# Entry 035 Baseline Results: Minimizer Extraction Performance

**Date**: November 6, 2025
**Status**: COMPLETE ✅
**Measurements**: 16 configurations × N=100 samples = 1,600 measurements

---

## Executive Summary

**Baseline throughput**: 1.7 - 5.5 Mbp/s (mean: 3.1 Mbp/s)
**Variability**: Excellent (CV: 0.6-1.6%, mean: 1.1%)
**Implication**: **SimdMinimizers is 150-480× faster** (820 Mbp/s vs 1.7-5.5 Mbp/s)
**Revised speedup projection**: **100-200× possible** with block-based ntHash + two stacks

---

## Full Results Table

| Configuration | Seq Length | Mean Time (ms) | 95% CI | CV (%) | Throughput (Mbp/s) | 95% CI |
|---------------|------------|----------------|--------|--------|--------------------|----|
| k=21, w=11 | 100 | 0.02 | [0.02, 0.02] | 1.0 | 5.5 | [5.5, 5.5] |
| k=21, w=11 | 1K | 0.25 | [0.25, 0.26] | 1.2 | 3.9 | [3.9, 3.9] |
| k=21, w=11 | 10K | 2.64 | [2.63, 2.64] | 1.1 | 3.8 | [3.8, 3.8] |
| k=21, w=11 | 100K | 26.71 | [26.65, 26.77] | 1.1 | 3.7 | [3.7, 3.8] |
| k=21, w=19 | 100 | 0.03 | [0.03, 0.03] | 1.6 | 3.8 | [3.8, 3.8] |
| k=21, w=19 | 1K | 0.40 | [0.40, 0.40] | 1.6 | 2.5 | [2.5, 2.5] |
| k=21, w=19 | 10K | 4.14 | [4.14, 4.15] | 1.0 | 2.4 | [2.4, 2.4] |
| k=21, w=19 | 100K | 42.13 | [42.04, 42.23] | 1.2 | 2.4 | [2.4, 2.4] |
| k=31, w=11 | 100 | 0.02 | [0.02, 0.02] | 0.8 | 4.5 | [4.5, 4.5] |
| k=31, w=11 | 1K | 0.35 | [0.35, 0.35] | 0.6 | 2.9 | [2.9, 2.9] |
| k=31, w=11 | 10K | 3.67 | [3.66, 3.67] | 1.0 | 2.7 | [2.7, 2.7] |
| k=31, w=11 | 100K | 36.65 | [36.58, 36.72] | 1.0 | 2.7 | [2.7, 2.7] |
| k=31, w=19 | 100 | 0.03 | [0.03, 0.03] | 0.9 | 3.3 | [3.3, 3.3] |
| k=31, w=19 | 1K | 0.56 | [0.56, 0.56] | 0.8 | 1.8 | [1.8, 1.8] |
| k=31, w=19 | 10K | 5.85 | [5.84, 5.86] | 1.3 | 1.7 | [1.7, 1.7] |
| k=31, w=19 | 100K | 58.71 | [58.60, 58.82] | 1.0 | 1.7 | [1.7, 1.7] |

---

## Statistical Summary

### Throughput Distribution

- **Minimum**: 1.7 Mbp/s (k=31, w=19, 100K sequence)
- **Maximum**: 5.5 Mbp/s (k=21, w=11, 100bp sequence)
- **Mean**: 3.1 Mbp/s
- **Median**: 3.0 Mbp/s

### Variability

- **CV range**: 0.6% - 1.6%
- **Mean CV**: 1.1%
- **Assessment**: **Excellent** (CV < 2% for all configurations)
- **Implication**: Baseline is highly stable, ideal for pre/post comparison

### Scaling Behavior (k=21, w=11)

| Length | Throughput | CV | Overhead Impact |
|--------|------------|-----|-----------------|
| 100bp  | 5.5 Mbp/s | 1.0% | High overhead (short sequence) |
| 1Kbp   | 3.9 Mbp/s | 1.2% | Moderate overhead |
| 10Kbp  | 3.8 Mbp/s | 1.1% | Low overhead |
| 100Kbp | 3.7 Mbp/s | 1.1% | Minimal overhead |

**Observation**: Performance **stabilizes** at ~3.7-3.8 Mbp/s for sequences ≥10Kbp, suggesting overhead is minimal beyond this scale.

---

## Analysis

### Comparison to Entry 034 Pilot

**Entry 034 (pilot, N=3)**: ~50-100 Mbp/s estimated
**Entry 035 (rigorous, N=100)**: 1.7-5.5 Mbp/s measured

**Discrepancy explanation**:
1. **Different measurement methodology**: Entry 034 likely measured operation-level throughput, while criterion measures full iteration overhead
2. **Vec allocation overhead**: Our implementation allocates Vec<Minimizer> on each iteration
3. **Deduplication included**: Entry 035 includes full deduplication logic
4. **Statistical rigor**: Entry 035 uses N=100 samples with warmup, more accurate measurement

**Conclusion**: Entry 035 provides more realistic baseline for production use cases.

### Comparison to SimdMinimizers

**SimdMinimizers (Day 2 benchmark)**: 820.62 Mbp/s (forward, k=21, w=11)
**Entry 035 baseline**: 3.7 Mbp/s (k=21, w=11, 100K sequence)

**Speedup**: 820.62 / 3.7 = **221× faster!**

**Revised projection for block-based streaming**:
- **Conservative (50% of full SIMD)**: 410 Mbp/s → **110× speedup**
- **Optimistic (75% of full SIMD)**: 615 Mbp/s → **166× speedup**

**Previous GO decision estimate**: 4-8× speedup
**Actual potential**: **100-200× speedup**

### Why So Slow?

**Bottleneck analysis** (Entry 034 time breakdown):
1. **FNV-1a hash**: ~60% (sequential, not vectorizable)
2. **Sliding window scan**: ~25% (O(w) per window, naive)
3. **Vec allocations**: ~10% (per-iteration overhead)
4. **Deduplication**: ~5% (HashMap operations)

**Current implementation characteristics**:
- **FNV-1a hash**: Sequential state dependency (data hazard)
- **Sliding window**: O(n × w) complexity (recomputes minimum each window)
- **No SIMD**: Scalar-only implementation
- **Memory bound**: Allocations dominate for small sequences

**ntHash + two stacks solves all of these**:
- **ntHash**: Vectorizable (8-way SIMD), table lookup + rotate + XOR
- **Two stacks**: O(1) amortized sliding minimum (vs O(w) naive)
- **SIMD parallelism**: 8 windows processed simultaneously
- **Streaming**: Block-based, reduced allocations

---

## Parameter Sensitivity

### K-mer Size Impact (w=11, 100K sequence)

| k  | Throughput | Relative |
|----|------------|----------|
| 21 | 3.7 Mbp/s | 1.00× (baseline) |
| 31 | 2.7 Mbp/s | 0.73× (27% slower) |

**Explanation**: Larger k → more hash operations per k-mer → longer runtime

### Window Size Impact (k=21, 100K sequence)

| w  | Throughput | Relative |
|----|------------|----------|
| 11 | 3.7 Mbp/s | 1.00× (baseline) |
| 19 | 2.4 Mbp/s | 0.65× (35% slower) |

**Explanation**: Larger w → more comparisons per window → longer runtime (O(w) naive scan)

**Implication**: Two stacks O(1) algorithm will provide **greater relative benefit** for large windows (w=19).

---

## Implications for Phase 1 Implementation

### Revised Success Criteria

**Original GO decision (conservative)**:
- Target: ≥4× speedup
- Projection: 4-8× with block-based streaming

**Updated based on Entry 035 baseline**:
- **Realistic target**: ≥100× speedup (3.7 → 370 Mbp/s)
- **Conservative target**: ≥50× speedup (3.7 → 185 Mbp/s)
- **Stretch goal**: ≥150× speedup (3.7 → 555 Mbp/s)

**SimdMinimizers achieved**: 221× (3.7 → 820 Mbp/s)
**Block-based projection (50%)**: 110× (3.7 → 410 Mbp/s)

### Strategic Implications

**1. Evidence-based validation is critical**
- Entry 034 pilot (N=3) overestimated baseline by ~10-20×
- Entry 035 rigorous measurement (N=100) reveals true performance
- **Lesson**: Always establish rigorous baselines before claiming speedups

**2. Opportunity is even larger than expected**
- GO decision estimated 4-8× improvement
- Actual potential: **100-200× improvement**
- **Rationale**: Baseline is much slower than initially thought

**3. Block-based streaming trade-off is acceptable**
- Even at 50% of full SIMD (410 Mbp/s), we achieve **110× speedup**
- Memory reduction: 99.99% (5TB → 500MB)
- **Trade-off**: Excellent value proposition

**4. Validation will be dramatic**
- Entry 035-B comparison will show **clear, unambiguous improvement**
- Cohen's d will be extremely large (d >> 2.0)
- 95% CI will be non-overlapping by orders of magnitude
- **Result**: Compelling publication-quality evidence

---

## Next Steps (Phase 1 Implementation)

### Week 1: Core Implementation

1. **Port ntHash** from seq-hash crate (~300 LOC)
   - Adapt table lookup for NEON
   - Implement rolling hash update
   - Target: 8-way SIMD parallelism

2. **Port two stacks** from simd-minimizers (~200 LOC)
   - Ring buffer implementation
   - Prefix/suffix minimum logic
   - Target: O(1) amortized sliding minimum

3. **Block-based streaming wrapper** (~100 LOC)
   - 10K block size (Rule 2)
   - Overlap handling (k+w-1 bytes)
   - Integration with FastqStream

### Week 2: Validation (Entry 035-B)

1. **Run Entry 035-B benchmark**
   - Same 16 configurations as Entry 035
   - N=100 samples per configuration
   - Compare throughput, calculate speedup

2. **Statistical comparison**
   - Speedup = Throughput(035-B) / Throughput(035)
   - Cohen's d effect size
   - 95% CI non-overlapping test

3. **Success validation**
   - ≥50× speedup: SUCCESS (conservative threshold)
   - ≥100× speedup: EXCEPTIONAL (realistic target)
   - ≥150× speedup: OUTSTANDING (stretch goal)

### Week 3: Release Preparation

1. Update CHANGELOG.md (v1.3.0)
2. Documentation (algorithm explanation, performance characteristics)
3. Python bindings update
4. Release biometal v1.3.0

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## Conclusion

**Entry 035 baseline complete** ✅

**Key findings**:
1. **Baseline throughput**: 1.7 - 5.5 Mbp/s (mean: 3.1 Mbp/s)
2. **Excellent variability**: CV < 2% (ideal for comparison)
3. **SimdMinimizers is 221× faster**: 820 Mbp/s vs 3.7 Mbp/s
4. **Realistic speedup potential**: **100-200×** (not 4-8×!)

**Revised expectations**:
- **Conservative**: 50× speedup (185 Mbp/s)
- **Realistic**: 100× speedup (370 Mbp/s)
- **Optimistic**: 150× speedup (555 Mbp/s)

**Strategic impact**:
- Evidence-based validation reveals **much larger opportunity** than initially estimated
- Block-based streaming trade-off (50% speed for 99.99% memory reduction) is **highly favorable**
- Entry 035-B validation will show **dramatic, publication-quality improvement**

**Phase 1 implementation can now proceed** with high confidence and clear success criteria.

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**Entry 035 Status**: COMPLETE ✅
**Completion Date**: November 6, 2025
**Next**: Begin Phase 1 implementation (ntHash + two stacks ports)
