# dendibakh/perf-ninja

This is an online course where you can learn and master the skill of low-level performance analysis and tuning.

Repository: https://github.com/dendibakh/perf-ninja
Canonical: https://ross.abutalabs.com/products/perf-ninja
Language: C++
License Family: other
Last push: 2026-08-25T19:12:14+00:00

## Health v2 (maintenance only)
Score: 77/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 35, longevity 100
- inputs: {"age_days": 1870, "days_push": 8, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3825, forks 395 (observed 2026-08-28T04:08:22.159387+00:00)

## What it is
An online course in the form of C++ lab assignments for learning low-level performance analysis and tuning, covering issues like CPU cache misses and branch mispredictions. Solutions are submitted to GitHub for automated benchmarking and verification across modern Intel, AMD, and Apple hardware.

## Use cases
- learn low-level performance analysis and tuning
- practice fixing CPU cache misses
- learn to fix branch mispredictions
- improve C++ code performance through hands-on labs
- learn vectorization and compiler intrinsics
- benchmark code optimizations on modern CPUs

## When to choose
- you want hands-on practice optimizing C++ code performance
- you want to learn performance analysis on modern Intel, AMD, or Apple hardware
- you prefer learning through lab assignments and videos rather than theory alone

## When to avoid
- you don't know basic C++
- you need a tool or library rather than a course
- you need a formally licensed open-source project (the repo has no license)

## Facets
- artifact type: learning-resource
- maturity: active
- function: benchmarking, developer-tools
- domain: performance, tutorials, developer-tools
- platform: windows, cpp
- tags: performance-analysis, cpu-optimization, course, lab-assignments, low-level-optimization, computer-architecture, linux, macos

## Member repositories
- dendibakh/perf-ninja (main) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:22.159387+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:26:22.358824+00:00, confidence not recorded.
  - readme: https://github.com/dendibakh/perf-ninja (fetched 2026-08-28T04:08:22.159387+00:00, sha 579ce4556e0e)
- Data as of 2026-08-30T08:39:29.467469+00:00.