studio extension can launch Nsight Compute activities from the Visual Studio Nsight menu.
  • Nsight-Visual-Studio-Code-Edition - VS Code CUDA debugging workflow that can precede kernel-level performance profiling.
  • Nsight-Visual-Studio-Edition — Windows Visual Studio integration for CUDA debugging/profiling workflows
  • Nsight-Eclipse-Plugins — Eclipse-based IDE plugins for older CUDA Linux development workflows
  • NVCC — NVCC compiles the kernels that Nsight Compute then profiles
  • CUDA-Python and cuda-bindings — CUDA Python binding examples are documented as compatible with Nsight Compute profiling.
  • cuTile — NVIDIA’s cuTile quickstart documents profiling cuTile Python kernels with ncu.
  • CUDA-GDB — CUDA-GDB debugs kernel correctness; Nsight Compute optimizes kernel performance
  • Compute-Sanitizer — Compute Sanitizer checks correctness; Nsight Compute analyzes performance

Resources

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