hakmem/PHASE4_STEP1_COMPLETE.md

Phase 4-Step1: PGO Workflow - COMPLETE ✓

Date: 2025-11-29 Status: ✅ Complete Performance Gain: +6.25% (57.0 → 60.6 M ops/s)

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Summary

Phase 4-Step1 implemented a fully automated PGO (Profile-Guided Optimization) workflow for the HAKMEM Tiny Front using the Box pattern. The implementation achieved a +6.25% performance improvement (within the expected +5-10% range) with zero code changes - pure compiler optimization.

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Implementation

Box 1: PGO Profile Collection Box

Purpose: Automated, reproducible profile data collection Contract: Execute representative workloads → Generate .gcda files

Components:

1. scripts/box/pgo_tiny_profile_config.sh - Workload configuration
2. scripts/box/pgo_tiny_profile_box.sh - Profile collection automation
3. Makefile PGO targets - Workflow orchestration

Design Principles:

• ✅ Deterministic: Fixed seeds (42) for reproducibility
• ✅ Representative: 5 workloads covering diverse allocation patterns
• ✅ Automated: Single command (make pgo-tiny-full) for complete workflow
• ✅ Safe: Validation checks, error detection, timeout protection
• ✅ Observable: Clear progress reporting, .gcda file verification

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Workload Design

The PGO profile collection uses 5 representative workloads to capture diverse allocation patterns:

Workload	Purpose	Key Characteristics
bench_random_mixed 5M 256 42	Common case	Medium working set, balanced cache pressure
bench_random_mixed 5M 128 42	Hot path bias	Smaller working set, higher TLS cache hit rate
bench_random_mixed 5M 512 42	Cold path bias	Larger working set, more SuperSlab allocations
bench_tiny_hot 16 100 60000	Class 0 intensive	Smallest size class (16B)
bench_tiny_hot 64 100 60000	Class 3 intensive	Common small object size (64B)

Coverage: The workloads exercise:

• Hot TLS SLL pop path (high-frequency allocations)
• Cold refill path (SuperSlab allocations)
• Multiple size classes (0, 3, and mixed)
• Varied cache pressure scenarios

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Makefile Targets

# Step 1: Build instrumented binaries (-fprofile-generate)
make pgo-tiny-profile

# Step 2: Collect profile data (run workloads → .gcda files)
make pgo-tiny-collect

# Step 3: Build optimized binaries (-fprofile-use)
make pgo-tiny-build

# Full workflow: profile → collect → build → test
make pgo-tiny-full

Default Goal: The Makefile help target now includes PGO instructions (lines 18-23)

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Performance Results

Baseline (No PGO)

Run 1: 57.04 M ops/s
Run 2: 57.14 M ops/s
Run 3: 56.95 M ops/s
Average: 57.04 M ops/s

PGO-Optimized

Run 1: 60.49 M ops/s
Run 2: 60.68 M ops/s
Run 3: 60.66 M ops/s
Average: 60.61 M ops/s

Improvement

Absolute: +3.57 M ops/s
Relative: +6.25%
Expected: +5-10% ✓

Verification: Latest test (after Makefile fix) confirmed 60.75 M ops/s - consistent with expected performance.

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Technical Details

Profile Data Collection

The pgo_tiny_profile_box.sh script implements a robust collection workflow:

1. Binary Validation

   • Checks binaries exist and are executable
   • Auto-fixes permissions if needed

2. Profile Cleanup

   • Removes old .gcda files to prevent stale data
   • Reports cleanup statistics

3. Workload Execution

   • Runs each workload with 30s timeout
   • Detects timeouts and failures
   • Fails fast on errors

4. Profile Verification

   • Confirms .gcda files were generated
   • Reports profile file count and locations
   • Detects missing -fprofile-generate flag

Output: 33 .gcda files (confirmed in latest run)

Compiler Flags

# Profile Generation (Step 1)
PROFILE_GEN_FLAGS = -fprofile-generate -flto

# Profile Use (Step 3)
PROFILE_USE_FLAGS = -fprofile-use -flto

LTO: Link-Time Optimization is enabled for both phases to maximize PGO effectiveness.

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Workflow Fix (2025-11-29)

Issue: Initial implementation had pgo-tiny-build calling the profile collection script, causing:

• Duplicate script execution
• Unclear separation of concerns
• Skipped pgo-tiny-collect in dependency chain

Fix: Cleaned up the workflow:

# Before (broken):
pgo-tiny-full: pgo-tiny-profile pgo-tiny-build  # Missing collect!

# After (correct):
pgo-tiny-full: pgo-tiny-profile pgo-tiny-collect pgo-tiny-build

Result: Each target now has a single responsibility:

• pgo-tiny-profile: Build only
• pgo-tiny-collect: Collect only
• pgo-tiny-build: Build only
• pgo-tiny-full: Orchestrate all steps

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Help Target Update

The Makefile help target (lines 8-37) now includes:

Benchmarking (PGO-optimized, +6% faster):
  make pgo-tiny-full                - Full PGO workflow (~5-10 min)
                                      = Profile + Optimize + Test
  make pgo-tiny-profile             - Step 1: Build profile binaries
  make pgo-tiny-collect             - Step 2: Collect profile data
  make pgo-tiny-build               - Step 3: Build optimized

Phase 4 Performance:
  Baseline:      57.0 M ops/s
  PGO-optimized: 60.6 M ops/s (+6.25%)

TIP: For best performance, use 'make pgo-tiny-full'

This ensures developers won't forget how to use PGO builds.

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Artifacts

New Files

• scripts/box/pgo_tiny_profile_config.sh - Workload definitions
• scripts/box/pgo_tiny_profile_box.sh - Collection automation
• PHASE4_STEP1_COMPLETE.md - This completion report

Modified Files

• Makefile (lines 8-37) - Help target with PGO instructions
• Makefile (lines 1305-1356) - PGO workflow targets

Documentation

• CURRENT_TASK.md - Phase 4 roadmap
• docs/design/PHASE4_TINY_FRONT_BOX_DESIGN.md - Complete Box design

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Box Pattern Compliance

✅ Single Responsibility: Profile collection is a separate Box ✅ Clear Contract: Workloads → .gcda files → Optimized binaries ✅ Observable: Progress reporting, error detection, summary statistics ✅ Safe: Validation, timeouts, fail-fast on errors ✅ Testable: Deterministic seeds for reproducibility

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Next Steps

Phase 4-Step2: Hot/Cold Path Box

• Target: +10-15% improvement (60.6 → 70.0 M ops/s)
• Approach: Separate hot (inline, likely) and cold (noinline, unlikely) paths
• Design: Already specified in PHASE4_TINY_FRONT_BOX_DESIGN.md

Phase 4-Step3: Front Config Box

• Target: +5-8% improvement (70.0 → 76.0 M ops/s)
• Approach: Compile-time config optimization
• Design: Already specified in design doc

Overall Phase 4 Target: 73-83 M ops/s (vs current 60.6 M ops/s)

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Lessons Learned

1. PGO is high ROI: +6.25% with zero code changes, ~30 minutes of work
2. Representative workloads matter: 5 diverse workloads > 1 simple workload
3. Automation is critical: Manual PGO workflows are error-prone
4. Box pattern scales: Profile collection fits the Box pattern naturally
5. Help targets prevent forgetting: Make workflows discoverable

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Conclusion

Phase 4-Step1 successfully implemented PGO optimization using the Box pattern, achieving +6.25% performance improvement (57.0 → 60.6 M ops/s) with:

• ✅ Fully automated workflow (make pgo-tiny-full)
• ✅ Reproducible results (deterministic seeds)
• ✅ Clear documentation (help target, design doc)
• ✅ Robust error handling (validation, timeouts)
• ✅ Within expected range (+5-10%)

Status: Ready to proceed to Phase 4-Step2 (Hot/Cold Path Box)

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Signed: Claude (2025-11-29) Commit: TBD (pending git commit)