Awesome profiling

March 19, 2026 · View on GitHub

General utilites

  • https://ebpf.io/: Revolutionary sandboxed kernel profiling technology that makes it easier to build various profiling utilities. Tons of options here in Python https://github.com/iovisor/bcc
  • Dtrace: Available on Solaris (includes Mac but not Ubuntu) with notable highlights prstat and mpstat. prstat is not available on Ubuntu but can be replicated with htop and ps
  • collectl: Full system level profiling including CPU, disk, memory and network
  • perf: CPU level performance counters
  • gprof: sampling and instrumentation aware profiling
  • google perf tools
  • Heaptrack: a heap memory profiler for linux
  • jemalloc: another heap memory profiler
  • ETW: Event tracing for windows
  • Mac OS instruments: Mac OS instruments for profiling based on top of Dtrace
  • Renderdoc: Multi platform graphics debugger for OpenGL and Vulkan
  • Windows Perf Analyzer: If htop could plot lines, windows only but recently added support for android
  • htop: Visualize utilization as bar charts or line charts, issue commands to processes
  • Magic Trace: High resolution programmable traces
  • pprof: pprof is a tool for visualization and analysis of profiling data
  • Samply: a command line CPU profiler which uses the Firefox profiler as its UI. works on macOS, Linux, and Windows.

Continuous Profiling

  • parca: Continuous profiling for analysis of CPU and memory usage, down to the line number and throughout time. Saving infrastructure cost, improving performance, and increasing reliability
  • parca-agent: eBPF-based always-on profiler auto-discovering targets in Kubernetes and systemd, zero code changes or restarts needed! Supports multiple languages: C/C++, Rust, Go, Python, Ruby, Java, etc.

Python specific

  • viztracer
  • psutil: Like htop but from within your python code
  • pyinstrument:python call stack visualizer
  • pycallgraph: Visualize call stack as a graph (Maintenance mode)
  • py-spy: Sampling profiler for Python
  • line profiler: Line by line profiling
  • palanteer: Fanciest UI, looks like something out of the matrix
  • yappi: multi threaded profiling
  • Pycharm profiler: Built in profiler in Pycharm
  • TAU
  • gprof2dot: Graphical call stack visualizer (Maintenance mode)
  • snakeviz: Visualize python cprofile data
  • scalene: CPU and GPU based profiling with a web GUI
  • pprofile: Very low overhead line profile
  • austin-python: Line-level very low overhead time & memory profiler with web & terminal UI
  • py-perf A low-overhead, sampling CPU profiler for Python implemented using eBPF.
  • oracletrace: “Lightweight Python execution tracer that detects performance regressions by comparing execution traces across runs

Ruby specific

  • rbspy: Sampling CPU profiler for Ruby
  • rbperf: Low-overhead sampling profiler and tracer for Ruby implemented in BPF
  • vernier: Next generation CRuby profiler

Java specific

  • JProfiler: Java profiler for cpu, multithreading, graphical call stack visualizer
  • Java visual VM: Bundled with JDK

Mobile specific

  • iOS: Instruments app in Xcode
  • Android: Android Profiler in Android Studio

C# specific

C++ specific

  • Tracy: Windows only but very comprehensive and helpful for game development
  • Callgrind: Valgrind extension

Web specific

PyTorch specific

CPU specific

GPU specific

  • pynvml: Like nvidia-smi for your code with deeper level instrumentation
  • NVIDIA visual profiler
  • NVIDIA tools
  • GPU View: Windows specific GPU profiling
  • Ingero: eBPF-based GPU causal observability agent. Traces CUDA Runtime/Driver APIs via uprobes and host kernel events via tracepoints. Builds causal chains explaining GPU latency with full Python-to-CUDA stack traces.
  • ROC profiler: AMD ROCm profiler
  • Omniperf: AMD profiler for MI100 and MI200 accelerators
  • NVIDIA NCU: Infinitely more useful than NVIDIA's nsys, does a godbolt style view on PTX and gives actionable performance hints

Books

Blogs

  • Flame Graphs: flame graphs vs flame charts, off cpu profiling, icicle charts and more
  • How to read icicle and flame graphs: Flame graphs and icicle graphs are a great way to visualize performance profiles. In this post, we will learn how to read and interpret them.
  • Sampling vs Tracing: sampling based profilers are easier to use since they don't require any code change while instrumentation based profilers require code changes but are generally more informative
  • C++ performance tools: reddit post with tons of links

Talks

Understanding code structure