I have a user account on a super computer where jobs are handled with slurm.
I would like to know the total amount of CPU hours that I have consumed on this super computer. I think that's an understandable question, because there is only a limited number of CPU hours available per project. I'm surprised that an answer is not easy to find.
I know that there are all these commands like sacct, sreport, sshare, etc... but it seems that there is no simple command that displays the used CPU hours.
Can someone help me out?
As others have commented, sacct should give you that information. You will need to look at the man page to get information for past jobs. You can specify a --starttime and --endtime to restrict your query to match your allocation as it ends/renews. The -l options should get you more information than you need so you can get a smaller set of options by specifying what you need with --format.
In your instance, the correct answer is to ask the administrators. You have been given an allocation of time to draw from. They likely have a system that will show you your balance and you can reconcile your balance against the output of sacct. Also, if the system you are using has different node types such as high memory, GPU, MIC, or old, they will likely charge you differently for those resources.
You can get an overview of the used CPU hours with the following:
sacct -SYYYY-mm-dd -u username -ojobid,start,end,alloccpu,cputime | column -t
You will could calculate the total accounting units (SBU in our system) multiplying CPUTime by AllocCPU which means multiplying the total (sysem+user) CPU time by the amount of CPU used.
An example:
JobID NodeList State Start End AllocCPUS CPUTime
------------ --------------- ---------- ------------------- ------------------- ---------- ----------
6328552 tcn[595-604] CANCELLED+ 2019-05-21T14:07:57 2019-05-23T16:48:15 240 506-17:12:00
6328552.bat+ tcn595 CANCELLED 2019-05-21T14:07:57 2019-05-23T16:48:16 24 50-16:07:36
6328552.0 tcn[595-604] FAILED 2019-05-21T14:10:37 2019-05-23T16:48:18 240 506-06:44:00
6332520 tcn[384,386,45+ COMPLETED 2019-05-23T16:06:04 2019-05-24T00:26:36 72 25-00:38:24
6332520.bat+ tcn384 COMPLETED 2019-05-23T16:06:04 2019-05-24T00:26:36 24 8-08:12:48
6332520.0 tcn[384,386,45+ COMPLETED 2019-05-23T16:06:09 2019-05-24T00:26:33 60 20-20:24:00
6332530 tcn[37,41,44,4+ FAILED 2019-05-23T17:11:31 2019-05-25T09:13:34 240 400-08:12:00
6332530.bat+ tcn37 FAILED 2019-05-23T17:11:31 2019-05-25T09:13:34 24 40-00:49:12
6332530.0 tcn[37,41,44,4+ CANCELLED+ 2019-05-23T17:11:35 2019-05-25T09:13:34 240 400-07:56:00
The fields are shown in the the manpage. They can be shown as -oOPTION (in lower case or in proper POSIX notation --format='Option,AnotherOption...' (a list is in the man).
So far so good. But there is a big caveat here:
What you see here is perfect to get an idea of what you have run or what to expect in terms of CPU / hours. But this will not necessarily reflect your real budget status, as in many cases each node / partition may have an extra parameter, the weight, which is a parameter set for accounting purposes and not part of SLURM. For instance,the GPU nodes may have a weight value of x3, which means that each GPU/hour is measured as 3 SBU instead of 1 for budgetary purposes. What I mean to say is that you can use sacct to gain insight on the CPU times but this will not necessarily reflect how much SBU credits you still have.
Related
We have a number of prometheus servers, each one monitors its own region (actually 2 per region), there are also thanos servers that can query multiple regions, and we also use alertmanager for the alerting.
Lately, we had an issue that few metrics stopped to report and we only discovered it when we needed the metrics.
We are trying to find out how to monitor the changes in the number of reported metrics in a scalable system that grow and shrink as required.
I'll be glad about your advice.
You can either count the number of timeseries in the head chunk (last 0-2 hours) or the rate at which you're ingesting samples:
prometheus_tsdb_head_series
or
rate(prometheus_tsdb_head_samples_appended_total[5m])
Then you compare said value with itself a few minutes/hours ago, e.g.
prometheus_tsdb_head_series / prometheus_tsdb_head_series offset 5m
and see whether it fits within an expected range (say 90-110%) and alert otherwise.
Or you can look at the metrics with the highest cardinality only:
topk(100, count({__name__=~".+"}) by (__name__))
Note however that this last expression can be quite costly to compute, so you may want to avoid it. Plus the comparison with 5 minutes ago will not be as straightforward:
label_replace(topk(100, count({__name__=~".+"}) by (__name__)), "metric", "$1", "__name__", "(.*)")
/
label_replace(count({__name__=~".+"} offset 5m) by (__name__), "metric", "$1", "__name__", "(.*)")
You need the label_replace there because the match for the division is done on labels other than __name__. Computing this latest expression takes ~10s on my Prometheus instance with 150k series, so it's anything but fast.
And finally, whichever approach you choose, you're likely to get a lot of false positives (whenever a large job is started or taken down), to the point that it's not going to be all that useful. I would personally not bother trying.
First I would like to let you know that I have recently asked this question already, however it was considered to be unclear, see Linux: CPU benchmark requiring longer time and different CPU utilization levels. This is now a new attempt to formulate the question using a different approach.
What I need: In my research, I look at the CPU utilization of a computer and analyze the CPU utilization pattern within a period of time. For example, a CPU utilization pattern within time period 0 to 10 has the following form:
time, % CPU used
0 , 21.1
1 , 17
2 , 18
3 , 41
4 , 42
5 , 60
6 , 62
7 , 62
8 , 61
9 , 50
10 , 49
I am interested in finding a simple representation for a given CPU utilization pattern. For the evaluation part, I need to create some CPU utilization patterns on my laptop which I will then record and analyse. These CPU utilization patterns that I need to create on my laptop should
be over a time period of more than 5 minutes, ideally of about 20 minutes.
the CPU utilization pattern should have "some kind of dynamic behavior" or in other words, the % CPU used should not be (almost) constant over time, but should vary over time.
My Question: How can I create such a utilization pattern? Of course, I could just run an arbitrary program on my laptop and I will obtain a desired CPU pattern. However, this solution is not ideal since a reader of my work has no means to repeat this experiment if wanted since he has not access to the program I used. Therefore it would be much more beneficial to use something instead of an arbitrary program on my laptop (in my previous post I was thinking about open source CPU benchmarks for example). Can anyone recommend me something?
Many thanks!
I suggest a moving average. Select a window size and use it to average over. You'll need to decide what type of patterns you want to identify since the wider the window, the more smoothing you get and the fewer "features" you'll see. And CPU activity is very bursty. For example, if you are trying to identify cache bottlenecks, you'll want a small window, probably in the 10ms to 100ms range. If instead you want to correlate to longer term features, such as energy or load, you'll want a larger window, perhaps 10sec to minutes.
It looks like you are using OS provided CPU usage and not hardware registers. This means that the OS is already doing some smoothing. It may also be doing estimation for some performance values. Try to find documentation on this if you are integrating over a smaller window. A word of warning: this level of information can be hard to find. You may have to do a lot of digging. Depending upon your familiarity with kernel code, it may be easier to look at the code.
I'm new to Unix, however, I have recently realized that very simple Unix commands can do very simple things to large data set very very quickly. My question is why are these Unix commands so fast relative to R?
Let's begin by assuming that the data is big, but not larger than the amount of RAM on your computer.
Computationally, I understand that Unix commands are likely faster than their R counterparts. However, I can't imagine that this would explain the entire time difference. After all basic R functions, like Unix commands, are written in low-level languages like C/C++.
I therefore suspect that the speed gains have to do with I/O. While I only have a basic understanding of how computers work, I do understand that to manipulate data it most first be read from disk (assuming the data is local). This is slow. However, regardless of whether you use R functions or Unix commands to manipulate data both most obtain the data from disk.
Therefore I suspect that how data is read from disk, if that even makes sense, is what is driving the time difference. Is that intuition correct?
Thanks!
UPDATE: Sorry for being vague. This was done on purpose, I was hoping to discuss this idea in general, rather than focus on a specific example.
Regardless, I'll generate an example of counting the number of rows
First I'll generate a big data set.
row = 1e7
col = 50
df<-matrix(rpois(row*col,1),row,col)
write.csv(df,"df.csv")
Doing it with Unix
time wc -l df.csv
real 0m12.261s
user 0m1.668s
sys 0m2.589s
Doing it with R
library(data.table)
system.time({ nrow(fread("df.csv")) })
...
user system elapsed
26.77 1.67 47.07
Notice that elapsed/real > user + system. This suggests that the CPU is waiting on the disk.
I suspected the slow speed of R has to do with reading the data in. It appears that I'm right:
system.time(fread("df.csv"))
user system elapsed
34.69 2.81 47.41
My question is how is the I/O different for Unix and R. Why?
I'm not sure what operations you're talking about, but in general, more complex processing systems like R use more complex internal data structures to represent the data being manipulated, and constructing these data structures can be a big bottleneck, significantly slower than the simple lines, words, and characters that Unix commands like grep tend to operate on.
Another factor (depending on how your scripts are set up) is whether you're processing the data one thing at a time, in "streaming mode", or reading everything into memory. Unix commands tend to be written to operate in pipelines, and to read a small piece of data (usually one line), process it, maybe write out a result, and move on to the next line. If, on the other hand, you read the entire data set into memory before processing it, then even if you do have enough RAM, allocating and organizing all the necessary memory can be very expensive.
[updated in response to your additional information]
Aha. So you were asking R to read the whole file into memory at once. That accounts for much of the difference. Let's talk about a few more things.
I/O. We can think about three ways of reading characters from a file, especially if the style of processing we're doing affects the way that's most convenient to do the reading.
Unbuffered small, random reads. We ask the operating system for 1 or a few characters at a time, and process them as we read them.
Unbuffered large, block-sized reads. We ask the operating for big chunks of memory -- usually of a size like 1k or 8k -- and chew on each chunk in memory before asking for the next chunk.
Buffered reads. Our programming language gives us a way of asking for as many characters as we want out of an intermediate buffer, and code that's built into the language ("library" code) automatically takes care of keeping that buffer full by reading large, block-sized chunks from the operating system.
Now, the important thing to know is that the operating system would much rather read big, block-sized chunks. So #1 can be drastically slower than 2 and 3. (I've seen factors of 10 or 100.) But no well-written programs use #1, so we can pretty much forget about it. As long as you're using 2 or 3, the I/O speed will be roughly the same. (In extreme cases, if you know what you're doing, you can get a little efficiency increase by using 2 instead of 3, if you can.)
Now let's talk about the way each program processes the data. wc has basically 5 steps:
Read characters one at a time. (I can assure you it uses method 3.)
For each character read, add one to the character count.
If the character read was a newline, add one to the line count.
If the character read was or wasn't a word-separator character, update the word count.
At the very end, print out the counts of lines, words, and/or characters, as requested.
So as you can see it's all I/O and very simple, character-based processing. (The only step that's at all complicated is 4. As an exercise, I once wrote a version of wc that contrived not to do all of steps 2, 3, and 4 inside the read loop if the user didn't ask for all the counts. My version did indeed run significantly faster if you invoked wc -c or wc -l. But obviously the code was significantly more complicated.)
In the case of R, on the other hand, things are quite a bit more complicated. First, you told it to read a CSV file. So as it reads, it has to find the newlines separating lines and the commas separating columns. That's roughly equivalent to the processing that wc has to do. But then, for each number that it finds, it has to convert it into an internal number that it can work with efficiently. For example, if somewhere in the CSV file occurs the sequence
...,12345,...
R is going to have to read those digits (as individual characters) and then do the equivalent of the math problem
1 * 10000 + 2 * 1000 + 3 * 100 + 4 * 10 + 5 * 1
to get the value 12345.
But there's more. You asked R to build a table. A table is a specific, highly regular data structure which orders all the data into rigid rows and columns for efficient lookup. To see how much work that can be, let's use a slightly far-fetched hypothetical real-world example.
Suppose you're a survey company and it's your job to ask people walking by on the street certain questions. But suppose that the questions are complicated enough that you need all the people seated in a classroom at once. (Suppose further that the people don't mind this inconvenience.)
But first you have to build that classroom. You're not sure how many people are going to walk by, so you build an ordinary classroom, with room for 5 rows of 6 desks for 30 people, and you haul in the desks, and the people start filing in, and after 30 people file in you notice there's a 31st, so what do you do? You could ask him to stand in the back, but you're kind of fixated on the rigid-rows-and-columns idea, so you ask the 31st person to wait, and you quickly call the builders and ask them to build a second 30-person classroom right next to the first, and now you can accept the 31st person and in fact 29 more for a total of 60, but then you notice a 61st person.
So you ask him to wait, and you call the builders back again, and you have them build two more classrooms, so now you've got a nice 2x2 grid of 30-person classrooms, but the people keep coming and soon enough the 121st person shows up and there's not enough room and you still haven't even started asking your survey questions yet.
So you call some fancier builders that know how to do steelwork and you have them build a big 5-story building next door with 50-person classrooms, 5 on each floor, for a total of 50 x 5 x 5 = 1,250 desks, and you have the first 120 people (who've been waiting patiently) file out of the old rooms into the new building, and now there's room for the 121st person and quite a few more behind him, and you hire some wreckers to demolish the old classrooms and recycle some of the materials, and the people keep coming and pretty soon there's 1,250 people in your new building waiting to be surveyed and the 1,251st has just showed up.
So you build a giant new skyscraper with 1,000 desks on each floor and 100 floors, and you demolish the old 5-story building, but the people keep coming, and how big did you say your big data set was? 1e7 x 50? So I don't think the 100-story building is going to be big enough, either. (And when you're all done with all this, the only "survey question" you're going to ask is "How many rows are there?")
Contrived as it may seem, this is actually not too bad an analogy for what R is having to do internally to build the table to store that data set in.
Meanwhile, Bob's discount survey company, who can only tell you how many people he surveyed and how many were men and women and in which age brackets, is down there on the streetcorner, and the people are filing by, and Bob is jotting down tally marks on his clipboards, and the people, once surveyed, are walking away and going about their business, and Bob isn't wasting time and money building any classrooms at all.
I don't know anything about R, but see if there's a way to construct an empty 1e7 x 50 matrix up front, and read the CSV file into it. You might find that significantly quicker. R will still have to do some building, but at least it won't have any false starts.
I am running a small Cassandra cluster on Google Compute Engine. From our CPU graphs (as reported by collectd), I notice that a nontrivial amount of processor time is spent in NICE. How can I find out what process is consuming this? I've tried just start top and staring at it for a while, but the NICE cpu usage is a bit spikey (most of the time, NICE is at 0%; only on occasion will it spike up to 30-40%) so "sit and wait" isn't very effective.
"Nice" generally refers to to the priority of a process. (More positive values are lower priority, more negative values are higher priority.) You can run ps -eo nice,pid,args | grep '^\s*[1-9]' to get a list of positive nice (low priority) commands.
On a CPU graph NICE time is time spent running processes with positive nice value (ie low priority). This means that it is consuming CPU, but will give up that CPU time for most other processes. Any USER CPU time for one of the processes listed in the above ps command will show up as NICE.
I made this function in Octave which plots fractals. Now, it takes a long time to plot all the points I've calculated. I've made my function as efficient as possible, the only way I think I can make it plot faster is by having my CPU completely focus itself on the function or telling it somehow it should focus on my plot.
Is there a way I can do this or is this really the limit?
To determine how much CPU is being consumed for your plot, run your plot, and in a separate window (assuming your on Linux/Unix), run the top command. (for windows, launch the task master and switch to the 'Processes' tab, click on CPU header to sort by CPU).
(The rollover description for Octave on the tag on your question says that Octave is a scripting language. I would expect it's calling gnuplot to create the plots. Look for this as the highest CPU consumer).
You should see that your Octave/gnuplot cmd is near the top of the list, and for top there is a column labeled %CPU (or similar). This will show you how much CPU that process is consuming.
I would expect to see that process is consuming 95% or more CPU. If you see that is a significantly lower number, then you need to check the processes below that, are they consuming the remaining CPU (some sort of Virus scan (on a PC), or DB or Server?)? If a competing program is the problem, then you'll have to decide if you can wait til it/they are finished, OR that you can kill them and restart later. (For lunix, use kill -15 pid or kill -11 pid. Only use kill -9 pid as a last resort. Search here for articles on correct order for trying to kill -$n)
If there are no competing processes AND it octave/gnuplot is using less than 95%, then you'll have to find alternate tools to see what is holding up the process. (This is unlikely, it's possible some part of your overall plotting process is either Disk I/O or Network I/O bound).
So, it depends on the timescale you're currently experiencing versus the time you "want" to experience.
Does your system have multiple CPUs? Then you'll need to study the octave/gnuplot documentation to see if it supports a switch to indicate "use $n available CPUs for processing". (Or find a plotting program that does support using $n multiple CPUs).
Realistically, if your process now takes 10 mins, and you can, by eliminating competing processes, go from 60% to 90%, that is a %50 increase in CPU, but will only reduce it to 5 mins (not certain, maybe less, math is not my strong point ;-)). Being able to divide the task over 5-10-?? CPUs will be the most certain path to faster turn-around times.
So, to go further with this, you'll need to edit your question with some data points. How long is your plot taking? How big is the file it's processing. Is there something especially math intensive for the plotting you're doing? Could a pre-processed data file speed up the calcs? Also, if the results of top don't show gnuplot running at 99% CPU, then edit your posting to show the top output that will help us understand your problem. (Paste in your top output, select it with your mouse, and then use the formatting tool {} at the top of the input box to keep the formatting and avoid having the output wrap in your posting).
IHTH.
P.S. Note the # of followers for each of the tags you've assigned to your question by rolling over. You might get more useful "eyes" on your question by including a tag for the OS you're using, and a tag related to performance measurement/testing (Go to the tags tab and type in various terms to see how many followers you're getting. One bit of S.O. etiquette is to only specify 1 programming language (if appropriate) and that may apply to OS's too.)