Feign Client + Eureka POST请求正文(Feign Client + Eureka POST request body)

 程序启动时，在main（）中，使用任何系统计时器记录时间。 当程序在main（）的底部结束时，使用相同的系统计时器记录时间。 取时间2和时间1之间的差异。 你去！ 
 
 您可以使用不同的系统计时器，其中一些计时器的分辨率高于其他计时器。 我建议您在SO网站上搜索“系统计时器”，而不是在这里讨论。 如果您只想要任何系统计时器，gettimeofday（）可以在Linux系统上运行，但它已被更新，更高精度的功能所取代。 实际上，gettimeofday（）只测量以微秒为单位的时间，这应该足以满足您的需求。 
 
 如果您无法获得具有足够好分辨率的计时器，请考虑多次循环运行程序，计算循环执行时间，并将测量时间除以循环迭代次数。 
 
 编辑： 
 
 系统计时器可用于测量总应用程序性能，包括GPU计算期间使用的时间。 请注意，以这种方式使用系统计时器仅适用于实时或挂钟时间，而不是处理时间。 基于挂钟时间的测量必须包括等待GPU操作完成所花费的时间。 
 
 如果要测量GPU内核所用的时间，可以选择几个选项。 首先，您可以使用Compute Visual Profiler收集各种分析信息，虽然我不确定它是否报告时间，但它必须能够（这是一个基本的分析功能）。 其他分析器 - 脑海中浮现 - 提供对CUDA内核的支持。 
 
 另一种选择是使用CUDA事件来记录时间。 请参阅CUDA 4.0编程指南，其中讨论了使用CUDA事件来测量时间。 
 
 另一种选择是使用围绕GPU内核调用的系统计时器。 请注意，鉴于内核调用返回的异步性质，您还需要使用主机端GPU同步调用（例如cudaThreadSynchronize（））来跟随内核调用，以使此方法适用。 如果你选择这个选项，我强烈建议在循环中调用内核，在结束时定时循环+一次同步（因为在不在不同流中执行的内核调用之间发生同步，循环内部不需要cudaThreadSynchronize（）），并除以迭代次数。 

When your program starts, in main(), use any system timer to record the time. When your program ends at the bottom of main(), use the same system timer to record the time. Take the difference between time2 and time1. There you go!
 
There are different system timers you can use, some with higher resolution than others. Rather than discuss those here, I'd suggest you search for "system timer" on the SO site. If you just want any system timer, gettimeofday() works on Linux systems, but it has been superseded by newer, higher-precision functions. As it is, gettimeofday() only measures time in microseconds, which should be sufficient for your needs.
 
If you can't get a timer with good enough resolution, consider running your program in a loop many times, timing the execution of the loop, and dividing the measured time by the number of loop iterations.
 
EDIT:
 
System timers can be used to measure total application performance, including time used during the GPU calculation. Note that using system timers in this way applies only to real, or wall-clock, time, rather than process time. Measurements based on the wall-clock time must include time spent waiting for GPU operations to complete.
 
If you want to measure the time taken by a GPU kernel, you have a few options. First, you can use the Compute Visual Profiler to collect a variety of profiling information, and although I'm not sure that it reports time, it must be able to (that's a basic profiling function). Other profilers - PAPI comes to mind - offer support for CUDA kernels.
 
Another option is to use CUDA events to record times. Please refer to the CUDA 4.0 Programming Guide where it discusses using CUDA events to measure time.
 
Yet another option is to use system timers wrapped around GPU kernel invocations. Note that, given the asynchronous nature of kernel invocation returns, you will also need to follow the kernel invocation with a host-side GPU synchronization call such as cudaThreadSynchronize() for this method to be applicable. If you go with this option, I highly recommend calling the kernel in a loop, timing the loop + one synchronization at the end (since synchronization occurs between kernel calls not executing in different streams, cudaThreadSynchronize() is not needed inside the loop), and dividing by the number of iterations.