--- name: perf-workload-profiling-f68a26436acc description: > Code instrumentation for timing workloads. Two scenarios: (1) Training loop — inject manual timing to report per-iteration latency, throughput (samples/sec), and data load time. (2) Standalone kernel/op — write CUDA event timing code with warmup, per-iteration statistics, and anti-pattern avoidance. Also covers NVTX annotation for labeling profiler timelines. NOT for: running or analyzing profiler tools (nsys, ncu, Nsight Systems, Nsight Compute), writing kernels (Triton, CuTe, CUDA), applying optimizations (CUDA Graphs, gradient checkpointing, fusion), or interpreting roofline/SOL% metrics. Triggers: "measure throughput", "benchmark this function", "time my training loop", "samples per second", "NVTX annotate", "instrument my dataloader", "data load time", "kernel timing", "how do I time". license: Apache-2.0 metadata: author: NVIDIA Corporation --- # Workload Profiling ## Quick Reference Pick ONE path based on the workload type: | Workload | Approach | Section | |----------|----------|---------| | Training loop | Manual `torch.cuda.synchronize()` + `time.perf_counter()` with warmup | Loop Workloads — Manual Timing | | Single kernel or op | Write CUDA event benchmark (pre-allocate, warmup, event pairs) | Non-Loop Workloads — CUDA Event Benchmarking | | Add timeline labels for nsys | Use `@nvtx.annotate` decorator or context manager | NVTX Reference | ## Principles - **Measure, don't guess.** Every performance claim must trace back to profiler output or structured measurement data. Never invent metrics. - **Isolate steady-state.** Warmup costs (CUDA context init, cuDNN autotuning, JIT compilation) distort measurements. Always exclude warmup iterations before collecting data. - **Use hardware timing.** CUDA events measure GPU time precisely. CPU timers (`time.perf_counter()`) include host overhead and miss asynchronous execution. - **No sync inside measurement loops.** Each `torch.cuda.synchronize()` adds 10-50us overhead. Record CUDA events asynchronously, sync once at the end. - **Pre-allocate everything.** Tensors, events, compiled kernels — all before the timing loop. For CuTe DSL kernels, pre-compile with `cute.compile()`. - **Minimize profiler interference.** Start with lightweight measurement (manual timing for latency/throughput) and escalate to heavier tools (Kineto, nsys, ncu) only when lighter tools cannot answer the question. ## Loop Workloads — Manual Timing For training loops and iterative workloads, use manual `torch.cuda.synchronize()` + `time.perf_counter()` timing with warmup to measure per-iteration latency, throughput, and data load time. ### Injection Template Read the user's training script, understand the dataloader and loop structure, then inject timing code. ```python import time import torch WARMUP = 5 NUM_ITERS = 30 BATCH_SIZE = 128 # global batch size for throughput calculation iter_times = [] data_times = [] for i, batch in enumerate(dataloader): if i >= WARMUP + NUM_ITERS: break t_data_end = time.perf_counter() torch.cuda.synchronize() t_start = time.perf_counter() # ... existing training loop body ... torch.cuda.synchronize() t_end = time.perf_counter() if i >= WARMUP: iter_ms = (t_end - t_start) * 1000 iter_times.append(iter_ms) if i > 0: data_times.append((t_data_end - prev_iter_end) * 1000) print(f"[{i:04d}]: iter {iter_ms:.2f} ms, fps {BATCH_SIZE / (iter_ms / 1000):.2f}") prev_iter_end = t_end import statistics print(f"Average: iter {statistics.mean(iter_times):.2f} ms, " f"fps {BATCH_SIZE / (statistics.mean(iter_times) / 1000):.2f}") ``` ### Interpreting Results - **iter (ms)**: Wall-clock time per iteration (compute + communication, excluding data loading) - **data (ms)**: Time spent in dataloader between iterations. If `data / iter > 0.2`, data loading is a bottleneck. - **fps**: Global throughput in samples/second. Use with known FLOPs-per-sample to compute MFU. ### Limitations Manual timing reports **aggregate** iteration timing — not per-sub-phase breakdown (forward, backward, optimizer). When the user asks **where time is spent within compute**: 1. Add `torch.cuda.synchronize()` + `time.perf_counter()` around each sub-phase for a one-off diagnosis, OR 2. Add NVTX annotations and run with `nsys profile` for timeline visualization. ## Non-Loop Workloads — CUDA Event Benchmarking For single kernels, one-shot inference, or standalone operations, write CUDA event benchmarking code directly. ### PyTorch: Simple (Mean Only) ```python import torch def benchmark(fn, warmup=50, iters=100): for _ in range(warmup): fn() torch.cuda.synchronize() start = torch.cuda.Event(enable_timing=True) end = torch.cuda.Event(enable_timing=True) start.record() for _ in range(iters): fn() end.record() torch.cuda.synchronize() return start.elapsed_time(end) / iters # ms per iteration ``` ### PyTorch: Detailed (Per-Iteration Stats) ```python import torch import statistics def benchmark_detailed(fn, warmup=50, iters=100): for _ in range(warmup): fn() torch.cuda.synchronize() starts = [torch.cuda.Event(enable_timing=True) for _ in range(iters)] ends = [torch.cuda.Event(enable_timing=True) for _ in range(iters)] for i in range(iters): starts[i].record() fn() ends[i].record() torch.cuda.synchronize() times = [starts[i].elapsed_time(ends[i]) for i in range(iters)] return { "mean_ms": statistics.mean(times), "median_ms": statistics.median(times), "std_ms": statistics.stdev(times) if len(times) > 1 else 0, "min_ms": min(times), "max_ms": max(times), } ``` ### Anti-Patterns | Anti-Pattern | Problem | |--------------|---------| | `torch.cuda.synchronize()` before AND after each iteration | Adds ~10-50us overhead per iteration | | `time.perf_counter()` for GPU timing | Measures CPU time, misses async GPU execution | | Missing warmup | First iterations include JIT, clock ramp-up, context init | | Allocating tensors inside measurement loop | Allocation overhead pollutes timing | | Reporting only mean | Hides variance, outliers, bimodal distributions | For additional benchmarking templates (CUDA Graph, CuTe DSL, Triton, Raw CUDA), see [references/benchmarking-patterns.md](references/benchmarking-patterns.md). ## NVTX Reference NVTX (NVIDIA Tools Extension) adds named annotations to profiler timelines. Use NVTX to label phases (forward, backward, optimizer) for readability in nsys — not for measurement. ```python import nvtx # Decorator — annotates every call @nvtx.annotate("training_step", color="blue") def training_step(): ... # Context manager — annotates a code block with nvtx.annotate("data_loading", color="green"): batch = next(dataloader) ``` - **Do** annotate training phases (forward, backward, optimizer, data loading) for nsys timeline clarity. - **Do not** annotate for measurement — use CUDA events or manual timing instead. - **Do not** over-annotate — too many fine-grained ranges add visual clutter and minor overhead. For NVTX domains, categories, payloads, and legacy API details, see [references/nvtx-api.md](references/nvtx-api.md). ## References - [references/benchmarking-patterns.md](references/benchmarking-patterns.md) — CUDA Graph, CuTe DSL, Triton, Raw CUDA templates; warmup guidance; GPU hardware properties; reporting format - [references/nvtx-api.md](references/nvtx-api.md) — Domains, categories, payloads, legacy push/pop API - [references/pytorch-profiler-api.md](references/pytorch-profiler-api.md) — PyTorch 2.0+ profiler API changes (`device_time` vs deprecated `cuda_time`) --- Source attribution: yo-steven Original project: https://github.com/yo-steven/skills-exploration-20260522/blob/6e1127ffb7690aa8038b172d0ae7cc720754870d/skills/TensorRT-LLM/perf-workload-profiling/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice SPDX-License-Identifier: Apache-2.0 AND CC-BY-4.0 Copyright (c) 2026 NVIDIA Corporation This project is licensed under a dual license: - Apache License 2.0 (for code and configuration) - Creative Commons Attribution 4.0 International (CC BY 4.0) (for documentation) You may use the contents of this repository under the terms of either license, or both, as applicable to the type of content. ================================================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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