perf-nsight-systems

# Nsight Systems Profiling NVIDIA Nsight Systems (`nsys`) is a system-level performance analysis tool that captures CPU/GPU activity timelines, API traces, and OS-level events. Unlike Nsight Compute (kernel-level), `nsys` shows the big picture — how kernels, memory transfers, communication, and CPU work overlap in time. ## When to Use Reach for this skill when you encounter: - **Triggers**: User wants to profile a training script end-to-end, analyze GPU utilization, find pipeline bottlenecks, check communication/compute overlap, or interpret `.nsys-rep` reports - **Symptoms**: Training slower than expected, GPU idle between iterations, need to understand where time is spent across CPU and GPU, poor scaling in distributed training - **Keywords**: "nsys", "nsight systems", "GPU timeline", "GPU utilization", "kernel launch overhead", "training profiling", "NCCL overlap", "nsys-rep", "cuda trace", "GPU idle", "pipeline stall", "data loading bottleneck" Do NOT use this skill for: - Kernel-level optimization (use Nsight Compute / `ncu` instead) - GPU hardware metrics like SM throughput, cache hit rates (use `ncu`) - GPU monitoring without profiling (use `nvidia-smi`) - Non-CLI usage (GUI workflows, IDE integration) — consult official docs ## Requirements | Dependency | Version | Notes | |------------|---------|-------| | CUDA Toolkit | >=11.0 | Includes `nsys` | | `nsys` binary | Match CUDA version | Verify with `nsys -v` | | NVIDIA GPU | Any supported | | Permissions: `nsys` may require `sudo` or `CAP_SYS_ADMIN` for system-wide tracing and GPU metrics. In containers, use `--privileged` or `--cap-add=SYS_ADMIN`. ## Reporting Principles **Every number must have an authoritative source.** When presenting timing data, kernel counts, API call durations, or any quantitative metric, always cite the source: `nsys stats` report output, `nsys analyze` rule output, exported SQLite query result, recipe CSV, or raw command output. Show the actual command and its output before interpreting. Never synthesize, estimate, or extrapolate numbers that did not come from a tool output. **Use `nsys stats` for structured analysis, not raw trace data.** Always extract metrics via targeted `nsys stats -r <report>` commands rather than trying to read or interpret `.nsys-rep` files directly. Stats reports produce compact, tabular summaries; raw trace data can be enormous (especially with backtraces or verbose API tracing). Run the smallest set of reports needed for the task, then request additional reports only if the initial results raise questions. ## Workflows ### Workflow 1: Profile a DL Training Script Goal: Capture a clean, focused profile of steady-state training iterations. **Step 1 — Add profiler markers** to your training script to skip warmup: ```python # In training script for i, batch in enumerate(train_loader): if i == warmup_iters: torch.cuda.cudart().cudaProfilerStart() train_step(model, batch) if i == warmup_iters + profile_iters: torch.cuda.cudart().cudaProfilerStop() break ``` **Step 2 — Profile with `cudaProfilerApi` capture range:** ```bash nsys profile -c cudaProfilerApi \ -t cuda,nvtx,cudnn,cublas \ --pytorch=autograd-nvtx \ -o train_profile -- python train.py ``` This captures only steady-state iterations — no warmup, no initialization noise. Note: `-t cuda,nvtx,cudnn,cublas` enables **API-specific tracing**. By default, `-t cuda` only traces the CUDA runtime/driver layer — you see kernel names and launch times but cannot attribute them to higher-level libraries. Adding `cudnn` and `cublas` traces the library-level API calls, letting you distinguish convolution time (cuDNN) from GEMM time (cuBLAS) and measure library overhead separately from raw kernel execution. **Step 3 — Quick summary:** ```bash nsys stats -r cuda_gpu_kern_sum,cuda_api_sum,cuda_gpu_mem_time_sum \ train_profile.nsys-rep ``` When you traced library APIs (`cudnn`, `cublas` in `-t`), also run the library-specific reports to see API-level overhead (workspace allocation, algorithm selection) separately from raw kernel execution: ```bash nsys stats -r cudnn_api_sum,cublas_api_sum train_profile.nsys-rep ``` **Step 4 — Detect anti-patterns:** ```bash nsys analyze -r all train_profile.nsys-rep ``` **Step 5 — Dig deeper** based on findings. See Tier 2 references. ### Workflow 2: Diagnose GPU Idle Time Goal: Find why the GPU is idle between training iterations. **Step 1 — Profile with OS runtime tracing:** ```bash nsys profile -t cuda,nvtx,osrt \ --pytorch=autograd-nvtx \ -o idle_debug -- python train.py ``` **Step 2 — Check GPU gaps and utilization:** ```bash nsys analyze -r gpu_gaps,gpu_time_util idle_debug.nsys-rep ``` **Step 3 — Check kernel launch phases:** ```bash nsys stats -r cuda_kern_exec_sum idle_debug.nsys-rep ``` High queue time = GPU was busy (not the issue). Near-zero queue time for all kernels = GPU was starved (host not submitting work fast enough). **Step 4 — Common causes and fixes:** | GPU idle cause | Evidence | Fix | |---------------|----------|-----| | Slow data loading | CPU busy in DataLoader during gaps | Increase `num_workers`, use `pin_memory=True` | | Synchronous memcpy | `cuda_memcpy_sync` rule fires | Use `non_blocking=True` transfers | | Over-synchronization | Frequent `cudaDeviceSynchronize` in trace | Remove unnecessary sync calls | | Host-side computation | CPU sampling shows compute during gaps | Move to GPU or overlap with async ops | | Python GIL contention | GIL trace shows contention | Use multiprocessing, reduce Python overhead | ### Workflow 3: Profile Distributed Training Goal: Profile multi-GPU/multi-node training with communication analysis. **Step 1 — Collect per-rank profiles:** ```bash nsys profile -t cuda,nvtx,mpi,ucx \ --pytorch=autograd-nvtx \ -o profile_%q{RANK} \ -- torchrun --nproc_per_node=8 train.py ``` **Step 2 — Analyze NCCL communication/compute overlap:** ```bash nsys recipe nccl_gpu_overlap_trace -- profile_*.nsys-rep nsys recipe nccl_gpu_time_util_map -- profile_*.nsys-rep ``` **Step 3 — Check per-rank utilization:** ```bash nsys recipe cuda_gpu_time_util_map -- profile_*.nsys-rep ``` **Step 4 — Check for stragglers:** Compare `cuda_gpu_kern_sum` across ranks. If one rank is slower, check its network and data loading patterns. ### Workflow 4: Analyze Iteration Time Consistency Goal: Check whether training iterations are stable or have outliers. ```bash # Profile with NVTX iteration markers nsys profile --pytorch=autograd-nvtx -t cuda,nvtx \ -o iter_check -- python train.py # Check iteration timing distribution nsys stats -r nvtx_pushpop_sum iter_check.nsys-rep # Check GPU projection per NVTX range nsys stats -r nvtx_gpu_proj_sum iter_check.nsys-rep # Visual pace analysis nsys recipe nvtx_pace -- iter_check.nsys-rep ``` High StdDev in iteration duration indicates inconsistency — investigate outlier iterations on the timeline. ### Workflow 5: Attribute Kernels to Source Code via Stack Traces Goal: Identify which Python function or code path triggers expensive GPU kernels. **Step 1 — Profile with backtrace collection:** ```bash nsys profile -t cuda,nvtx \ --backtrace=cuda \ --python-backtrace=lbr \ --pytorch=autograd-nvtx \ -o stacktrace_profile -- python train.py ``` - `--backtrace=cuda`: Captures CUDA API call stacks (C/C++ frames) so each `cudaLaunchKernel` shows the host-side call chain that triggered it. - `--python-backtrace=lbr`: Captures Python-level call stacks, correlating GPU work back to specific Python functions (e.g., `compute_attention` vs `compute_ffn`). **Step 2 — Get kernel summary and NVTX attribution:** Use targeted stats reports to identify top kernels and their NVTX context: ```bash # Top kernels by total GPU time nsys stats -r cuda_gpu_kern_sum stacktrace_profile.nsys-rep # Kernels attributed to NVTX ranges (maps kernels to annotated code regions) nsys stats -r nvtx_kern_sum stacktrace_profile.nsys-rep ``` The `nvtx_kern_sum` report (requires `--pytorch=autograd-nvtx` or manual NVTX annotations) maps each kernel to its enclosing NVTX range, directly showing which Python function or autograd op launched it. This is more efficient than manually cross-referencing raw backtrace data. **Step 3 — For PyTorch models**, `--pytorch=autograd-nvtx` automatically wraps each autograd op in an NVTX range. Combined with backtrace, this maps: GPU kernel → CUDA API call → Python function → PyTorch autograd op. **When to use**: Workloads with multiple code paths launching similar kernels (e.g., attention vs FFN both calling GEMM). Stack traces disambiguate which caller is responsible for the dominant kernel time. ## Output Formats **Report files** (`.nsys-rep`): Binary format, viewable in GUI or processed with `nsys stats`, `nsys analyze`, `nsys export`, `nsys recipe`. **Stats output formats**: `column` (terminal), `csv`, `json`, `table`, `tsv`, `hdoc`, `htable`. **Export formats**: `sqlite` (SQL queries), `arrow`/`parquetdir` (Pandas/Dask), `hdf`, `jsonlines`, `text`. **Recipe output**: Directory with CSV/Parquet data + Plotly HTML visualizations + `.nsys-analysis` (Jupyter notebook). **Key stats report columns:** | Report | Key columns | |--------|------------| | `cuda_gpu_kern_sum` | Time%, Total Time, Instances, Kernel Name | | `cuda_api_sum` | Time%, Total Time, Num Calls, API Name | | `cuda_kern_exec_sum` | API Time, Queue Time, Kernel Time | | `cuda_gpu_mem_time_sum` | Time%, Total Time, Operations, Direction | | `nvtx_gpu_proj_sum` | Projected Duration, Original Duration, GPU Op Count | ## Examples ### Example 1: Quick DL Profile and Summary ```bash # Profile nsys profile -t cuda,nvtx,cudnn,cublas \ --pytorch=autograd-nvtx --stats=true \ -o quick_profile -- python train.py # Auto-generates stats at the end of profiling ``` ### Example 2: Detect Sync Memcpy in DataLoader ```bash nsys profile -t cuda,nvtx -o dataloader_check -- python train.py nsys analyze -r cuda_memcpy_sync,cuda_memcpy_async dataloader_check.nsys-rep ``` If flagged, fix with: ```python loader = DataLoader(dataset, pin_memory=True, num_workers=4) tensor_gpu = tensor_cpu.to(device, non_blocking=True) ``` ### Example 3: Multi-Node NCCL Analysis ```bash # Collect nsys profile -t cuda,nvtx,mpi -o rank_%q{RANK} \ -- torchrun --nproc_per_node=8 train.py # Analyze overlap nsys recipe nccl_gpu_overlap_trace -- rank_*.nsys-rep # Visualize nsys recipe nccl_gpu_time_util_map -- rank_*.nsys-rep ``` ### Example 4: API-Level Breakdown (cuDNN vs cuBLAS) ```bash # Profile with library-level tracing nsys profile -t cuda,nvtx,cudnn,cublas \ -o api_breakdown -- python model.py # cuDNN API summary (convolution calls) nsys stats -r cudnn_api_sum api_breakdown.nsys-rep # cuBLAS API summary (GEMM calls) nsys stats -r cublas_api_sum api_breakdown.nsys-rep # Compare with kernel-level view nsys stats -r cuda_gpu_kern_sum api_breakdown.nsys-rep ``` The API-level reports (`cudnn_api_sum`, `cublas_api_sum`) show time spent in library calls including overhead (workspace allocation, algorithm selection), while `cuda_gpu_kern_sum` shows only raw GPU kernel execution. The difference reveals library-side overhead. ## Error Handling | Error | Cause | Fix | |-------|-------|-----| | `nsys: command not found` | Not in PATH | `export PATH=$PATH:/usr/local/cuda/bin` | | `Permission denied` or `requires root` | Needs elevated privileges | `sudo nsys ...` or `--cap-add=SYS_ADMIN` in containers | | No CUDA activity captured | App didn't use GPU during collection window | Adjust `--delay`/`--duration`, or use `cudaProfilerApi` capture range | | Report file very large | Long profile with many APIs traced | Use focused capture (`-c cudaProfilerApi`), reduce `--duration` | | `--pytorch` has no effect | Wrong nsys version or Python env | Verify nsys version supports `--pytorch`; check Python is in PATH | | `nsys stats` shows empty reports | No matching activity in report | Check `--trace` flags included the right APIs | | MPI rank profiles out of sync | Clock skew between nodes | Use NTP sync; analyze per-rank independently | | `cudaProfilerStart` not captured | Missing `-c cudaProfilerApi` flag | Add `--capture-range=cudaProfilerApi` | | Recipe fails with import error | Missing Python dependencies | Install recipe dependencies: `pip install pandas plotly` | ## Finding More Information ### Tier 1: This File (SKILL.md) You are reading it now. The workflows and error table above cover the most common DL profiling tasks. Search this file first. ### Tier 2: references/ Directory Grep for keywords across `references/` — headers are grep-friendly: - `references/cli-profiling.md` — Complete `nsys profile` flags for DL - `references/cli-post-collection.md` — `nsys stats`, `analyze`, `export`, `recipe` commands - `references/app-preparation.md` — Focused profiling, NVTX markers, PyTorch patterns - `references/stats-reports.md` — CUDA statistical report columns and meanings - `references/expert-systems.md` — Expert system rules, anti-pattern detection - `references/recipes-dl.md` — DL-relevant advanced recipes with examples - `references/nvtx-analysis.md` — NVTX statistical reports for annotated code **How to search:** 1. `Grep` for your keyword across `references/` 2. `Read` only the file that Grep points to ### Tier 3: Official Documentation If Tiers 1-2 don't answer: - [User Guide](https://docs.nvidia.com/nsight-systems/UserGuide/index.html) — Full CLI reference, all tracing options - [Analysis Guide](https://docs.nvidia.com/nsight-systems/AnalysisGuide/index.html) — Stats reports, expert systems, recipes WebFetch or WebSearch these URLs for the latest content. Consider distilling new findings back into `references/`.