Awesome profiling

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

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.

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.

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

Pytorch profiler: Visual profiles of computations and data loading for PyTorch models, requires changes to code
PyTorch memory profiler: Can help debug OOMs and memory spikes

pynvml: Like nvidia-smi for your code with deeper level instrumentation
NVIDIA visual profiler
NVIDIA tools
GPU View: Windows specific GPU profiling
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

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

pyreverse: Get python classes and then visualize with graphviz
pdb: Use step in functionality or line by line to understand how your code works
IntelliJ UML Class diagrams