autotune-doctor 0.5.0

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⚡ Executive Summary

Compilers like Clang and GCC use fixed, general-purpose pass pipelines (such as -O3 or -O2). While these standard pipelines perform acceptably across millions of disparate software projects, they are fundamentally suboptimal for any specific compute-intensive workload. Because compiler optimizations are interdependent and non-commutative—a phenomenon known in computer science as the Phase-Ordering Problem—applying Pass A before Pass B can either unlock massive SIMD vectorization or permanently destroy vectorization opportunities.

Autotune transforms compiler optimization from manual guesswork into a reproducible, developer-usable science:

1. Analyzes Workload AST: Extracts loop nesting, memory-to-compute ratios, branch density, and floating-point intensity without modifying source code.
2. Flexible Baseline Benchmarking: Benchmarks against configurable baselines (-O0, -O1, -O2, -O3, or -Os) using nanosecond-precision monotonic hardware timers.
3. Genetic Phase-Ordering Search: Evolves custom sequences of LLVM New Pass Manager (NPM) transformations tailored to your exact silicon microarchitecture (Apple Silicon NEON, AMD Zen, Intel AVX-512).
4. Strict Pluggable Validation: Bitwise, numerical delta, and checksum execution guards guarantee that optimized binaries produce 100% correct outputs.
5. Rigorous Statistical Proof: Re-evaluates winners with Welch’s t-test, Mann-Whitney U rank-sum tests, Cohen's $d$ effect sizes, and Coefficient of Variation (CV%) noise gates.
6. Production Artifact Generation: Emits transformed IR (.optimized.ll), assembly (.s), native binaries (.bin), CMake snippets, Makefiles, and standalone Shell compilation scripts.

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📊 Empirical Proof Benchmarks

The following table presents real, un-fabricated benchmark results measured across curated workloads on Apple Silicon (ARM64) and Linux (x86_64) using LLVM New Pass Manager. Every result is backed by independent multi-sample re-benchmarking, bitwise correctness checks, and Welch's t-test statistical validation:

Workload Kernel	Problem Domain	Baseline Config	Baseline Time	Autotune Time	Confirmed Speedup	Evidence Grade	Primary Compiler Mechanics
2D Convolution (conv2d)	Computer Vision / Image Filtering	-O0	68.90 ms	5.08 ms	13.56× Gain	Grade A	SROA + Loop Rotate + LICM + Vectorize + Unroll
PolyBench GEMM (gemm)	Linear Algebra Matrix Multiplication	-O0	227.26 ms	18.07 ms	12.58× Gain	Grade A	SLP Vectorizer + Early-CSE + GVN + Loop Vectorize
PolyBench BiCG (bicg)	BiConjugate Gradient Linear Solver	-O0	15.32 ms	3.21 ms	4.77× Gain	Grade A	Kernel Loop Pipelining + Interleaved Vectorization
N-Body Gravity (nbody)	Astrophysics Particle Simulation	-O0	41.91 ms	10.33 ms	4.06× Gain	Grade A	SROA + Pairwise Force SIMD + Reassociation
Dense Matrix Mult (matrix_mult)	Scientific Computing / HPC	-O0	46.70 ms	12.60 ms	3.71× Gain	Grade A	Loop Flattening + LICM + Invariant Hoisting
Matrix Transpose (matrix_transpose)	Cache Locality / Strided Memory	-O3	38.48 ms	28.64 ms	1.34× Gain (+30.2%)	Grade A	Stride Restructuring over -O3 + Memory SSA LICM

All runs evaluated with $N=10$ confirmation repetitions, 3 warmup iterations, CV noise $< 5.0%$, and $p < 0.0001$.

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🥊 Why Autotune vs. Status Quo?

Feature	Standard clang -O3	OpenTuner / GCC Plugins	CompilerGym	Autotune Doctor
Workload-Tailored Passes	❌ Fixed Pipeline	⚠️ Heuristic Only	⚠️ Research Only	✅ Fully Automated GA + AI
LLVM NPM Support	✅ Built-in	❌ Legacy Pass Manager	❌ Deprecated Pass Mgr	✅ Full Modern NPM (LLVM 14-22)
Configurable Baselines	❌ None	❌ Ad-hoc	❌ -O3 Only	✅ -O0, -O1, -O2, -O3, -Os
Statistical Proof Engine	❌ None	❌ Mean/Median Only	❌ Reward Score Only	✅ Welch $t$-test, Mann-Whitney, Cohen $d$
Bitwise Output Verification	❌ None	⚠️ Exit Code Only	❌ Emulated	✅ Exact, Numeric, Checksum, Script
Production Build Export	❌ CLI Flags Only	❌ Manual Shell Script	❌ Python Object Only	✅ CMake, Makefile, Shell, C Header
Zero-Knowledge Security	N/A	❌ Plaintext configs	❌ N/A	✅ Native OS Keyring Isolation
CI Regression Gate	❌ None	❌ Custom Scripting	❌ N/A	✅ Native autotune guard with exit codes

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🔬 System Workflow Architecture

flowchart TD
    A[C/C++ Workload Source] --> B[AST Feature Extraction & Profiling]
    B --> C[Empirical Baseline Benchmarking: -O0 / -O1 / -O2 / -O3]
    C --> D{Pipeline Seeding}
    D -->|Offline Heuristics| E[Microarchitecture-Biased Hardware Seeds]
    D -->|LLM Guidance| F[AI Pass Candidate Synthesis]
    E --> G[Genetic Algorithm Phase-Order Evolution Engine]
    F --> G
    G --> H[LLVM Pipeline Compilation: clang -> opt NPM -> native binary]
    H --> I[Pluggable Correctness Validation Gate]
    I -->|Failed / Miscompiled| J[Automatic Discard & Crash Analysis]
    I -->|Verified Correct| K[Fresh Multi-Sample Confirmation Benchmarking]
    K --> L[Statistical Verification: Welch t-test, Mann-Whitney U, Cohen's d]
    L --> M[Evidence Grading: Grade A / B / C / D / F]
    M --> N[Structured JSON & Interactive Standalone HTML Reports]
    N --> O[autotune apply / export / guard / reproduce]

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🚀 Quick Start

1. Installation

# Install from PyPI:
pip install autotune-doctor

# Or install latest developer build:
git clone https://github.com/youknowme19/Autotune-The-Phase-Ordering-CLI-Doctor.git
cd Autotune-The-Phase-Ordering-CLI-Doctor
pip install -e ".[dev]"

2. Verify Toolchain Health

autotune doctor

Autotune v0.5.0
Phase-Ordering CLI Doctor

                         System & Toolchain Diagnostics                         
┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Check Component     ┃ Status ┃ Details                                       ┃
┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ Python Version      │ [OK]   │ 3.11.15                                       │
│ OS & Architecture   │ [OK]   │ Darwin (arm64 - Apple Silicon)                │
│ Clang Compiler      │ [OK]   │ /opt/homebrew/opt/llvm/bin/clang (v22.1.8)    │
│ LLVM Opt Binary     │ [OK]   │ /opt/homebrew/opt/llvm/bin/opt (v22.1.8)      │
│ Measurement Backend │ [OK]   │ macOS high-precision monotonic timing         │
└─────────────────────┴────────┴───────────────────────────────────────────────┘

3. Optimize a Workload (Flagship Command)

autotune doctor examples/conv2d/kernel.c --preset quick --baseline -O0

╭──────────────────────────────────────────────────────────╮
│ AUTOTUNE DOCTOR                                          │
│ AI-Guided LLVM Phase-Ordering Optimization & Diagnostics │
╰──────────────────────────────────────────────────────────╯
Analyzing kernel.c (Preset: quick)...
  Gen 01/04 [━━━━━╸──────────────] | Best:  48.494 ms | Gain:  1.47x | Diversity: 1.00 | Valid: 8
  Gen 02/04 [━━━━━━━━━━╸─────────] | Best:  48.494 ms | Gain:  1.47x | Diversity: 0.62 | Valid: 7
  Gen 03/04 [━━━━━━━━━━━━━━━╸────] | Best:  48.037 ms | Gain:  1.48x | Diversity: 0.88 | Valid: 7
  Gen 04/04 [━━━━━━━━━━━━━━━━━━━━╸] | Best:  45.998 ms | Gain:  1.55x | Diversity: 0.88 | Valid: 8

🏆 OPTIMIZATION FOUND
  - Baseline (-O0):  71.217 ms
  - Autotune Winner: 45.998 ms
  - Confirmed Gain:  1.55× (Grade A — IMPROVED)
  - Correctness:     ✓ PASS
  - Statistical p:   0.0000 (Cohen's d: 56.27)

Winning pass pipeline:
  sroa → loop-rotate → licm → instcombine → loop-unroll

Artifacts:
  - JSON Report: .autotune/runs/run_20260905_195221_f6b2f20b/report.json
  - HTML Report: .autotune/runs/run_20260905_195221_f6b2f20b/report.html

Reproduce:
  autotune reproduce .autotune/runs/run_20260905_195221_f6b2f20b/report.json

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🛠️ CLI Command Reference

autotune doctor

Flagship end-to-end workload optimization, evidence grading, and diagnostics.

autotune doctor <source> [OPTIONS]

Options:
  -b, --baseline TEXT                   Baseline optimization level [-O0|-O1|-O2|-O3|-Os|-Oz] (default: -O3)
  -p, --preset [quick|balanced|aggressive] Search preset budget (default: balanced)
  -w, --workload PATH                   Path to stdin workload input file
  --args TEXT                           Space-separated argv arguments passed to executable
  --correctness-strategy [exitcode|numeric] Validation verification strategy
  -o, --output-json PATH                Path to export structured JSON report
  --export-sh PATH                      Export standalone reproduction shell script
  --no-llm                              Enforce 100% offline heuristic search (zero network calls)

autotune profile

Inspects AST structural complexity, loop depths, memory vs compute intensity, and suggests pass families.

autotune profile examples/matrix_transpose/kernel.c
# Machine-readable JSON output for tooling pipelines:
autotune profile examples/matrix_transpose/kernel.c --json

autotune explain

Generates human-readable, scientifically grounded explanations of why the winning pass sequence outperformed baseline.

autotune explain .autotune/runs/run_latest/report.json

Explicitly deconstructs performance gains into:

• Observed Facts: Verified empirical runtimes, standard deviation, p-value, instruction delta.
• Inferred Compiler Mechanics: Concrete transformations executed by each LLVM pass.
• Hypothesized Synergies: Inter-pass interactions (e.g., SROA scalarization enabling loop vectorization).

autotune apply

Applies the winning pass pipeline and generates production compiler artifacts without mutating user source code.

autotune apply report.json --output-dir ./build/autotune/

Emits:

• kernel.ll — Raw unoptimized LLVM IR.
• kernel.optimized.ll — Bitcode transformed by the winning pass sequence.
• kernel.s — Native target machine assembly.
• kernel.bin — Compiled native binary executable.
• manifest.json — Toolchain triple, SHA-256 provenance hashes, and exact compilation flags.

autotune export

Exports reproducible compilation recipes into standard build systems.

# Export CMake integration snippet:
autotune export report.json --format cmake -o autotune.cmake

# Export Makefile snippet:
autotune export report.json --format make -o Makefile.autotune

# Export standalone shell script:
autotune export report.json --format shell -o build_optimized.sh

autotune guard

Continuous Integration performance gate preventing speed and correctness regressions.

# Fail CI build if candidate regresses by more than 3%:
autotune guard examples/matrix_transpose/kernel.c --reference report.json --threshold 0.03

# Strict environment mode (fails if CPU architecture or LLVM major version changes):
autotune guard examples/matrix_transpose/kernel.c --reference report.json --strict-env --ci

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🛡️ Continuous Integration Guard Gate

Integrate Autotune directly into your GitHub Actions workflow to block regressions in pull requests:

name: Performance Regression Guard

on:
  push:
    branches: [ main ]
  pull_request:
    branches: [ main ]

jobs:
  autotune-guard:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: "3.11"
      - name: Install LLVM & Clang
        run: sudo apt-get update && sudo apt-get install -y clang llvm llvm-dev
      - name: Install Autotune
        run: pip install autotune-doctor
      - name: Verify Performance Gate
        run: |
          autotune guard src/kernel.c --reference benchmarks/golden_report.json --threshold 0.05 --ci

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📈 Statistical Rigor & Evidence Grades

Autotune does not rely on single-run measurements. Every candidate is evaluated using interleaved runs, Coefficient of Variation (CV%) noise checks, and non-parametric statistical tests:

• Welch's t-test: Parametric two-sample t-test for unequal variances ($p$-value).
• Mann-Whitney U test: Non-parametric rank-sum test robust against timing outliers.
• Cohen's d: Standardized effect size quantifying practical optimization impact ($d > 0.8$ denotes large effect).

Evidence Grading Matrix

Grade	Classification	Speedup ($\times$)	Statistical Criteria	Action
A	IMPROVED	$\ge 1.05\times$	$p < 0.05$, $d > 0.5$, CV $< 10%$	Eligible for Production & Knowledge Store
B	MODEST_GAIN	$1.02\times - 1.05\times$	$p < 0.05$, $d > 0.2$, CV $< 12%$	Statistically significant minor improvement
C	NOISY_IMPROVEMENT	$> 1.02\times$	$p \ge 0.05$ or High Variance	Improvement observed but noisy
D	PARITY	$0.98\times - 1.02\times$	Indistinguishable from Baseline	Optimization neutral
F	REGRESSION / FAIL	$< 0.98\times$ or Error	Incorrect output, crash, or slowdown	Immediate Discard

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🔒 Security & Privacy

• Zero Secret Leakage: API keys stored via autotune config keyring utilize native OS Keyring mechanisms (Keychain on macOS, SecretService on Linux) and are never logged, serialized into JSON reports, or committed.
• 100% Offline Capability: With --no-llm, Autotune runs entirely locally using AST heuristics and genetic algorithms with zero network calls.
• Non-Destructive: autotune apply outputs to .autotune/artifacts/ or custom directories; original source files are never altered.

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📄 License & Citations

Autotune is distributed under the Apache 2.0 License. See LICENSE for details.

If you use Autotune in your research or production systems, please cite:

@software{autotune_doctor_2026,
  author = {Autotune Contributors},
  title = {Autotune: AI-Guided LLVM Phase-Ordering Optimization & Diagnostics System},
  year = {2026},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/youknowme19/Autotune-The-Phase-Ordering-CLI-Doctor}}
}