Linux:CPU基准测试需要更长的时间和不同的CPU利用率级别 [英] Linux: CPU benchmark requiring longer time and different CPU utilization levels

问题描述

为了进行研究，我需要一个CPU基准测试才能在Ubuntu笔记本电脑(Ubuntu 15.10，Memory 7.7 GiB，Intel Core i7-4500U CPU @ 1.80HGz x 4、64位)上进行一些实验.在理想的世界中，我希望有一个满足以下条件的基准:

For my research I need a CPU benchmark to do some experiments on my Ubuntu laptop (Ubuntu 15.10, Memory 7.7 GiB, Intel Core i7-4500U CPU @ 1.80HGz x 4, 64bit). In an ideal world, I would like to have a benchmark satisfying the following:

1. CPU应该是官方基准测试，而不是出于透明度目的由我自己创建.
2. 在笔记本电脑上执行基准测试所需的时间应至少5分钟(越多越好).
3. 在整个执行过程中，基准测试应导致不同级别的CPU.例如，我不希望有一个基准可以使CPU利用率永久保持在100％左右-所以我想要一个基准，可以使CPU利用率随时间变化.

1. The CPU should be an official benchmark rather than created by my own for transparency purposes.
2. The time needed to execute the benchmark on my laptop should be at least 5 minutes (the more the better).
3. The benchmark should result in different levels of CPU throughout execution. For example, I don't want a benchmark which permanently keeps the CPU utilization level at around 100% - so I want a benchmark which will make the CPU utilization vary over time.

尤其是第2点和第3点是我研究的关键.但是，到目前为止，我找不到任何合适的基准.到目前为止，我发现的基准包括:sysbench，CPU斐波那契，CPU Blowfish，CPU加密鱼，CPU N-Queens.但是，所有这些仅需要几秒钟即可完成，并且笔记本电脑上的利用率始终处于100％的水平.

Especially points 2 and 3 are really key for my research. However, I couldn't find any suitable benchmarks so far. Benchmarks I found so far include: sysbench, CPU Fibonacci, CPU Blowfish, CPU Cryptofish, CPU N-Queens. However, all of them just need a couple of seconds to complete and the utilization level on my laptop is at 100% constantly.

问题:有人知道适合我的基准吗?我也很高兴听到您有其他意见/问题.谢谢！

Question: Does anyone know about a suitable benchmark for me? I am also happy to hear any other comments/questions you have. Thank you!

推荐答案

要选择基准，您需要确切地知道要测量的内容.您的问题不包含此内容，因此没有任何人可以毫不犹豫地告诉您.

To choose a benchmark, you need to know exactly what you're trying to measure. Your question doesn't include that, so there's not much anyone can tell you without taking a wild guess.

如果您要衡量Turbo时钟速度如何工作，以使功率有限的CPU(如笔记本电脑)在突发性工作负载下运行得更快(例如，将Haswell与Skylake的新的和改进的电源管理进行比较)，则可以运行一些功能微不足道的时间是1秒钟，关闭2秒，然后计算它管理的循环迭代次数.

If you're trying to measure how well Turbo clock speed works to make a power-limited CPU like your laptop run faster for bursty workloads (e.g. to compare Haswell against Skylake's new and improved power management), you could just run something trivial that's 1 second on, 2 seconds off, and count how many loop iterations it manages.

占空比和周期长度应为基准参数，因此可以进行绘制.例如具有非常快速的开/关周期， Skylake的速度更快，对Turbo的反应将更快地加速，而下降到最小功率的速度更快(为下一次爆发留出更多的余量).

The duty cycle and cycle length should be benchmark parameters, so you can make plots. e.g. with very fast on/off cycles, Skylake's faster-reacting Turbo will ramp up faster and drop down to min power faster (leaving more headroom in the bank for the next burst).

在演讲中的发言者(英特尔CPU电源管理的首席架构师)说，Javascript基准测试实际上具有足够的爆发力，可以使Skylake的电源管理提供可衡量的加速，这与大多数其他基准测试不同，后者只是将CPU固定为100％全程. 因此，如果您想使用众所周知的现成基准，请看看Javascript基准.

The speaker in that talk (the lead architect for power management on Intel CPUs) says that Javascript benchmarks are actually bursty enough for Skylake's power management to give a measurable speedup, unlike most other benchmarks which just peg the CPU at 100% the whole time. So maybe have a look at Javascript benchmarks, if you want to use well-known off-the-shelf benchmarks.

如果要自己滚动，请将循环承载的依赖关系链放入循环中，最好是在微体系结构之间的延迟不太变化的事物.一长串整数加法将起作用，Fibonacci是阻止编译器对其进行优化的好方法.选择一个固定的迭代计数以适合当前的CPU速度，或者每10M迭代检查一次时钟.

If rolling your own, put a loop-carried dependency chain in the loop, preferably with something that's not too variable in latency across microarchitectures. A long chain of integer adds would work, and Fibonacci is a good way to stop the compiler from optimizing it away. Either pick a fixed iteration count that works well for current CPU speeds, or check the clock every 10M iterations.

或设置一个计时器，该计时器将在一段时间后触发，并设置一个在循环内检查的标志. (例如来自信号处理程序).具体来说， alarm(2) 可能是一个不错的选择.记录一下您在此工作中进行了多少次迭代.

Or set a timer that will fire after some time, and have it set a flag that you check inside the loop. (e.g. from a signal handler). Specifically, alarm(2) may be a good choice. Record how many iterations you did in this burst of work.

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