Benchmark Analysis & Improvement Plan

Generated: 2026-05-12 09:15 UTC
Focus: Improve Pareto-optimal ratios from current baseline

📊 Current Benchmark Results

Summary

Benchmark	Pareto-Optimal	Total Configs	Pass Rate	Status
Conversational	2	9	22%	❌ CRITICAL
Vision	4	8	50%	⚠️ NEEDS WORK
Scientific	6	12	50%	⚠️ NEEDS WORK
Balanced	2	2	100%	✅ GOOD

Overall: 14/31 = 45% Pareto-optimal
Target: 80% Pareto-optimal

🔍 Detailed Analysis

1. Conversational Workload (22% - CRITICAL)

Pareto-Optimal Configurations (2/9):

conversational_fp16_seq1024 (Score: 0.7982)
- VRAM: 3000 MiB (75.5%)
- Throughput: 60 ops/sec
- Accuracy: 6.75
- Hamiltonian: 7.764
conversational_fp16_seq2048 (Score: 0.7982)
- VRAM: 3000 MiB (75.5%)
- Throughput: 30 ops/sec
- Accuracy: 6.75
- Hamiltonian: 7.764

Non-Pareto Configurations (7/9):

conversational_int4_seq1024/2048/4096 (3 configs)
- VRAM: 1200 MiB (30%)
- Throughput: 60/30/15 ops/sec
- Accuracy: 5.2 ⚠️ Lower accuracy
- Score: 0.629 (vs 0.798 for fp16)
conversational_int8_seq1024/2048/4096 (3 configs)
- VRAM: 1800 MiB (45%)
- Throughput: 60/30/15 ops/sec
- Accuracy: 6.0 ⚠️ Still lower than fp16
- Score: 0.706 (vs 0.798 for fp16)
conversational_fp16_seq4096 (1 config)
- VRAM: 3000 MiB
- Throughput: 15 ops/sec ⚠️ Too slow
- Accuracy: 6.75
- Score: 0.724 (lower due to throughput)

Root Causes:

Quantization accuracy loss - int4/int8 models have lower accuracy (5.2-6.0 vs 6.75)
Long sequence penalty - seq4096 has poor throughput (15 ops/sec)
Lack of middle-ground configs - No fp16_seq512 or int8_seq1536 tested

2. Vision Workload (50% - NEEDS WORK)

Pareto-Optimal Configurations (4/8):

vision_b8_i640 - Best throughput, moderate VRAM
vision_b4_i640 - Balanced
vision_b2_i640 - Lower VRAM, high accuracy
vision_b1_i640 - Lowest VRAM, highest accuracy

Non-Pareto Configurations (4/8):

vision_b*_i320 (4 configs) - All lower resolution variants
- Lower accuracy due to smaller input size
- Not competitive with 640x640 variants

Root Causes:

Resolution matters - All 640x640 configs are Pareto-optimal
320x320 not competitive - Lower resolution hurts accuracy too much
Need intermediate resolutions - Test 480x480, 512x512

3. Scientific Workload (50% - NEEDS WORK)

Pareto-Optimal Configurations (6/12):

Good mix of qubit counts (12, 16, 20) and sequence lengths
All Pareto configs have low VRAM (180-220 MiB)
Balance between throughput and accuracy

Non-Pareto Configurations (6/12):

Configurations with poor throughput/accuracy trade-offs
Too many qubits with long sequences (high latency)
Too few qubits (lower accuracy potential)

Root Causes:

Over-parameterization - Some configs use more resources than needed
Under-parameterization - Others don't meet accuracy requirements
Need better sampling - Explore q14, q18 qubit counts

🎯 Improvement Strategies

Strategy 1: Improve Conversational (22% → 67% target)

Add 4 new configurations:

conversational_fp16_seq512

Quantization: FP16 (no loss)
Sequence: 512 (shorter, faster)
Expected: Higher throughput (120 ops/sec)
VRAM: ~2400 MiB
Reason: Gap between seq1024 and seq2048

conversational_int8_seq1536

Quantization: INT8 (acceptable loss)
Sequence: 1536 (middle ground)
Expected: Good accuracy (6.2-6.4), moderate throughput
VRAM: ~1950 MiB
Reason: Better int8 accuracy with optimal sequence length

conversational_fp16_seq768

Quantization: FP16
Sequence: 768
Expected: High throughput (80 ops/sec), full accuracy
VRAM: ~2700 MiB
Reason: Fill gap between seq512 and seq1024

conversational_int8_seq768

Quantization: INT8
Sequence: 768
Expected: Better throughput (80 ops/sec), acceptable accuracy (6.0-6.2)
VRAM: ~1650 MiB
Reason: INT8 sweet spot - shorter sequences improve accuracy

Expected Result: 6/13 = 46% (still below target, but 2x improvement)

Additional Improvements:

Tune quantization calibration for int4/int8
Use Qwen2:1.5b instead of Qwen-1.5 (better accuracy)
Test dynamic quantization (loads fp16, quantizes at runtime)

Strategy 2: Improve Vision (50% → 75% target)

Add 2 new configurations:

vision_b*_i512 (4 configs: b1, b2, b4, b8)

Resolution: 512x512 (intermediate)
Expected: Better accuracy than 320, lower VRAM than 640
Reason: Fill resolution gap

vision_b*_i480 (4 configs)

Resolution: 480x480
Expected: Good balance
Reason: Common mobile resolution

Expected Result: 10/16 = 63% (if 6 new configs are Pareto-optimal)

Additional Improvements:

Use YOLOv8n quantized (reduces VRAM by 40%)
Test TensorRT optimization (improves throughput)
Add mAP50 metric for accuracy

Strategy 3: Improve Scientific (50% → 75% target)

Add 4 new configurations:

scientific_q14_s1024

Qubits: 14 (between 12 and 16)
Sequence: 1024
Expected: Good accuracy/throughput balance

scientific_q18_s1024

Qubits: 18 (between 16 and 20)
Sequence: 1024
Expected: Higher accuracy, acceptable throughput

scientific_q14_s2048
scientific_q18_s2048

Expected Result: 10/16 = 63%

Additional Improvements:

Use adaptive shot count (more shots for higher qubits)
Tune QAOA parameters (gamma, beta) per configuration
Test VQE algorithm (may be more efficient)

🚀 Implementation Plan

Phase 1: Quick Wins (2 hours)

Add conversational configs

cd ~/diamond-node/benchmarks
# Edit orthogonal_test.py to add 4 new configs

Add vision i512 resolution

# Add 4 new vision configs with 512x512 resolution

Re-run benchmarks

/home/diamondnode/venv312/bin/python benchmarks/orthogonal_test.py --workload all

Phase 2: Model Optimization (4 hours)

Switch to Qwen2:1.5b

ollama pull qwen2:1.5b  # Better accuracy than Qwen-1.5

Quantize YOLO

# Use YOLOv8n with INT8 quantization
from ultralytics import YOLO
model = YOLO('yolov8n.pt')
model.export(format='engine', int8=True)

Tune QUBO parameters

# In mycelial_qubo.py
lam_dist = 0.35  # was 0.4
lam_redund = -0.25  # was -0.2
lam_resource = -0.9  # was -0.8

Phase 3: Advanced (8 hours)

Implement dynamic quantization
Add TensorRT optimization
Implement waveform equilibrium scoring (currently returns 0)
Add LangSmith tracing for optimization metrics

📈 Expected Outcomes

Metric	Current	After Phase 1	After Phase 2	After Phase 3
Conversational	22%	46%	56%	67% ✅
Vision	50%	63%	69%	75% ✅
Scientific	50%	63%	69%	75% ✅
Overall	45%	57%	65%	72%

Final Target: 72% overall (close to 80% goal)

🔧 Quick Test Commands

Test Conversational with Qwen2:1.5b

# Pull better model
ollama pull qwen2:1.5b

# Test inference
ollama run qwen2:1.5b "What is 2+2?" --verbose

# Monitor VRAM
~/diamond-node/scripts/vram_check.sh

Test Vision with Quantized YOLO

from ultralytics import YOLO
import torch

# Load and quantize
model = YOLO('yolov8n.pt')
model = torch.quantization.quantize_dynamic(
    model, {torch.nn.Linear}, dtype=torch.qint8
)

# Test inference
results = model('test_image.jpg')

Test Scientific QUBO Tuning

cd ~/diamond-node

# Edit scripts/mycelial_qubo.py with new lambda values
nano scripts/mycelial_qubo.py

# Run test
/home/diamondnode/venv312/bin/python scripts/mycelial_qubo.py --shots 1024 --outer-rounds 5 --json

📊 Monitoring

Track Progress

# Run full benchmark suite
/home/diamondnode/venv312/bin/python benchmarks/orthogonal_test.py --workload all

# Check results
cat ~/diamond-node/benchmark_results/current-full/report_*.txt

# Compare before/after
diff \
  ~/diamond-node/benchmark_results/previous/report_conversational.txt \
  ~/diamond-node/benchmark_results/current-full/report_conversational.txt

VRAM Monitoring During Benchmarks

# Terminal 1: Run benchmarks
/home/diamondnode/venv312/bin/python benchmarks/orthogonal_test.py

# Terminal 2: Monitor VRAM
watch -n 1 '~/diamond-node/scripts/vram_check.sh'

# Terminal 3: Monitor GPU temperature
watch -n 1 'nvidia-smi --query-gpu=temperature.gpu,power.draw --format=csv,noheader'

✅ Success Criteria

Phase 1 Complete When:

4 new conversational configs added
4 new vision i512 configs added
Benchmark suite runs successfully
Conversational: 4-5 Pareto-optimal (44-56%)

Phase 2 Complete When:

Qwen2:1.5b installed and tested
YOLO quantization working
QUBO parameters tuned
Conversational: 5-6 Pareto-optimal (56-67%)

Phase 3 Complete When:

Waveform equilibrium implemented
TensorRT optimization working
LangSmith tracing active
Overall: 70%+ Pareto-optimal ✅

Status: Analysis complete, ready for implementation
Priority: Phase 1 (quick wins) recommended
ETA: Phase 1 = 2 hours, Phase 2 = 6 hours, Phase 3 = 14 hours total

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Benchmark Analysis & Improvement Plan

📊 Current Benchmark Results

Summary

🔍 Detailed Analysis

1. Conversational Workload (22% - CRITICAL)

2. Vision Workload (50% - NEEDS WORK)

3. Scientific Workload (50% - NEEDS WORK)

🎯 Improvement Strategies

Strategy 1: Improve Conversational (22% → 67% target)

Strategy 2: Improve Vision (50% → 75% target)

Strategy 3: Improve Scientific (50% → 75% target)

🚀 Implementation Plan

Phase 1: Quick Wins (2 hours)

Phase 2: Model Optimization (4 hours)

Phase 3: Advanced (8 hours)

📈 Expected Outcomes

🔧 Quick Test Commands

Test Conversational with Qwen2:1.5b

Test Vision with Quantized YOLO

Test Scientific QUBO Tuning

📊 Monitoring

Track Progress

VRAM Monitoring During Benchmarks

✅ Success Criteria

Phase 1 Complete When:

Phase 2 Complete When:

Phase 3 Complete When:

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Benchmark Analysis & Improvement Plan

📊 Current Benchmark Results

Summary

🔍 Detailed Analysis

1. Conversational Workload (22% - CRITICAL)

2. Vision Workload (50% - NEEDS WORK)

3. Scientific Workload (50% - NEEDS WORK)

🎯 Improvement Strategies

Strategy 1: Improve Conversational (22% → 67% target)

Strategy 2: Improve Vision (50% → 75% target)

Strategy 3: Improve Scientific (50% → 75% target)

🚀 Implementation Plan

Phase 1: Quick Wins (2 hours)

Phase 2: Model Optimization (4 hours)

Phase 3: Advanced (8 hours)

📈 Expected Outcomes

🔧 Quick Test Commands

Test Conversational with Qwen2:1.5b

Test Vision with Quantized YOLO

Test Scientific QUBO Tuning

📊 Monitoring

Track Progress

VRAM Monitoring During Benchmarks

✅ Success Criteria

Phase 1 Complete When:

Phase 2 Complete When:

Phase 3 Complete When: