This project is a practical guide to performance analysis and tuning on modern CPUs, bridging microarchitecture details with hands-on profiling. It explains how caches, TLBs, prefetchers, branch predictors, and out-of-order execution influence real program speed, then connects those concepts to concrete optimization strategies. Readers learn how to design trustworthy benchmarks, avoid measurement traps (warmup, turbo, frequency scaling), and interpret hardware performance counters. The book walks through vectorization, memory layout, data-oriented design, and algorithmic choices, illustrating when compiler flags, intrinsics, or hand-rolled assembly make sense. It also demonstrates tool-driven workflows—using profilers and PMU events—to locate true bottlenecks and validate that changes actually help. Throughout, the emphasis is on a methodical loop of hypothesize → measure → change → re-measure, rather than folklore or premature micro-optimizations.

Features

  • Covers CPU microarchitecture, caches, memory hierarchies, branch predictors, vector units, and related performance topics
  • Provides practical tuning advice (code examples, benchmarking, profiling)
  • Source files in LaTeX plus tooling: Python script for export, tools like pandoc, bibtex, pdflatex
  • Glossary, metadata, figures, charts to aid understanding; visual aids (images, diagrams)
  • Supports building PDF output on Windows and Linux (macOS possible but with extra steps)
  • Freely distributable under a permissive (CC0) license; readers have full access to all content

Project Samples

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License

Creative Commons Attribution License

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Additional Project Details

Operating Systems

Linux, Mac, Windows

Programming Language

Python

Related Categories

Python System Software

Registered

2025-09-22