00180d0b1c
- Implementación de detector híbrido (Whisper + Chat + Audio + VLM) - Sistema de detección de gameplay real vs hablando - Scene detection con FFmpeg - Soporte para RTX 3050 y RX 6800 XT - Guía completa en 6800xt.md para próxima IA - Scripts de filtrado visual y análisis de contexto - Pipeline automatizado de generación de videos
110 lines
3.4 KiB
Markdown
110 lines
3.4 KiB
Markdown
# GPU/CPU Monitoring Report - Twitch Highlight Detector
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## System Information
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- **GPU**: NVIDIA GeForce RTX 3050 (8192 MiB)
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- **Driver**: 580.126.09
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- **Device**: cuda (CUDA requested and available)
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## Execution Summary
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- **Total Runtime**: ~10.5 seconds
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- **Process Completed**: Successfully
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- **Highlights Found**: 1 (4819s - 4833s, duration: 14s)
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## GPU Utilization Analysis
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### Peak GPU Usage
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- **Single Peak**: 100% GPU SM utilization (1 second only)
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- **Location**: During RMS calculation phase
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- **Memory Usage**: 0-4 MiB (negligible)
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### Average GPU Utilization
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- **Overall Average**: 3.23%
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- **During Processing**: ~4% (excluding idle periods)
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- **Memory Utilization**: ~1% (4 MiB / 8192 MiB)
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### Timeline Breakdown
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1. **Chat Analysis**: < 0.1s (CPU bound)
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2. **FFmpeg Audio Extraction**: 8.5s (CPU bound - FFmpeg threads)
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3. **Audio Decode**: 9.1s (CPU bound - soundfile library)
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4. **CPU->GPU Transfer**: 1.08s (PCIe transfer)
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5. **GPU Processing**:
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- Window creation: 0.00s (GPU)
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- RMS calculation: 0.12s (GPU - **100% spike**)
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- Peak detection: 0.00s (GPU)
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## CPU vs GPU Usage Breakdown
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### CPU-Bound Operations (90%+ of runtime)
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1. **FFmpeg audio extraction** (8.5s)
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- Process: ffmpeg
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- Type: Video/audio decoding
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- GPU usage: 0%
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2. **Soundfile audio decoding** (9.1s overlap)
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- Process: Python soundfile
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- Type: WAV decoding
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- GPU usage: 0%
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3. **Chat JSON parsing** (< 0.5s)
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- Process: Python json module
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- Type: File I/O + parsing
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- GPU usage: 0%
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### GPU-Bound Operations (< 1% of runtime)
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1. **Audio tensor operations** (0.12s total)
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- Process: PyTorch CUDA kernels
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- Type: RMS calculation, window creation
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- GPU usage: 100% (brief spike)
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- Memory: Minimal tensor storage
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2. **GPU Memory allocation**
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- Audio tensor: ~1.2 GB (308M samples × 4 bytes)
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- Chat tensor: < 1 MB
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- Calculation buffers: < 100 MB
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## Conclusion
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### **FAIL: GPU not utilized**
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**Reason**: Despite the code successfully using PyTorch CUDA for tensor operations, GPU utilization is minimal because:
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1. **Bottleneck is CPU-bound operations**:
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- FFmpeg audio extraction (8.5s) - 0% GPU
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- Soundfile WAV decoding (9.1s) - 0% GPU
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- These operations cannot use GPU without CUDA-accelerated libraries
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2. **GPU processing is trivial**:
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- Only 0.12s of actual CUDA kernel execution
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- Operations are too simple to saturate GPU
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- Memory bandwidth underutilized
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3. **Architecture mismatch**:
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- Audio processing on GPU is efficient for large batches
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- Single-file processing doesn't provide enough parallelism
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- RTX 3050 designed for larger workloads
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## Recommendations
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### To actually utilize GPU:
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1. **Use GPU-accelerated audio decoding**:
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- Replace FFmpeg with NVIDIA NVDEC
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- Use torchaudio with CUDA backend
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- Implement custom CUDA audio kernels
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2. **Batch processing**:
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- Process multiple videos simultaneously
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- Accumulate audio batches for GPU
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- Increase tensor operation complexity
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3. **Alternative: Accept CPU-bound nature**:
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- Current implementation is already optimal for single file
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- GPU overhead may exceed benefits for small workloads
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- Consider multi-threaded CPU processing instead
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## Metrics Summary
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- **GPU utilization**: 3.23% average (FAIL - below 10% threshold)
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- **CPU usage**: High during FFmpeg/soundfile phases
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- **Memory usage**: 4 MiB GPU / 347 MB system
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- **Process efficiency**: 1 highlight / 10.5 seconds
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