GPU Optimization Analysis: Improving from 10% to 80%+ Utilization

Problem Identified

The current implementation shows:

• Continuous GPU usage: ~10%
• Spikes to 100% causing potential errors
• Wasted capacity that can be exploited with proper shader optimization

Root Causes

1. Work Group Size Too Small

• Current: 16×16 = 256 threads per work group
• Problem: Not enough parallelism to keep GPU busy
• Solution: Increase to 32×32 = 1024 threads per work group

2. Sequential Iteration Execution

• Current: Each iteration waits for previous to complete
• Problem: GPU sits idle between iterations
• Solution: Pipeline iterations, dispatch all work without waiting

3. Excessive Synchronization

• Current: ctx.finish() after each operation
• Problem: Forces GPU to wait, breaks parallelism
• Solution: Only synchronize when absolutely necessary

4. Memory Access Patterns

• Current: Random memory access in neighborhood loops
• Problem: Poor memory coalescing, cache misses
• Solution: Optimize access patterns for better coalescing

5. State Changes Between Iterations

• Current: Re-binding textures and uniforms each iteration
• Problem: Overhead from state changes
• Solution: Pre-bind once, reuse across iterations

Optimizations Applied

1. Increased Work Group Size

// Before: layout(local_size_x = 16, local_size_y = 16)
// After:  layout(local_size_x = 32, local_size_y = 32)

• Impact: 4x more threads per work group
• Benefit: Better GPU occupancy, more parallelism

2. Pipelined Iterations

# Before: Wait for each iteration
for i in range(iterations):
    dispatch()
    ctx.finish()  # Wait

# After: Pipeline all iterations
for i in range(iterations):
    dispatch()  # Don't wait
ctx.finish()  # Only at end

• Impact: GPU can work on multiple iterations simultaneously
• Benefit: Better utilization, reduced idle time

3. Pre-binding Resources

# Before: Re-bind each iteration
for i in range(iterations):
    bind_textures()
    set_uniforms()
    dispatch()

# After: Bind once, reuse
bind_textures()  # Once
set_uniforms()   # Once
for i in range(iterations):
    dispatch()

• Impact: Reduced state change overhead
• Benefit: Faster dispatch, less CPU-GPU communication

4. Optimized Memory Access

// Better memory coalescing by processing rows
for (int dy = -2; dy <= 2; dy++) {
    int y = coord.y + dy;
    if (y < 0 || y >= u_grid_size.y) continue;
    
    // Process row - threads access adjacent memory
    for (int dx = -2; dx <= 2; dx++) {
        // Coalesced access
    }
}

• Impact: Better memory bandwidth utilization
• Benefit: Reduced cache misses, faster memory access

Expected Improvements

GPU Utilization

• Before: ~10% continuous, 100% spikes
• After: 70-80% continuous, smoother load
• Target: 80%+ sustained utilization

Performance

• Before: Picos causan errores, bajo throughput
• After: Carga más uniforme, mayor throughput
• Expected: 5-10x improvement in sustained performance

Stability

• Before: Picos del 100% causan errores
• After: Carga más uniforme, menos errores
• Benefit: Más estable, mejor para producción

Additional Optimizations to Consider

1. Multiple Compute Shaders in Parallel

• Run evolution, learning, and metrics simultaneously
• Use different compute shader programs concurrently
• Potential: 2-3x additional improvement

2. Texture Arrays for Batch Processing

• Process multiple networks simultaneously
• Better memory utilization
• Potential: Scale to multiple networks

3. Async Execution

• Remove all ctx.finish() calls except when reading results
• Use GPU command queue more efficiently
• Potential: Additional 20-30% improvement

4. Work Group Size Tuning

• Test different work group sizes (16×16, 32×32, 64×64)
• Find optimal for RTX 3090 architecture
• Potential: Additional 10-20% improvement

Testing Recommendations

1. Monitor GPU Usage: Use nvidia-smi or GPU monitoring tools
2. Measure Sustained Load: Should be 70-80% continuous
3. Check for Errors: Reduced errors from load spikes
4. Benchmark Performance: Compare before/after throughput
5. Stress Test: Run extended tests to verify stability

Next Steps

1. ✅ Increase work group size (32×32)
2. ✅ Pipeline iterations
3. ✅ Pre-bind resources
4. ✅ Optimize memory access
5. ⏳ Test and measure GPU utilization
6. ⏳ Fine-tune work group sizes
7. ⏳ Implement parallel compute shaders
8. ⏳ Add async execution