Random slowdowns in node.js execution - node.js

I have an optimization algorithm written in node.js that uses cpu time (measured with performance.now()) as a heuristic.
However, I noticed that occasionally some trivial lines of code would cost much more than usual.
So I wrote a test program:
const timings = [];
while (true) {
const start = performance.now();
// can add any trivial line of code here, or just nothing
const end = performance.now();
const dur = end - start;
if (dur > 1) {
throw [
"dur > 1",
{
start,
end,
dur,
timings,
avg: _.mean(timings),
max: _.max(timings),
min: _.min(timings),
last: timings.slice(-10),
},
];
}
timings.push(dur);
}
The measurements showed an average of 0.00003ms and a peak >1ms (with the second highest <1ms but same order of magnitude).
The possible reasons I can think of are:
the average timing isn't the actual time for executing the code (some compiler optimization)
performance.now isn't accurate somehow
cpu scheduling related - process wasn't running normally but still counted in performance.now
occasionally node is doing something extra behind the scenes (GC etc)
something happening on the hardware/os level - caching / page faults etc
Is any of these a likely reason, or is it something else?
Whichever the cause is, is there a way to make a more accurate measurement for the algorithm to use?
The outliers are current causing the algorithm to misbehave & without knowing how to resolve this issue the best option is to use the moving average cost as a heuristic but has its downsides.
Thanks in advance!
------- Edit
I appreciate how performance.now() will never be accurate, but was a bit surprised that it could span 3-4 orders of magnitude (as opposed to 2 orders of magnitude or ideally 1.)
Would anyone have any idea/pointers as to how performance.now() works and thus what's likely the major contributor to the error range?
It'd be nice to know if the cause is due to something node/v8 doesn't have control over (hardware/os level) vs something it does have control over (a node bug/options/gc related), so I can decide whether there's a way to reduce the error range before considering other tradeoffs with using an alternative heuristic.
------- Edit 2
Thanks to #jfriend00 I now realize performance.now() doesn't measure the actual CPU time the node process executed, but just the time since when the process started.
The question now is
if there's an existing way to get actual CPU time
is this a feature request for node/v8
unless the node process doesn't have enough information from the OS to provide this

You're unlikely to be able to accurately measure the time for one trivial line of code. In fact, the overhead in executing performance.now() is probably many times higher than the time to execute one trivial line of code. You have to be careful that what you're measuring takes substantially longer to execute than the uncertainty or overhead of the measurement itself. Measuring very small executions times is not going to be an accurate endeavor.
1,3 and 5 in your list are also all possibilities. You aren't guaranteed that your code gets a dedicated CPU core that is never interrupted to service some other thread in the system. In my Windows system, even when my nodejs is the only "app" running, there are hundreds of other threads devoted to various OS services that may or may not request some time to run while my nodejs app is running and eventually get some time slice of the CPU core my nodejs app was using.
And, as best I know, performance.now() is just getting a high resolution timer from the OS that's relative to some epoch time. It has no idea when your thread is and isn't running on a CPU core and wouldn't have any way to adjust for that. It just gets a high resolution timestamp which you can compare to some other high resolution timestamp. The time elapsed is not CPU time for your thread. It's just clock time elapsed.

Is any of these a likely reason, or is it something else?
Yes, they all sound likely.
is there a way to make a more accurate measurement for the algorithm to use?
No, sub-millisecond time measurements are generally not reliable, and almost never a good idea. (Doesn't matter whether a timing API promises micro/nanosecond precision or whatever; chances are that (1) it doesn't hold up in practice, and (2) trying to rely on it creates more problems than it solves. You've just found an example of that.)
Even measuring milliseconds is fraught with peril. I once investigated a case of surprising performance, where it turned out that on that particular combination of hardware and OS, after 16ms of full load the CPU ~tripled its clock rate, which of course had nothing to do with the code that appeared to behave weirdly.
EDIT to reply to edited question:
The question now is
if there's an existing way to get actual CPU time
No.
is this a feature request for node/v8
No, because...
unless the node process doesn't have enough information from the OS to provide this
...yes.

Related

Measuring a feature's share of a web service's execution time

I have a piece of code that includes a specific feature that I can turn on and off. I want to know the execution time of the feature.
I need to measure this externally, i.e. by simply measuring execution time with a load test tool. Assume that I cannot track the feature's execution time internally.
Now, I execute two runs (on/off) and simply assume that the difference between the resulting execution time is my feature's execution time.
I know that it is not entirely correct to do this as I'm looking at two separate runs that may be influenced by networking, programmatic overhead, or the gravitational pull of the moon. Still, I hope I can assume that the result will still be viable if I have a sufficiently large number of requests.
Now for the real question. I do the above using the average response time. Which is not perfect, but more or less ok.
My question is, what if I now use a percentile (say, 95th) instead?
Would my imperfect subtract-A-from-B approach become significantly more imperfect when using percentiles?
I would stick to the percentiles as the "average" approach can mask the problem, for example if you have very low response times during the initial phase of the test when the load is low and very high response times during the main phase of test when the load is immense the arithmetic mean approach will give you okayish values while with the percentiles you will get the information that the response time for 95% of requests was X or higher.
More information: Understanding Your Reports: Part 3 - Key Statistics Performance Testers Need to Understand

Explain surprising number of timers and finding their origin

I have a CPU leak somewhere and am trying to find out the origin (the RAM increases a bit too but not as fast).
I have collected some data through dotnet-counters and found out that the number of timers in the given process keeps increasing. For instance, when 46 % of the CPU was used, it was reporting around 450 timers (see following data). Note that this count only increases, slowly but surely like my CPU usage. And also note that it's pretty much at a time where it was idle, not having customers online.
This leads to 2 questions:
1) Is it normal? Should I continue on this track? I find it odd to have this many timers.
2) In my own code, I never user the Timer class, I use the Stopwatch class once but that's it.
So what would be the best way to find the class/library that uses those timers?
1) There was indeed something fishy and it is definitely not normal.
I had a class spawning hundreds of threads and keeping them alive to listen to incoming calls.
Long story short, that many timers is not something normal.
2) The Timer was coming from somewhere deep in a library. I couldn't find an easy way to find them.
Best option is to use dotnet-trace collect. https://www.speedscope.app/ helped me a lot and it is possible to generate de traces for speedscope directly from dotnet-trace.

Estimating WCET of a task on Linux

I want to approximate the Worst Case Execution Time (WCET) for a set of tasks on linux. Most professional tools are either expensive (1000s $), or don't support my processor architecture.
Since, I don't need a tight bound, my line of thought is that I :
disable frequency scaling
disbale unnecesary background services and tasks
set the program affinity to run on a specified core
run the program for 50,000 times with various inputs
Profiling it and storing the total number of cycles it had completed to
execute.
Given the largest clock cycle count and knowing the core frequency, I can get an estimate
Is this is a sound Practical approach?
Secondly, to account for interference from other tasks, I will run the whole task set (40) tasks in parallel with each randomly assigned a core and do the same thing for 50,000 times.
Once I get the estimate, a 10% safe margin will be added to account for unforseeble interference and untested path. This 10% margin has been suggested in the paper "Approximation of Worst Case Execution time in Preepmtive Multitasking Systems" by Corti, Brega and Gross
Some comments:
1) Even attempting to compute worst case bounds in this way means making assumptions that there aren't uncommon inputs that cause tasks to take much more or even much less time. An extreme example would be a bug that causes one of the tasks to go into an infinite loop, or that causes the whole thing to deadlock. You need something like a code review to establish that the time taken will always be pretty much the same, regardless of input.
2) It is possible that the input data does influence the time taken to some extent. Even if this isn't apparent to you, it could happen because of the details of the implementation of some library function that you call. So you need to run your tests on a representative selection of real life data.
3) When you have got your 50K test results, I would draw some sort of probability plot - see e.g. http://www.itl.nist.gov/div898/handbook/eda/section3/normprpl.htm and links off it. I would be looking for isolated points that show that in a few cases some runs were suspiciously slow or suspiciously fast, because the code review from (1) said there shouldn't be runs like this. I would also want to check that adding 10% to the maximum seen takes me a good distance away from the points I have plotted. You could also plot time taken against different parameters from the input data to check that there wasn't any pattern there.
4) If you want to try a very sophisticated approach, you could try fitting a statistical distribution to the values you have found - see e.g. https://en.wikipedia.org/wiki/Generalized_Pareto_distribution. But plotting the data and looking at it is probably the most important thing to do.

Performance issue while using Parallel.foreach() with MaximumDegreeOfParallelism set as ProcessorCount

I wanted to process records from a database concurrently and within minimum time. So I thought of using parallel.foreach() loop to process the records with the value of MaximumDegreeOfParallelism set as ProcessorCount.
ParallelOptions po = new ParallelOptions
{
};
po.MaxDegreeOfParallelism = Environment.ProcessorCount;
Parallel.ForEach(listUsers, po, (user) =>
{
//Parallel processing
ProcessEachUser(user);
});
But to my surprise, the CPU utilization was not even close to 20%. When I dig into the issue and read the MSDN article on this(http://msdn.microsoft.com/en-us/library/system.threading.tasks.paralleloptions.maxdegreeofparallelism(v=vs.110).aspx), I tried using a specific value of MaximumDegreeOfParallelism as -1. As said in the article thet this value removes the limit on the number of concurrently running processes, the performance of my program improved to a high extent.
But that also doesn't met my requirement for the maximum time taken to process all the records in the database. So I further analyzed it more and found that there are two terms as MinThreads and MaxThreads in the threadpool. By default the values of Min Thread and MaxThread are 10 and 1000 respectively. And on start only 10 threads are created and this number keeps on increasing to a max of 1000 with every new user unless a previous thread has finished its execution.
So I set the initial value of MinThread to 900 in place of 10 using
System.Threading.ThreadPool.SetMinThreads(100, 100);
so that just from the start only minimum of 900 threads are created and thought that it will improve the performance significantly. This did create 900 threads, but it also increased the number of failure on processing each user very much. So I did not achieve much using this logic. So I changed the value of MinThreads to 100 only and found that the performance was much better now.
But I wanted to improve more as my requirement of time boundation was still not met as it was still exceeding the time limit to process all the records. As you may think I was using all the best possible things to get the maximum performance in parallel processing, I was also thinking the same.
But to meet the time limit I thought of giving a shot in the dark. Now I created two different executable files(Slaves) in place of only one and assigned them each half of the users from DB. Both the executable were doing the same thing and were executing concurrently. I created another Master program to start these two Slaves at the same time.
To my surprise, it reduced the time taken to process all the records nearly to the half.
Now my question is as simple as that I do not understand the logic behind Master Slave thing giving better performance compared to a single EXE with all the logic same in both the Slaves and the previous EXE. So I would highly appreciate if someone will explain his in detail.
But to my surprise, the CPU utilization was not even close to 20%.
…
It uses the Http Requests to some Web API's hosted in other networks.
This means that CPU utilization is entirely the wrong thing to look at. When using the network, it's your network connection that's going to be the limiting factor, or possibly some network-related limit, certainly not CPU.
Now I created two different executable files … To my surprise, it reduced the time taken to process all the records nearly to the half.
This points to an artificial, per process limit, most likely ServicePointManager.DefaultConnectionLimit. Try setting it to a larger value than the default at the start of your program and see if it helps.

Limiting work in progress of parallel operations of a streamed resource

I've found myself recently using the SemaphoreSlim class to limit the work in progress of a parallelisable operation on a (large) streamed resource:
// The below code is an example of the structure of the code, there are some
// omissions around handling of tasks that do not run to completion that should be in production code
SemaphoreSlim semaphore = new SemaphoreSlim(Environment.ProcessorCount * someMagicNumber);
foreach (var result in StreamResults())
{
semaphore.Wait();
var task = DoWorkAsync(result).ContinueWith(t => semaphore.Release());
...
}
This is to avoid bringing too many results into memory and the program being unable to cope (generally evidenced via an OutOfMemoryException). Though the code works and is reasonably performant, it still feels ungainly. Notably the someMagicNumber multiplier, which although tuned via profiling, may not be as optimal as it could be and isn't resilient to changes to the implementation of DoWorkAsync.
In the same way that thread pooling can overcome the obstacle of scheduling many things for execution, I would like something that can overcome the obstacle of scheduling many things to be loaded into memory based on the resources that are available.
Since it is deterministically impossible to decide whether an OutOfMemoryException will occur, I appreciate that what I'm looking for may only be achievable via statistical means or even not at all, but I hope that I'm missing something.
Here I'd say that you're probably overthinking this problem. The consequences for overshooting are rather high (the program crashes). The consequences for being too low are that the program might be slowed down. As long as you still have some buffer beyond a minimum value, further increases to the buffer will generally have little to no effect, unless the processing time of that task in the pipe is extraordinary volatile.
If your buffer is constantly filling up it generally means that the task before it in the pipe executes quite a bit quicker than the task that follows it, so even without a fairly small buffer it is likely to always ensure the task following it has some work. The buffer size needed to get 90% of the benefits of a buffer is usually going to be quite small (a few dozen items maybe) whereas the side needed to get an OOM error are like 6+ orders of magnate higher. As long as you're somewhere in-between those two numbers (and that's a pretty big range to land in) you'll be just fine.
Just run your static tests, pick a static number, maybe add a few percent extra for "just in case" and you should be good. At most, I'd move some of the magic numbers to a config file so that they can be altered without a recompile in the event that the input data or the machine specs change radically.

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