aicb/workload/aiob_inputs/Example.txt案例时间单位

[zzy999-win]

我想问一下SimAI/aicb/workload/aiob_inputs/Example.txt例子中的时间单位是什么啊

[tianhao909]

【Manual reply】

ns还是 us
可以再看看调用栈
也可以参考这个
aliyun/aicb#59

[tianhao909]

[Reply from Agent A] [For reference only]

cn

根据当前代码实现，aicb/workload/aiob_inputs/Example.txt 里的 time_gpu_max、time_gpu_min、time_gpu_avg 单位应为 毫秒（ms）。

原因是 AICB 在读取该文件时，会将这些值乘以 1000 后作为内部时间使用，也就是转换成 微秒（us）。对应逻辑可参考 aliyun/aicb/utils/utils.py 中解析 comp_filepath 的实现。

所以结论是：

• Example.txt 中展示的原始值：ms
• AICB 读入后的内部使用单位：us

目前示例文件和 README 里确实没有把这一点写清楚，容易引起歧义。后续比较合适的最小改动是补一条文档说明，避免用户再混淆。

en

Based on the current code path, the time_gpu_max, time_gpu_min, and time_gpu_avg values in aicb/workload/aiob_inputs/Example.txt should be interpreted as milliseconds (ms).

The reason is that when AICB parses this file, it multiplies these values by 1000 before using them internally, which means they are converted into microseconds (us). The relevant logic can be found in aliyun/aicb/utils/utils.py.

So the conclusion is:

• Raw values in Example.txt: ms
• Internal unit used by AICB after parsing: us

At the moment, this is not explicitly documented in the example file or README, so the confusion is understandable. A minimal follow-up improvement would be to add a short documentation note near the --comp_filepath workload/aiob_inputs/Example.txt examples.

[tianhao909]

[Reply from Agent B] [For reference only]

cn

您好，关于 aicb/workload/aiob_inputs/Example.txt 里时间相关字段的单位，结合 AICB 源码可以这样理解：

1. train_iter:10
   表示迭代/运行次数，不是时间，没有“时间单位”。

2. time_gpu_max / time_gpu_min / time_gpu_avg

   • 若该文件是由训练路径 AIOB 测时后，经 process_all_keys 汇总生成的，则这些统计量与 cuda_timing_decorator 记录的 GPU 事件时长一致：torch.cuda.Event.elapsed_time 返回毫秒，代码再乘以 1000，因此链路上写入的原始采样单位是 微秒（μs），汇总到文件后一般仍应理解为 μs。
   • 另一方面，extract_averages() 在读取 time_gpu_avg 时会对解析到的数字再 × 1000，从实现上相当于把文件里的数当作 毫秒（ms） 再换算成内部使用的 μs。因此：“文件里写的数”与“解析器如何解释”之间存在不一致风险——若文件已是 μs，再经 extract_averages 会可能被多放大 1000 倍，需要与您本地端到端生成结果对照确认。

建议：在仓库里为 Example.txt 与 README 中 --comp_filepath 小节补充一句官方约定（文件到底按 μs 还是按 ms），并与 extract_averages 的实现统一，避免仿真 workload 计算时间数量级错误。

（ns-3-alibabacloud 与 AIOB 文本格式无直接关系；SimAI 侧主要消费 AICB 生成的 workload。）

en

Hi — regarding the time fields in aicb/workload/aiob_inputs/Example.txt, here is what the AICB code path implies:

1. train_iter:10
   This is an iteration/run count, not a duration, so it does not have a time unit.

2. time_gpu_max / time_gpu_min / time_gpu_avg

   • If the file is produced by the training AIOB path and then summarized by process_all_keys, these statistics come from GPU timings recorded in cuda_timing_decorator. PyTorch’s torch.cuda.Event.elapsed_time returns milliseconds, and the code multiplies by 1000, so the stored per-sample timings are in microseconds (μs). The summarized time_gpu_* lines are therefore naturally interpreted as μs as well.
   • However, extract_averages() multiplies parsed time_gpu_avg values by 1000 again (and the param_time branch comments indicate the final internal values are in μs). That means the parser behaves as if the text file numbers were milliseconds that need converting to μs. So there is a potential inconsistency: if the file already contains μs (as the AIOB summarization path suggests), feeding it through extract_averages() may over-scale by 1000× unless the project intends a different on-disk convention.

Recommendation: document the intended on-disk unit for Example.txt next to --comp_filepath in the README (and/or a short header comment in the example), and align that convention with extract_averages() to avoid wrong compute-time magnitudes in generated SimAI workloads.

(The ns-3-alibabacloud repo is not directly tied to the AIOB text format; SimAI mainly consumes AICB-generated workloads.)

[tianhao909]

[Reply from Agent C] [For reference only]

中文版
感谢提问！

Example.txt 文件中 time_gpu_max、time_gpu_min、time_gpu_avg 的时间单位是 微秒（µs，microseconds）。

单位推导链：

AIOB 实际测量：使用 CUDA Events 计时，torch.cuda.Event.elapsed_time() 返回毫秒，再 × 1000 得到微秒，存入 JSON 中间文件作为 time_gpu 字段。对于 Python timer（time.time()），则乘以 1e6 直接得到微秒。

process_all_keys() 将多次 time_gpu（µs）聚合为 time_gpu_avg，最终写入 Example.txt——因此文件里的值单位是微秒。

extract_averages() 在读取该文件时执行 × 1000（µs → ns），用于生成 SimAI 仿真 workload 文件。

astra-sim 中 CLOCK_PERIOD = 1（1 Tick = 1 ns），最终以纳秒运行仿真。

以 Example.txt 中的 Emb 为例：

time_gpu_avg: 799 → 799 µs ≈ 0.8 ms（Embedding 层在 GPU 上的合理耗时）
param_time: 17374 → 17374 µs ≈ 17.4 ms（优化器更新步骤，合理）
当前代码中的注释存在误导（cuda_timing_decorator 里有 "时间以毫秒为单位" 的错误注释，实际是微秒），文件本身也缺少单位说明。我们将在后续更新 README 和代码注释来澄清这一点。

English Version
Thanks for the question!

The time_gpu_max, time_gpu_min, and time_gpu_avg values in Example.txt are in microseconds (µs).

Unit derivation chain:

AIOB measurement: CUDA Events timing — torch.cuda.Event.elapsed_time() returns milliseconds, then × 1000 converts to microseconds, stored as time_gpu in the intermediate JSON file. For Python timer-based measurements (time.time()), the value in seconds is multiplied by 1e6 → directly in microseconds.

process_all_keys() aggregates multiple time_gpu samples (in µs) into time_gpu_avg, then writes to the AIOB output file (i.e., Example.txt). So the values in the file are in microseconds.

extract_averages() reads these values and applies × 1000 (µs → ns) when generating SimAI simulation workload files.

astra-sim uses CLOCK_PERIOD = 1 (1 Tick = 1 ns), so the simulation runs in nanoseconds.

For example, from Example.txt:

time_gpu_avg: 799 → 799 µs ≈ 0.8 ms for the Embedding layer (reasonable on modern GPUs)
param_time: 17374 → 17374 µs ≈ 17.4 ms for optimizer update step (reasonable)
Note: there is a misleading comment in the source code (cuda_timing_decorator contains a comment saying "time in milliseconds" when it is actually microseconds), and the file itself lacks any unit annotation. We will update the README and code comments to clarify this in a follow-up fix.

已完成对 Issue #269 的完整分析。核心结论：Example.txt 中的 time_gpu_* 值单位为微秒（µs），通过代码追踪（CUDA 计时 → JSON 中间文件 → Example.txt 格式 → extract_averages() ×1000 → astra-sim nanosecond Tick）得到验证。建议的最小改动是：在 Example.txt 文件头部加注释声明单位，同时修复 utils.py 中误导性的注释和变量名，并在 README 的相关章节补充格式说明。