resonance-engine/docs/gtx1050_deployment_plan.md

GTX 1050 Deployment & Testing Plan

🎯 Target Hardware:

• OS: Ubuntu 24.04 LTS
• CPU: Intel i7-7700HQ @ 2.80GHz (4 cores, 8 threads)
• RAM: 32GB
• GPU: NVIDIA GTX 1050 4GB (nvidia-driver-470)
• Disk: 937GB NVMe (~87GB used, ~803GB free / 10%)

📦 What We Know Works:

1. 256×256 grid - Compiled and tested on Windows/RTX 4090
2. Guardian formation - 13 guardians with RHO_THRESH=1.00022
3. Probe sequence - A, B, C, D stress tests defined
4. Power scaling - ~37W on RTX 4090 (expect ~40-60W on GTX 1050)

🚀 Deployment Steps:

Phase 1: Environment Setup (Ubuntu)

# 1. Verify CUDA installation
nvidia-smi
nvcc --version

# 2. Install required libraries
sudo apt-get update
sudo apt-get install -y build-essential libnvml-dev

# 3. Verify GPU architecture support
# GTX 1050 = Pascal = sm_61

Phase 2: Transfer & Compile

# 1. Copy source files to Ubuntu
scp probe_256.cu user@gtx1050:~/fractal/
scp fractal_habit_256_full.cu user@gtx1050:~/fractal/

# 2. Compile on target hardware
cd ~/fractal
nvcc -O3 -arch=sm_61 -o probe_256_gtx1050 probe_256.cu -lnvml
nvcc -O3 -arch=sm_61 -o fractal_habit_256 fractal_habit_256_full.cu -lnvml -lcufft

Phase 3: Initial Test

# 1. Test basic execution
./fractal_habit_256

# 2. Check power usage
sudo nvidia-smi -pl 60  # Set power limit to 60W
./probe_256_gtx1050

# 3. Monitor with nvidia-smi
watch -n 1 nvidia-smi

🔬 Testing Protocol:

Test 1: Basic Functionality

• Run fractal_habit_256 for 100k steps
• Verify: Guardian formation (13 guardians)
• Monitor: Power draw, temperature, stability

Test 2: Power Limiting

# Test different power limits
sudo nvidia-smi -pl 40  # Minimum sustainable
sudo nvidia-smi -pl 50  # Balanced
sudo nvidia-smi -pl 60  # Performance
sudo nvidia-smi -pl 75  # Max (default)

Test 3: Full Probe Sequence

• Run probe_256_gtx1050 with monitoring
• Focus on crash at cycle ~1112 (Probe C - VRM Silence)
• Collect complete data for all probe phases

Test 4: Long-term Stability

• Run for extended period (10,000+ cycles)
• Monitor for memory leaks, GPU errors
• Check thermal throttling

📊 Data Collection:

Essential Metrics:

1. Power: Watts (nvidia-smi)
2. Temperature: GPU core temp
3. Performance: Cycles per second
4. Stability: Guardian count, omega values
5. Memory: GPU memory usage

Monitoring Script:

#!/bin/bash
# monitor_gtx1050.sh
while true; do
    nvidia-smi --query-gpu=power.draw,temperature.gpu,utilization.gpu,memory.used --format=csv
    sleep 1
done

⚠️ Potential Issues & Solutions:

Issue 1: CUDA Compatibility

• Check: GTX 1050 = sm_61 architecture
• Fix: Compile with -arch=sm_61

Issue 2: Power Limiting

• Check: GTX 1050 power limits (40-75W)
• Fix: Use nvidia-smi -pl to set limits

Issue 3: Memory Constraints

• Check: 4GB VRAM usage
• Fix: Monitor with nvidia-smi --query-gpu=memory.used

Issue 4: Thermal Throttling

• Check: Temperature > 80°C
• Fix: Improve cooling, reduce power limit

🎯 Success Criteria:

Minimum Viable Product:

1. ✅ 256×256 grid runs on GTX 1050
2. ✅ 13 guardians form and persist
3. ✅ Power draw < 60W sustained
4. ✅ Temperature < 80°C
5. ✅ No crashes in first 1000 cycles

Extended Goals:

1. ✅ Complete probe sequence (A-D) without crash
2. ✅ Stable operation for 10,000+ cycles
3. ✅ Power efficiency optimization
4. ✅ Documentation of performance characteristics

📋 Immediate Action Items:

1. Transfer files to Ubuntu system
2. Compile with correct architecture (sm_61)
3. Set power limit to 60W for testing
4. Run basic test - verify guardian formation
5. Execute full probe sequence - monitor for crash at cycle ~1112

🕒 Time Estimate:

• Setup: 30 minutes
• Compilation: 10 minutes
• Basic test: 15 minutes
• Full probe sequence: 30-60 minutes
• Total: 1.5-2 hours

🎪 Next Steps After Successful Deployment:

1. Performance optimization - tune parameters for GTX 1050
2. Extended testing - 24-hour stability run
3. Documentation - create GTX 1050 performance profile
4. Scaling tests - try 384×384 if 256×256 is stable
5. Application development - build on stable foundation

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Key Insight: The forensic audit showed the system works correctly but has power scaling inefficiencies. On GTX 1050, we're targeting the actual hardware constraints (40-60W), so these "inefficiencies" may actually be acceptable or even optimal for this hardware class.