Use Profile-Guided Optimization (PGO) to Improve Performance

Train the Compiler With Real Profiles

Profile-Guided Optimization (PGO) runs a instrumented binary on representative input, records edge frequencies, then rebuilds with fprofile-use so the compiler lays out hot branches and inlines the right callees. Chrome, Firefox, and LLVM itself ship PGO-trained builds.

Two-Phase Flow

Phase 1: -fprofile-generate run workload. Phase 2: -fprofile-use recompile using .profraw merged to .profdata.

bash
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Training input must resemble production or hot paths mislead layout
Cold error paths should stay cold in the profile
LTO plus PGO compounds gains on large binaries
BOLT can optimize further on top of PGO binaries

Working Through the Exercise

Keep the relevant documentation open while you implement. When your output disagrees with the reference, trace one failing case by hand before changing random lines.

Trace first: walk one failing input step by step
Read the manual: skim the official doc for the mechanism named in this task
Compare baselines: save measurements from the prior step so speedups are honest

Why for this exercise

You will generate and consume a profile to build a PGO-optimized binary. This exercise requires comparing branch layout or speed against a non-PGO build.

Demonstrate Profile-Guided Optimization (PGO) with performance comparison.

Requirements:

Create program with clear hot/cold code paths
Implement hot path (called frequently)
Implement cold path (rarely called)
Show conditional branches with bias
Compile with -fprofile-generate
Run with workload to generate profile
Recompile with -fprofile-use
Measure performance before/after PGO
Show 10-30% speedup
Explain which optimizations were applied
Print PGO build instructions and results

Train the Compiler With Real Profiles

Two-Phase Flow

Phase 1: -fprofile-generate run workload. Phase 2: -fprofile-use recompile using .profraw merged to .profdata.

bash
Loading…

Training input must resemble production or hot paths mislead layout
Cold error paths should stay cold in the profile
LTO plus PGO compounds gains on large binaries
BOLT can optimize further on top of PGO binaries

Working Through the Exercise

Keep the relevant documentation open while you implement. When your output disagrees with the reference, trace one failing case by hand before changing random lines.

Trace first: walk one failing input step by step
Read the manual: skim the official doc for the mechanism named in this task
Compare baselines: save measurements from the prior step so speedups are honest

Why for this exercise

You will generate and consume a profile to build a PGO-optimized binary. This exercise requires comparing branch layout or speed against a non-PGO build.

Demonstrate Profile-Guided Optimization (PGO) with performance comparison.

Requirements:

Create program with clear hot/cold code paths
Implement hot path (called frequently)
Implement cold path (rarely called)
Show conditional branches with bias
Compile with -fprofile-generate
Run with workload to generate profile
Recompile with -fprofile-use
Measure performance before/after PGO
Show 10-30% speedup
Explain which optimizations were applied
Print PGO build instructions and results

Topic Overview

Train the Compiler With Real Profiles

Two-Phase Flow

Working Through the Exercise

Why for this exercise

Implementation Task

Progressive Hints

Resources

Open Source

Use Profile-Guided Optimization (PGO) to Improve Performance

Topic Overview

Train the Compiler With Real Profiles

Two-Phase Flow

Working Through the Exercise

Why for this exercise

Implementation Task

Progressive Hints

Resources

Open Source