CUDA Thread/Block Visualizer
This interactive tool helps you understand how different thread and block configurations affect GPU execution patterns.
How to Use
- Grid Size: Total number of threads in your kernel (adjust with top slider)
- Block Size: Number of threads per block (adjust with bottom slider)
- Green dots: Active threads executing work
- Gray dots: Inactive threads (padding within warps)
- Click “Configuration Analysis” to see detailed performance metrics
Key Insights
Block size should be a multiple of 32 (Warp Size) for optimal Memory Coalescing
- Warps execute 32 threads simultaneously
- Non-aligned block sizes waste GPU resources
- Memory transactions are most efficient with aligned access
Larger blocks reduce scheduling overhead but may limit GPU Occupancy
- More threads per block = fewer blocks
- Better for compute-intensive kernels with thread cooperation
- May hit resource limits (registers, shared memory)
Power-of-2 sizes often work best for Memory Access Patterns
- Align with cache line boundaries
- Simplify address calculations
- Reduce memory bank conflicts
Experiment with These Configurations
Try these scenarios to understand the trade-offs:
Perfect Configuration: Grid=256, Block=32
- 8 blocks × 32 threads = 8 warps total
- 100% efficiency, perfect alignment
Poor Configuration: Grid=100, Block=7
- 15 blocks with irregular warp usage
- Significant thread waste in each warp
Large Block: Grid=1024, Block=256
- 4 blocks × 256 threads = 32 warps total
- Good for thread cooperation, may limit occupancy
Related Concepts
This visualization helps bridge the gap between CUDA theory and practical kernel optimization. Experiment with different values to build intuition for real-world kernel design!