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CUDA-based video applications performing tasks like encoding, decoding, filtering, or inference require detailed profiling and debugging to analyze performance, identify resource bottlenecks, and validate correctness. Efficient inspection of kernel behavior, memory access patterns, and execution timelines is essential, especially in real-time or high-throughput processing environments. Environment Setup Requirements Before initiating profiling or debugging tasks, the system must be correctly configured to allow for kernel inspection, runtime tracing, and memory error checking. The following components are mandatory: NVIDIA GPU with recent driver : Minimum driver version R525 is recommended to ensure compatibility with current CUDA tools like Nsight Compute and Nsight Systems. CUDA Toolkit installed : Required command-line tools include nvcc (for compilation), cuda-gdb (debugger), cuda-memcheck (memory validation), and headers for instrumentation. Debug-enabled build : The application must be compiled with debug symbols to enable kernel-level inspection. Use -G for enabling debugging and -lineinfo for embedding line mappings in the binary. Profiling tools installed : Install Nsight Compute for kernel profiling, Nsight Systems for system-wide tracing, and Visual Profiler (deprecated) for legacy workflows. Example build command for debug-enabled binary: nvcc -G -g -O0 -lineinfo -o video_app video_app.cu This command disables optimizations, includes line-level debug info, and enables kernel debugging support. Measuring Kernel-Level Performance with Nsight Compute Nsight Compute allows fine-grained inspection of GPU kernel execution. It provides metrics for thread occupancy, memory bandwidth, register usage, warp-level efficiency, and bottlenecks like instruction stalls or memory divergence. Launch GUI Profile: To start a session using the GUI profiler: ncu video_app This opens an interactive interface that captures and visualizes kernel metrics. CLI Example with metrics: For automated or script-based profiling: ncu --metrics sm__throughput.avg.pct_of_peak_sustained_elapsed,dram__throughput.avg.pct_of_peak_sustained_elapsed ./video_app This collects memory throughput metrics and shows how close each kernel runs to theoretical peak. Use Case: When analyzing a frame scaler using CUDA kernels (e.g., NV12 to RGB), measure global load/store efficiency and shared memory utilization to confirm if memory coalescing and tiling are effective. System-Wide Timeline Profiling with Nsight Systems Nsight Systems offers a timeline view of kernel launches, memory transfers, and CPU/GPU interactions. It is suitable for profiling frame processing pipelines, especially when working with: Suitable for profiling: CUDA Streams : Verify concurrency and overlap of compute and memory transfer. Pipelined stages : Observe how decode, inference, and render stages interact. Async memory copies : Check whether transfers overlap with kernel execution. Example Usage: nsys profile --trace=cuda,osrt,nvtx -o video_trace ./video_app Key Things to Observe: CPU stalls : Long gaps between kernel launches may indicate batching inefficiencies. Memory overlaps : Lack of concurrent memory transfers and compute points to poor stream utilization. Frame jitter : Inconsistent durations between frames often stem from poor synchronization or long memory copy delays. Debugging Kernel Logic with cuda-gdb cuda-gdb enables source-level stepping and inspection of variables in CUDA kernels. It is essential for detecting logic errors in pixel-level computations, index mismatches, or conditionals in filter kernels. Launch Debug Session: cuda-gdb ./video_app This opens an interactive GDB session for GPU debugging. Debug Workflow break kernel_function run info threads cuda thread apply all bt These commands set a breakpoint in the kernel, run the binary, list active threads, and backtrace each thread’s execution. Use the following commands for warp-specific debugging: cuda lwarps: Lists warps and their execution state. cuda thread [id]: Focuses on a specific thread for variable inspection. cuda warp [id]: Steps through a specific warp. Validating Memory Access with cuda-memcheck cuda-memcheck checks for out-of-bounds accesses, race conditions, and uninitialized memory usage. It is effective for validating frame buffer manipulations or custom video frame layouts. Run with Memory Checks: cuda-memcheck ./video_app Common Errors Out-of-bounds shared memory access : Caused by incorrect indexing or block dimensions. Uninitialized global memory reads : Often occur when buffers are declared but not written to before kernel launch. This tool is critical for validating custom memory layouts in frame buffers or convolutional filters. Using NVTX for Application Instrumentation Insert NVTX (NVIDIA Tools Extension) markers to label frames, stages, or kernels in timeline profilers. This improves interpretability of Nsight traces and enables correlation of performance metrics to specific video operations. Sample NVTX Markers: nvtxRangePushA('Frame Decode'); decode_frame<<<...>>>();nvtxRangePop(); nvtxRangePushA('Postprocess Filter'); apply_filter<<<...>>>(); nvtxRangePop(); You can also use color-coded annotations to label per-frame execution or isolate slow stages. Best Practices for CUDA Video Application Debugging Always build with debug symbols for development and profiling. Start with small, simple kernels before scaling up complexity. Use cuda-memcheck and enable error checking after kernel launches. Profile both at the kernel and system level to catch hidden bottlenecks. Annotate your code with NVTX markers for actionable timeline analysis. Regularly test and profile on production-like hardware and workloads.
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