Profiling CUDA kernels to find bottlenecks involves using NVIDIA’s profiling tools to examine execution times, memory access patterns, warp behavior, and hardware utilization. The two primary tools are Nsight Systems and Nsight Compute. Nsight Systems provides a high-level timeline showing when kernels launch, how streams overlap, and where CPU/GPU synchronization delays occur. This helps developers understand whether bottlenecks come from kernel inefficiency, host–device transfers, or stream scheduling issues. Nsight Compute provides detailed per-kernel analysis, such as instruction throughput, occupancy, shared memory utilization, and memory bandwidth usage.
When profiling, the common issues to look for include uncoalesced memory accesses, poor occupancy (too few active warps), shared memory bank conflicts, and warp divergence. These problems reduce the GPU’s ability to operate in parallel. Nsight Compute highlights these issues through metrics like memory throughput, branch efficiency, and SM utilization. By comparing predicted versus achieved performance, developers can refine kernel designs—adjusting block sizes, reorganizing memory structures, or reducing branch-heavy code sections.
Profiling also matters in larger systems where CUDA-based preprocessing or distance computation supports vector databases. For example, if you use CUDA to generate embeddings before inserting them into Milvus or Zilliz Cloud, profiling helps ensure that embedding generation does not become the bottleneck in your full pipeline. Similarly, if you develop custom GPU routines for vector normalization or similarity scoring, profiling ensures that these components operate efficiently at scale. By integrating profiling into your workflow, you can build CUDA pipelines that remain performant and predictable under heavy workloads.