cuda

Profile CUDA kernel performance bottlenecks by applying Nsight Systems and Nsight Compute to capture kernel timings and metrics. This workflow guides you through reproducing the issue, profiling with nsys and ncu, and consulting tooling references for targeted optimizations.

Debug a slow CUDA kernel using a standardized workflow that applies cuda-gdb and compute-sanitizer to diagnose issues. It helps developers reproduce problems, inspect kernel execution, and consult documentation to guide reproducible profiling and targeted optimizations.

Use Nsight Systems for system-wide profiling to observe kernel timings and Nsight Compute for detailed kernel-level performance analysis. This workflow integrates both tools to provide a consolidated overview for diagnosing GPU kernels across a typical development cycle.

Inspect compiled CUDA kernel binaries and PTX ISA using cuobjdump. This workflow includes binary inspection as part of a consolidated overview of kernel development, helping you understand low-level execution and diagnose performance issues.

Memory error detection is supported through the compute-sanitizer tool. The workflow integrates compute-sanitizer alongside cuda-gdb and Nsight profilers to help developers identify, analyze, and resolve both performance bottlenecks and memory errors in GPU kernels.

Standardizing your CUDA optimization workflow ensures reproducible profiling and targeted optimizations across scattered tooling ecosystems. It provides a structured guide to debug, profile, and optimize GPU kernels consistently using standard tools like nsys, ncu, and cuda-gdb.