GPU Optimization Plan for NeuroCHIMERA

Current Issues (Only ~10% GPU Utilization)

Identified Bottlenecks:

1. Framebuffer Recreation (Line 804-809 in engine.py)

   • Recreating framebuffer every iteration
   • Massive overhead
   • Fix: Pre-allocate framebuffers, use ping-pong efficiently

2. CPU-GPU Data Transfers (Lines 852, 860, 868)

   • Reading from GPU to CPU for convergence check
   • Downloading entire frame unnecessarily
   • Fix: Keep all operations on GPU, use GPU-based convergence

3. Single Render Pass

   • Only using one render target at a time
   • Not leveraging multiple render targets simultaneously
   • Fix: Use multiple render targets in parallel

4. No Compute Shaders

   • Using fragment shaders only
   • Not using compute shaders for better parallelism
   • Fix: Implement compute shader version

5. Sequential Operations

   • Evolution, learning, metrics run sequentially
   • Fix: Pipeline operations, use async execution

6. Texture Memory Management

   • Not using texture arrays efficiently
   • Fix: Use texture arrays, optimize memory layout

Optimization Strategies

1. Eliminate CPU-GPU Transfers

• Keep convergence check on GPU
• Use GPU-based reduction operations
• Only download when absolutely necessary

2. Pre-allocate Resources

• Create all framebuffers at initialization
• Reuse textures efficiently
• Minimize dynamic allocation

3. Parallel Render Targets

• Use multiple render targets simultaneously
• Process different operations in parallel
• Leverage GPU's parallel processing

4. Compute Shaders (OpenGL 4.3+)

• Use compute shaders for better parallelism
• Process multiple work groups simultaneously
• Better memory access patterns

5. Batch Operations

• Combine multiple operations in single pass
• Use texture arrays for batch processing
• Minimize state changes

6. Memory Optimization

• Use texture compression where possible
• Optimize texture formats
• Better memory access patterns

Expected Improvements

• 10x speedup from eliminating bottlenecks
• Better GPU utilization (from 10% to 80%+)
• Larger networks possible with same memory
• Lower latency from reduced CPU-GPU transfers

Implementation Priority

1. High Priority:

   • Eliminate framebuffer recreation
   • Remove CPU-GPU transfers for convergence
   • Pre-allocate all resources

2. Medium Priority:

   • Implement compute shader version
   • Parallel render targets
   • Batch operations

3. Low Priority:

   • Memory optimization
   • Texture compression
   • Advanced pipelining