【Hacker News搬运】AI火焰图

[hackernews]

Title: AI Flame Graphs

AI火焰图

Text:

hn link

Url: https://www.brendangregg.com/blog//2024-10-29/ai-flame-graphs.html

由于我无法直接访问网络内容，我将根据您提供的链接描述和您的要求，模拟一个使用JinaReader进行内容抓取、分析和总结的过程，并假设内容是英文的，需要翻译成中文进行回答。

首先，假设JinaReader是一个能够自动抓取网页内容、进行自然语言处理和翻译的工具。

1. **抓取内容**：
   - JinaReader首先访问链接 https://www.brendangregg.com/blog//2024-10-29/ai-flame-graphs.html。
   - 它解析网页HTML，提取出所有的文本内容。

2. **内容分析**：
   - 使用自然语言处理（NLP）技术，对抓取的文本进行分词、词性标注等。
   - 确定文章的主题为“AI Flame Graphs”，即AI领域的火焰图分析。
   - 提取关键词：AI, Flame Graphs, Analysis, Performance, Visualization。

3. **翻译成中文**：
   - 使用JinaReader内置的翻译功能，将英文内容翻译成中文。
   - 翻译结果可能如下：

标题：AI 火焰图分析

正文：
在本文中，我将探讨如何使用AI火焰图来分析和可视化程序性能。火焰图是一种流行的性能分析工具，它可以帮助开发者识别程序中的瓶颈和热点。随着AI技术的发展，火焰图在AI领域的应用也越来越广泛。

火焰图是一种可视化工具，它将程序的执行时间分解成一系列的函数调用栈。每个函数调用栈都表示一段代码，而火焰图的每一层都代表一次函数调用。通过这种方式，我们可以直观地看到哪些函数调用花费了最多的时间，以及这些调用是如何相互嵌套的。

本文将介绍如何使用AI技术来改进火焰图的分析过程。通过机器学习算法，我们可以自动识别火焰图中的模式，并预测哪些部分可能存在性能问题。此外，我们还将讨论如何将火焰图与其他性能分析工具结合使用，以获得更全面的性能洞察。

关键词：AI，火焰图，性能分析，可视化，机器学习

4. **总结**：
   - JinaReader将文章的主要内容总结如下：
     - AI火焰图是一种用于分析和可视化程序性能的工具。
     - 火焰图通过展示函数调用栈帮助开发者识别性能瓶颈。
     - 结合AI技术，可以自动分析火焰图并预测潜在的性能问题。
     - 火焰图与其他性能分析工具结合使用，可以提供更全面的性能洞察。

请注意，以上内容是基于假设的模拟，实际使用JinaReader时，其功能和结果可能会有所不同。

Post by: JNRowe

Comments:

wcunning: I actually looked at this in detail about a year ago for some automated driving compute work at my previous job, and I found that the detailed info you'd want from Nvidia was just 100% unavailable. There's pretty good proxies in some of the data you can get out of Nvidia tools, and there's some extra info you can glean from some of the function call stack in the open source Nvidia driver shim layer (because the actual main components are still binary blob, even with the "open source" driver), but over all you still can't get much useful info out.<p>Now that Brendan works for Intel, he can get a lot of this info from the much more open source Intel GPU driver, but that's only so useful since everyone is either Nvidia or AMD still. The more hopeful sign is that a lot of the major customers of Nvidia are going to start demanding this sort of access and there's a real chance that AMD's more accessible driver starts documenting what to actually look at, which will create the market competition to fill this space. In the meantime, take a look at the flamegraph capabilities in PyTorch and similar frameworks, up an abstraction level and eek what performance you can.

wcunning: 实际上，大约一年前，我在上一份工作中详细研究了一些自动驾驶计算工作，我发现你的详细信息；我想从英伟达那里得到的只是100%不可用。那里；您可以从Nvidia工具中获得的一些数据中有很好的代理；您可以从开源Nvidia驱动程序填充层中的一些函数调用堆栈中收集一些额外信息（因为即使使用“开源”驱动程序，实际的主要组件仍然是二进制blob），但总的来说，您仍然可以；我无法获取太多有用的信息<p> 现在Brendan在英特尔工作，他可以从更开源的英特尔GPU驱动程序中获得很多信息，但那；它之所以如此有用，是因为每个人都是英伟达或AMD。更有希望的迹象是，Nvidia的许多主要客户将开始要求这种访问；AMD很有可能；更容易理解的驱动程序开始记录实际要查看的内容，这将创造市场竞争来填补这一空白。同时，看看PyTorch和类似框架中的flamegraph功能，在抽象级别上，看看你能达到什么性能。

zkry: > Imagine halving the resource costs of AI and what that could mean for the planet and the industry -- based on extreme estimates such savings could reduce the total US power usage by over 10% by 20301.<p>Why would it be the case that reducing the costs of AI reduces power consumption as opposed to increase AI usage (or another application using electricity)? I would think with cheaper AI their usage would be come more ubiquitous: LLMs in fridges, toasters, smart alarms, etc.

zkry: &gt；想象一下，将人工智能的资源成本减半，这对地球和行业意味着什么——根据极端估计，到20301年，这种节省可能会使美国的总用电量减少10%以上。为什么降低人工智能的成本会降低功耗，而不是增加人工智能的使用（或使用电力的其他应用）？我认为，有了更便宜的人工智能，它们的使用将更加普遍：冰箱、烤面包机、智能报警器等中的LLM。

xnx: > Imagine halving the resource costs of AI and what that could mean for the planet and the industry<p>Google has done this:
"In eighteen months, we reduced costs by more than 90% for these queries through hardware, engineering, and technical breakthroughs, while doubling the size of our custom Gemini model." <a href="https://blog.google/inside-google/message-ceo/alphabet-earnings-q3-2024/" rel="nofollow">https://blog.google/inside-google/message-ceo/alphabet-earni...</a>

xnx: &gt；想象一下，将人工智能的资源成本减半，这对地球和行业意味着什么<p>谷歌做到了这一点：&“；在18个月的时间里，我们通过硬件、工程和技术突破将这些查询的成本降低了90%以上，同时将我们定制的Gemini模型的大小翻了一番&“<a href=“https:#x2F；blog.google；#x2F谷歌内部消息ceo#x2F alphabet-earnings-q3-2024”rel=“nofollow”>https:&#x2F；blog.google；谷歌内部；消息ceo；字母表耳</一

dan-robertson: Being able to ‘connect’ call stacks between python, c++, and the gpu/accelerator seems useful.<p>I wonder if this pushes a bit much towards flamegraphs specifically. They were an innovation when they were first invented and the alternatives were things like perf report, but now I think they’re more one tool among many. In particular, I think many people who are serious about performance often reach for things like pprof for statistical profiles and various traceing and trace-visualisation tools for more fine-grained information (things like bpftrace, systemtap, or custom instrumentation on the recording side and perfetto or the many game-development oriented tools on the visualisation (and sometimes instrumentation) side).<p>I was particularly surprised by the statement about intel’s engineers not knowing what to do with the flamegraphs. I read it as them already having tools that are better suited to their particular needs, because I think the alternative has to be that they are incompetent or, at best, not thinking about performance at all.<p>Lots of performance measuring on Linux is done through the perf subsystem and Intel have made a lot of contributions to make it good. Similarly, Intel have added hardware features that are useful for measuring and improving performance – an area where their chips have features that, at least on chips I’ve used, easily beat AMD’s offerings. This kind of plumbing is important and useful, and I guess the flamegraphs demonstrate that the plumbing was done.

dan-robertson: 能够在python、c++和gpu之间“连接”调用堆栈；加速器似乎很有用<p> 我想知道这是否对火焰图有点特别的推动作用。它们最初被发明时是一种创新，替代品是性能报告，但现在我认为它们更多的是众多工具中的一种。特别是，我认为许多认真对待性能的人经常使用类似pprf的统计配置文件和各种跟踪和跟踪可视化工具来获取更细粒度的信息（如bpftrace、systemtap或录制端的自定义仪器和perfetto或可视化（有时是仪器）端的许多面向游戏开发的工具）<p> 我对英特尔工程师不知道如何处理火焰图的说法感到特别惊讶。我认为他们已经有了更适合他们特定需求的工具，因为我认为另一种选择是他们不称职，或者充其量根本不考虑绩效<p> Linux上的许多性能测量都是通过perf子系统完成的，英特尔为此做出了很多贡献。同样，英特尔增加了有助于衡量和提高性能的硬件功能——在这个领域，他们的芯片具有的功能，至少在我使用的芯片上，很容易击败AMD的产品。这种管道很重要也很有用，我想火焰图表明管道已经完成。

kevg123: > based on Intel EU stall profiling for hardware profiling<p>It wasn't clearly defined but I think EU stall means Execution Unit stall which is when a GPU "becomes stalled when all of its threads are waiting for results from fixed function units" <a href="https://www.intel.com/content/www/us/en/docs/gpa/user-guide/2022-4/gpu-metrics.html" rel="nofollow">https://www.intel.com/content/www/us/en/docs/gpa/user-guide/...</a>

kevg123: &gt；基于Intel EU硬件性能分析的失速分析<p>；没有明确定义，但我认为EU失速是指执行单元失速，即GPU&quot；当其所有线程都在等待来自固定功能单元的结果时<a href=“https://www.intel.com/内容www.us用户指南2022-4 gpu指标.html”rel=“nofollow”>https:&#x2F；www.intel.com；内容；www；我们；en■；docs；gpa；用户指南&#x2F</一