How to Profile and Optimize CUDA Tile Kernels with NVIDIA Nsight Compute

NVIDIA Nsight Compute can now profile CUDA Tile kernels directly, giving developers detailed visibility into how tile-based code runs on the GPU. CUDA Tile lets you write CUDA at a higher level, working with multidimensional arrays called Tiles instead of managing threads, blocks, and grids manually. The compiler handles the mapping to hardware, but that raises a question: when the compiler decides how your kernel runs, how do you know if it's fast, or where it's slowing down? Nsight Compute closes that visibility gap. It connects GPU performance data back to your CUDA Tile kernel, identifies what is limiting performance, and points you toward concrete optimizations. That means you can take advantage of higher-level GPU programming without giving up visibility into occupancy, memory access behavior, generated SASS, or source-level performance. Watch to see how these insights translate into measurable results: two targeted changes increase occupancy and reduce kernel duration by 81%. What you will learn: - Connect Performance Metrics to Source: See how performance data and stalls map directly to individual lines of Python or C++ source and compiled SASS.9 - Actionable Optimization Guidance: Learn how to use compiler hints (like occupancy hints) and adjust tile block dimensions to maximize hardware utilization.10 - Validate Enhancements: Use the comparison tools to trace performance gains, monitor uncoalesced memory accesses, and confidently optimize existing kernels.11 Download the latest version of NVIDIA Nsight Compute: https://developer.nvidia.com/nsight-compute