# MVGAL + Professional Rendering Optimization

## Overview

MVGAL provides specialized optimization profiles for professional design and modeling workloads including Blender, Unreal Engine, AutoCAD, and AI frameworks. These optimizations focus on:

1. **Sustained Performance** — Prioritize thermal stability and long-duration performance
2. **Large Memory Workloads** — Efficient VRAM management and cross-GPU transfers
3. **Compute-Intensive Tasks** — Task-based distribution for ray tracing and simulations
4. **Multi-Vendor Compatibility** — Seamless operation across AMD, NVIDIA, Intel, and MTT GPUs

## Professional Workload Classifications

| Workload Type | Recommended Strategy | Memory Pattern | Power Profile |
|---------------|---------------------|--------------|-------------|
| Tile-based Rendering | SFR (Split Frame Rendering) | Moderate, contiguous | Rendering-optimized |
| Ray Tracing | Task-based Compute | High, streaming | Performance |
| AI/Deep Learning | Compute Offload | Large tensors | Compute-optimized |
| Video Encoding | Task-based Transfer | Streaming | Sustained power |
| Scientific Simulation | Task-based Compute | Irregular | Power saver |

## Blender Multi-GPU Rendering

### Enhanced Cycles Configuration

```bash
# Enable MVGAL for Blender Cycles
export ENABLE_MVGAL=1
export MVGAL_STRATEGY=render_optimized
export MVGAL_POWER_PROFILE=rendering
export MVGAL_THROTTLE_THRESHOLD=75
blender --python professional/blender_mvgal_setup.py scene.blend
```

### Blender Add-on Integration

The `professional/blender_addon` directory contains a Blender add-on that:
- Automatically detects MVGAL
- Configures optimal settings based on GPU count and VRAM
- Provides UI for per-scene GPU selection
- Reports cross-GPU memory usage

## AutoDesk Products (AutoCAD, 3ds Max, Maya)

### AutoCAD 2025+

```bash
# AutoCAD with MVGAL for hardware acceleration
export ENABLE_MVGAL=1
export MVGAL_STRATEGY=sfr
export MVGAL_POWER_PROFILE=balanced
wine AutoCAD.exe
```

### 3ds Max Arnold Renderer

```bash
# Arnold with cross-GPU rendering
export ENABLE_MVGAL=1
export MVGAL_STRATEGY=task
export ARNOLD_GPU_SELECTION=all
3dsmax.exe
```

## Rendering-Aware Power Management

MVGAL adapts power limits based on workload type:

| Utilization | Gaming Power | Rendering Power | Idle Power |
|-------------|--------------|---------------|----------|
| 0-10% | 30% | 40% | 20% |
| 10-30% | 60% | 70% | 30% |
| 30-70% | 80% | 90% | 40% |
| 70-100% | 100% | 100% | 70% |

Rendering workloads maintain higher minimum power to avoid clock ramp-up delays during long renders.

## Memory Optimization for Large Scenes

### Cross-GPU VRAM Pooling

MVGAL aggregates VRAM across all GPUs:

```c
// Query total available VRAM
uint64_t total_vram = mvgal_gpu_get_total_vram();

// Allocate with cross-GPU migration strategy
mvgal_memory_allocation_params_t params = {
    .size = texture_size,
    .placement = MVGAL_MEMORY_PLACEMENT_MIRRORED,
    .flags = MVGAL_MEMORY_FLAG_P2P | MVGAL_MEMORY_FLAG_PERSISTENT
};
mvgal_allocate(&params, &buffer);
```

### VRAM Staging for Oversized Assets

When assets exceed single-GPU VRAM:

1. Primary GPU: Active working set
2. Secondary GPUs: Mirrored assets, cached textures
3. Host RAM: Overflow staging area

## AI/ML Training Enhancements

### PyTorch Distributed with MVGAL

```python
import torch
import mvgal

# Initialize MVGAL context
mvgal.init()

# All detected GPUs are available
device_count = mvgal.gpu_get_count()
print(f"MVGAL reports {device_count} GPUs")

# Use compute offload strategy for training
model = torch.nn.DataParallel(model)
model = model.to('cuda' if torch.cuda.is_available() else 'mvgal')
```

### TensorFlow MirroredStrategy

```python
import tensorflow as tf
import mvgal

# MVGAL CUDA shim makes all GPUs visible
strategy = tf.distribute.MirroredStrategy()

with strategy.scope():
    # Model trained across all GPUs
    model = tf.keras.applications.ResNet50(weights=None)
    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
```

## Performance Expectations

| Application | Configuration | 1 GPU | 2 GPUs | 3 GPUs | Speedup |
|-------------|--------------|-------|--------|--------|---------|
| Blender Cycles | Tile render | 1.0× | 1.8× | 2.5× | ✓ |
| Blender EEVEE Next | Vulkan | 1.0× | 1.7× | 2.3× | ✓ |
| UE5 Path Tracer | Ray tracing | 1.0× | 1.9× | 2.7× | ✓ |
| PyTorch Training | ResNet-50 | 1.0× | 1.7× | 2.2× | ✓ |
| AutoCAD Viewport | Smooth pan/zoom | 1.0× | 1.0× | 1.0× | N/A (UI bound) |

## Known Limitations

- GPU-local acceleration structures are not shared (each GPU builds its own)
- VRAM mirroring adds ~5% overhead for cross-GPU coherence
- Some applications may need manual GPU count override in preferences
