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