# CUDA-SIMD Technical Debt Gradient (TDG) Specification

**Version**: 1.0.0
**Status**: Draft
**Authors**: PAIML Team
**Date**: 2025-01-08

## Abstract

This specification defines a comprehensive Technical Debt Gradient (TDG) system for CUDA PTX, SIMD (AVX2/AVX-512/NEON), and WGPU compute code. The system integrates Toyota Production System principles with Karl Popper's falsificationist methodology to create a 100-point defect detection and quality scoring framework.

## 1. Introduction

### 1.1 Motivation

GPU and SIMD code presents unique quality challenges not addressed by traditional static analysis:

1. **Thread Divergence Bugs** (PARITY-114): Early thread exit before synchronization barriers
2. **Memory Coalescing Violations**: Non-contiguous access patterns degrading throughput
3. **Shared Memory Bank Conflicts**: Strided access causing serialization
4. **Register Pressure**: Excessive register usage reducing occupancy
5. **Tile Size Violations**: Mismatched tile dimensions causing bounds errors

The Tauranta fault history across trueno, aprender, and realizar repositories reveals 41+ distinct GPU optimization tickets (PAR-001 through PAR-041) and critical barrier safety bugs (PARITY-114 through PARITY-117).

### 1.2 Design Philosophy

This specification follows the Toyota Production System (TPS) principles:

- **Genchi Genbutsu** (現地現物): Analyze actual PTX/SIMD artifacts, not abstractions
- **Jidoka** (自働化): Automatic quality gates that stop on defect detection
- **Kaizen** (改善): Continuous improvement through historical fault analysis
- **Hansei** (反省): Root cause analysis with 5-Why methodology
- **Poka-Yoke** (ポカヨケ): Error-proofing through static analysis

## 2. Fault Taxonomy

### 2.1 Critical Defect Classes (Severity: P0)

| ID | Pattern | Description | Detection Method |
|----|---------|-------------|------------------|
| PARITY-114 | Barrier Divergence | Thread exits before `bar.sync` | PTX CFG analysis |
| PAR-002 | GEMV Error 700 | Illegal memory access in GEMV | Bounds checking |
| PAR-041 | FlashAttention OOB | tile_kv < head_dim shared memory overflow | Tile dimension validation |

### 2.2 Performance Defect Classes (Severity: P1)

| ID | Pattern | Description | Detection Method |
|----|---------|-------------|------------------|
| PAR-001 | Q4_K Dequant | Inefficient dequantization | Throughput analysis |
| PAR-020 | Incremental Attention | Per-token attention overhead | Memory access pattern |
| PAR-030 | Fused RMSNorm+GEMV | Missing kernel fusion | Kernel launch count |
| PAR-034 | Tensor Core GEMM | Unused Tensor Core capability | SM utilization |
| PAR-036 | Persistent Threads | Non-persistent kernel design | Thread lifecycle |
| PAR-037 | CUDA Graphs | Missing graph capture | API call pattern |
| PAR-039 | Megakernel Fusion | Excessive kernel launches | Launch overhead |

### 2.3 Memory Efficiency Classes (Severity: P2)

| ID | Pattern | Description | Detection Method |
|----|---------|-------------|------------------|
| PAR-005 | Weight Caching | Redundant weight transfers | D2H/H2D ratio |
| PAR-023 | D2D Copy | Inefficient device-to-device | Memory copy pattern |
| PAR-028 | FP16 KV Cache | FP32 KV cache overhead | Precision analysis |
| PAR-032 | FP16 I/O | Mixed precision I/O | Data type flow |

## 3. 100-Point Popper Falsification Score

### 3.1 Scoring Framework

The CUDA-SIMD TDG score follows Popper's falsificationist epistemology: code must be testable, and tests must be capable of proving defects. The score comprises six categories:

```
Total Score = A + B + C + D + E + F

Where:
  A = Falsifiability & Testability (25 points) - GATEWAY
  B = Reproducibility Infrastructure (25 points)
  C = Transparency & Openness (20 points)
  D = Statistical Rigor (15 points)
  E = Historical Integrity (10 points)
  F = GPU/SIMD Specific (5 points)

GATEWAY RULE: If A < 15, Total Score = 0
```

### 3.2 Category A: Falsifiability & Testability (25 points)

| Criterion | Points | Description |
|-----------|--------|-------------|
| A.1 Barrier Safety | 5 | All `bar.sync` reachable from all threads |
| A.2 Bounds Verification | 5 | Shared memory indices within tile dimensions |
| A.3 Divergence Testing | 5 | Branch coverage includes warp divergence cases |
| A.4 Memory Race Detection | 5 | ThreadSanitizer or equivalent analysis |
| A.5 Occupancy Bounds | 5 | Register/shared memory within SM limits |

**Scoring Rules:**
- Full points: Criterion fully satisfied with automated verification
- Half points: Manual verification without automation
- Zero points: No verification

### 3.3 Category B: Reproducibility Infrastructure (25 points)

| Criterion | Points | Description |
|-----------|--------|-------------|
| B.1 Deterministic Output | 8 | Bitwise reproducible results |
| B.2 Version Pinning | 5 | CUDA/Driver/SM version locked |
| B.3 Hardware Specification | 5 | GPU model, compute capability documented |
| B.4 Benchmark Harness | 4 | Criterion-style statistical benchmarking |
| B.5 CI/CD Integration | 3 | Automated regression on GPU hardware |

### 3.4 Category C: Transparency & Openness (20 points)

| Criterion | Points | Description |
|-----------|--------|-------------|
| C.1 PTX Inspection | 6 | Generated PTX accessible and documented |
| C.2 Register Allocation | 5 | `--ptxas-options=-v` output analyzed |
| C.3 Occupancy Calculation | 5 | SM occupancy explicitly computed |
| C.4 Memory Layout | 4 | Shared memory bank mapping documented |

### 3.5 Category D: Statistical Rigor (15 points)

| Criterion | Points | Description |
|-----------|--------|-------------|
| D.1 Warmup Iterations | 4 | ≥3s warmup before measurement |
| D.2 Sample Count | 4 | ≥100 samples for statistical significance |
| D.3 Outlier Analysis | 4 | IQR-based outlier detection reported |
| D.4 Confidence Intervals | 3 | 95% CI on throughput metrics |

### 3.6 Category E: Historical Integrity (10 points)

| Criterion | Points | Description |
|-----------|--------|-------------|
| E.1 Fault Lineage | 4 | PARITY/PAR ticket references |
| E.2 Regression Tests | 3 | Tests derived from historical bugs |
| E.3 Root Cause Documentation | 3 | 5-Why analysis for each P0 defect |

### 3.7 Category F: GPU/SIMD Specific (5 points)

| Criterion | Points | Description |
|-----------|--------|-------------|
| F.1 Warp Efficiency | 2 | Active threads / warp size ratio |
| F.2 Memory Throughput | 2 | Achieved vs theoretical bandwidth |
| F.3 Instruction Mix | 1 | FMA/memory instruction ratio |

## 4. Detection Algorithms

### 4.1 Barrier Safety Analysis (PARITY-114)

```rust
/// Detects early-exit-before-barrier bugs in PTX
pub fn analyze_barrier_safety(ptx: &str) -> BarrierSafetyResult {
    let cfg = build_control_flow_graph(ptx);
    let barriers = find_all_barriers(&cfg); // bar.sync, bar.arrive, etc.

    for barrier in barriers {
        let dominator_tree = compute_dominators(&cfg, barrier.block);
        let reaching_threads = compute_reaching_definitions(&cfg, barrier);

        // Check: Can any thread exit before reaching this barrier?
        for exit_node in cfg.exit_nodes() {
            if !dominates(barrier.block, exit_node, &dominator_tree) {
                // PARITY-114: Thread can exit without reaching barrier
                report_defect(DefectClass::BarrierDivergence {
                    barrier_location: barrier.location,
                    exit_path: find_path(&cfg, entry, exit_node),
                });
            }
        }
    }
}
```

### 4.2 Tile Dimension Validation (PAR-041)

```rust
/// Validates FlashAttention tile dimensions
pub fn validate_tile_dimensions(kernel: &AttentionKernel) -> TileResult {
    let tile_kv = kernel.tile_kv();
    let head_dim = kernel.head_dim();

    // PAR-041: tile_kv must be >= head_dim for correct attention
    if tile_kv < head_dim {
        return TileResult::Defect(DefectClass::TileSizeViolation {
            tile_kv,
            head_dim,
            required: head_dim,
            ticket: "PAR-041",
        });
    }

    // Validate shared memory bounds
    let shared_required = tile_kv * head_dim * size_of::<f16>();
    let shared_available = kernel.shared_memory_size();

    if shared_required > shared_available {
        return TileResult::Defect(DefectClass::SharedMemoryOverflow {
            required: shared_required,
            available: shared_available,
        });
    }

    TileResult::Valid
}
```

### 4.3 Memory Coalescing Analysis

```rust
/// Analyzes memory access patterns for coalescing
pub fn analyze_coalescing(ptx: &str) -> CoalescingResult {
    let memory_ops = extract_memory_operations(ptx);
    let mut coalescing_score = 0.0;

    for op in memory_ops {
        match op.access_pattern {
            AccessPattern::Contiguous { stride: 1 } => {
                coalescing_score += 1.0;
            }
            AccessPattern::Strided { stride } if stride <= 32 => {
                // Partial coalescing possible
                coalescing_score += 32.0 / stride as f64;
            }
            AccessPattern::Random => {
                // No coalescing
                coalescing_score += 0.0;
            }
        }
    }

    CoalescingResult {
        efficiency: coalescing_score / memory_ops.len() as f64,
        problematic_accesses: memory_ops.iter()
            .filter(|op| matches!(op.access_pattern, AccessPattern::Random))
            .collect(),
    }
}
```

## 5. Toyota Way Integration

### 5.1 Jidoka Quality Gates

The cuda-tdg sub-command implements Jidoka (自働化) - automation with a human touch:

```
┌─────────────────────────────────────────────────────────────┐
│                     Jidoka Quality Gate                     │
├─────────────────────────────────────────────────────────────┤
│  Gate 1: Barrier Safety    ──► STOP if PARITY-114 detected │
│  Gate 2: Bounds Checking   ──► STOP if OOB possible        │
│  Gate 3: Occupancy         ──► WARN if < 50%               │
│  Gate 4: Coalescing        ──► WARN if < 80% efficiency    │
│  Gate 5: Throughput        ──► INFO if < target            │
└─────────────────────────────────────────────────────────────┘
```

### 5.2 Kaizen Continuous Improvement

Historical fault data drives continuous improvement:

```rust
pub struct KaizenMetrics {
    /// Number of PARITY/PAR tickets resolved
    pub tickets_resolved: u32,
    /// Mean time to detect defects (hours)
    pub mttd: f64,
    /// Mean time to fix defects (hours)
    pub mttf: f64,
    /// Defect escape rate (defects found in production / total defects)
    pub escape_rate: f64,
    /// Regression rate (% of fixes that regressed)
    pub regression_rate: f64,
}
```

### 5.3 Hansei Root Cause Analysis

Each P0 defect requires 5-Why analysis:

```markdown
## PARITY-114: Barrier Divergence Bug

### 5-Why Analysis

1. **Why did CUDA error 700 occur?**
   Thread accessed invalid memory after barrier desynchronization.

2. **Why did barrier desynchronization occur?**
   Some threads exited kernel early via conditional return.

3. **Why did threads exit early?**
   Early-exit optimization for zero-value inputs.

4. **Why wasn't this caught in testing?**
   Unit tests used uniform inputs; divergent paths untested.

5. **Why weren't divergent paths tested?**
   No systematic warp divergence test generation.

### Countermeasure
Static PTX analysis detecting exit-before-barrier patterns.
```

## 6. Command-Line Interface

### 6.1 Primary Commands

```bash
# Analyze PTX file for defects
batuta cuda-tdg analyze kernel.ptx

# Score CUDA codebase with 100-point system
batuta cuda-tdg score --path ./src/kernels/

# Generate defect report
batuta cuda-tdg report --format html --output report.html

# Check barrier safety specifically
batuta cuda-tdg barrier-check kernel.ptx

# Validate tile dimensions for attention kernels
batuta cuda-tdg validate-tiles --head-dim 128 --tile-kv 64
```

### 6.2 Integration Commands

```bash
# CI/CD quality gate (exits non-zero on P0 defects)
batuta cuda-tdg gate --min-score 85

# Compare against baseline
batuta cuda-tdg diff --baseline main --current HEAD

# Generate Kaizen report
batuta cuda-tdg kaizen --since 2024-01-01
```

### 6.3 Output Formats

```bash
# JSON for tooling
batuta cuda-tdg score --format json

# SARIF for IDE integration
batuta cuda-tdg analyze --format sarif

# Terminal with colors
batuta cuda-tdg score --format terminal
```

## 7. Metrics Collection

### 7.1 Static Metrics

| Metric | Description | Target |
|--------|-------------|--------|
| barrier_safety_score | % of barriers with guaranteed convergence | 100% |
| bounds_check_coverage | % of memory ops with bounds validation | 100% |
| register_pressure | Registers per thread | < 64 |
| shared_memory_usage | Shared memory per block | < 48KB |
| warp_divergence_sites | Number of divergent branches | Minimize |

### 7.2 Dynamic Metrics

| Metric | Description | Target |
|--------|-------------|--------|
| achieved_occupancy | Active warps / max warps | > 50% |
| memory_throughput | GB/s achieved | > 80% theoretical |
| compute_throughput | GFLOPS achieved | > 60% theoretical |
| warp_efficiency | Active threads / warp size | > 90% |
| bank_conflict_rate | Conflicted accesses / total | < 5% |

### 7.3 Historical Metrics

| Metric | Description | Trend |
|--------|-------------|-------|
| defect_density | Defects per KLOC | Decreasing |
| mttd | Mean time to detection | Decreasing |
| mttf | Mean time to fix | Decreasing |
| escape_rate | Production defects / total | Decreasing |
| regression_rate | Regressions / fixes | Decreasing |

## 8. Peer-Reviewed Citations

### 8.1 GPU Architecture & Optimization

1. Volkov, V., & Demmel, J. W. (2008). "Benchmarking GPUs to tune dense linear algebra." *SC '08: Proceedings of the 2008 ACM/IEEE Conference on Supercomputing*, 1-11. https://doi.org/10.1109/SC.2008.5214359

2. Dao, T., et al. (2022). "FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness." *Advances in Neural Information Processing Systems*, 35, 16344-16359.

3. NVIDIA Corporation. (2023). "CUDA C++ Best Practices Guide." NVIDIA Developer Documentation. https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/

4. Gregg, B., & Hazelwood, K. (2011). "The 5× PCIe Rule: When GPU Kernel Launch Overhead Dominates." *NVIDIA GTC 2011*.

### 8.2 SIMD Programming

5. Intel Corporation. (2024). "Intel 64 and IA-32 Architectures Optimization Reference Manual." Intel Developer Zone.

6. Lemire, D. (2018). "Stream VByte: Faster Byte-Oriented Integer Compression." *Information Processing Letters*, 130, 1-6. https://doi.org/10.1016/j.ipl.2017.09.011

7. Langdale, G., & Lemire, D. (2019). "Parsing Gigabytes of JSON per Second." *The VLDB Journal*, 28(6), 941-960.

### 8.3 Quality & Testing

8. Popper, K. R. (1959). *The Logic of Scientific Discovery*. Routledge.

9. Liker, J. K. (2004). *The Toyota Way: 14 Management Principles from the World's Greatest Manufacturer*. McGraw-Hill.

10. Ohno, T. (1988). *Toyota Production System: Beyond Large-Scale Production*. Productivity Press.

11. Shingo, S. (1986). *Zero Quality Control: Source Inspection and the Poka-Yoke System*. Productivity Press.

### 8.4 Static Analysis

12. Cousot, P., & Cousot, R. (1977). "Abstract Interpretation: A Unified Lattice Model for Static Analysis of Programs." *POPL '77*, 238-252.

13. Ferrante, J., Ottenstein, K. J., & Warren, J. D. (1987). "The Program Dependence Graph and Its Use in Optimization." *ACM TOPLAS*, 9(3), 319-349.

14. Cytron, R., et al. (1991). "Efficiently Computing Static Single Assignment Form and the Control Dependence Graph." *ACM TOPLAS*, 13(4), 451-490.

### 8.5 Reproducibility in Computing

15. Collberg, C., & Proebsting, T. A. (2016). "Repeatability in Computer Systems Research." *Communications of the ACM*, 59(3), 62-69.

16. Peng, R. D. (2011). "Reproducible Research in Computational Science." *Science*, 334(6060), 1226-1227.

## 9. Implementation Roadmap

### Phase 1: Core Analysis (MVP)
- [ ] PTX parser with CFG construction
- [ ] Barrier safety analysis (PARITY-114)
- [ ] Basic 100-point scoring
- [ ] CLI interface

### Phase 2: Deep Analysis
- [ ] Memory coalescing analysis
- [ ] Register pressure analysis
- [ ] Occupancy calculation
- [ ] SARIF output for IDE integration

### Phase 3: Historical Integration
- [ ] PARITY/PAR ticket tracking
- [ ] Kaizen metrics collection
- [ ] Regression test generation
- [ ] CI/CD quality gates

### Phase 4: Advanced Features
- [ ] WGPU shader analysis
- [ ] AVX2/AVX-512/NEON SIMD analysis
- [ ] Cross-platform comparison
- [ ] Performance prediction models

## 10. Appendix A: Fault History

### A.1 PARITY Series (Critical Bugs)

| Ticket | Title | Status | Resolution |
|--------|-------|--------|------------|
| PARITY-114 | Barrier Safety Bug | Resolved | Static PTX analysis |
| PARITY-116 | Q5_K Kernel Spec | Resolved | Implemented |
| PARITY-117 | Q6_K Kernel Spec | Resolved | Implemented |

### A.2 PAR Series (Optimizations)

| Ticket | Title | Status |
|--------|-------|--------|
| PAR-001 | Q4_K Dequantization | Resolved |
| PAR-002 | CUDA GEMV Error 700 | Resolved |
| PAR-003 | Q4_K/Q5_K/Q6_K GEMV | Resolved |
| PAR-005 | GPU Weight Caching | Resolved |
| PAR-020 | Incremental Attention | Resolved |
| PAR-021 | GQA Support | Resolved |
| PAR-023 | D2D Memory Copy | Resolved |
| PAR-028 | FP16 KV Cache | Resolved |
| PAR-030 | Fused RMSNorm+GEMV | Resolved |
| PAR-031 | Tiled Q4K GEMV | Resolved |
| PAR-032 | FP16 I/O | Resolved |
| PAR-034 | Tensor Core GEMM | Resolved |
| PAR-036 | Persistent Threads | Resolved |
| PAR-037 | CUDA Graphs | Resolved |
| PAR-038 | Multi-Stream Pipeline | Resolved |
| PAR-039 | Megakernel Fusion | Resolved |
| PAR-040 | Showcase Benchmark | Resolved |
| PAR-041 | FlashAttention Head Dim | Resolved |

## 11. Appendix B: Score Interpretation

| Score Range | Grade | Interpretation |
|-------------|-------|----------------|
| 90-100 | A+ | Production-ready, minimal debt |
| 80-89 | A | Production-ready with monitoring |
| 70-79 | B | Acceptable, prioritize improvements |
| 60-69 | C | Technical debt accumulating |
| 50-59 | D | Significant remediation needed |
| 0-49 | F | Not production-ready |
| 0 (Gateway) | FAIL | Falsifiability requirements not met |

---

*This specification is maintained by the PAIML team and follows semantic versioning.*
