I create a new cell in my jupyter notebook.
I type %%time in the first line of my new cell.
I type some codes in the second line.
I run this cell and get some information as follows
CPU times: user 2min 8s, sys: 14.5 s, total: 2min 22s
Wall time: 1min 29s
My question is what does these parameters mean?
CPU times, user, sys, total(I think that it means user+total), Wall time
If we run the code below in a cell:
%%time
from time import sleep
for i in range(3):
print(i, end=' ')
sleep(0.1)
The output is:
0 1 2
CPU times: user 5.69 ms, sys: 118 µs, total: 5.81 ms
Wall time: 304 ms
The wall time means that a clock hanging on a wall outside of the computer would measure 304 ms from the time the code was submitted to the CPU to the time when the process completed.
User time and sys time both refer to time taken by the CPU to actually work on the code. The CPU time dedicated to our code is only a fraction of the wall time as the CPU swaps its attention from our code to other processes that are running on the system.
User time is the amount of CPU time taken outside of the kernel. Sys time is the amount of time taken inside of the kernel. The total CPU time is user time + sys time. The differences between user and sys time is well explained in the post:
What do 'real', 'user' and 'sys' mean in the output of time(1)?
Related
I was analyzing mini-dump of one of my processes using Windbg. I used .time command to see the process time and I got the result as below. I was expecting (Process Uptime = Kernel Time + User Time), which was not the case. Does any body know why or my interpretation is wrong?
0:035> .time
Debug session time: Tue May 5 14:30:24.000 2020 (UTC - 7:00)
System Uptime: not available
Process Uptime: 3 days 5:29:22.000
Kernel time: 0 days 9:06:26.000
User time: 11 days 18:50:47.000
The kernel & user times match the CPU / Kernel & User Times displayed in Process Explorer under the Performance tab, and are likely related to the times returned by GetProcessTimes. They add up to the Total Time displayed in Process Explorer, or the CPU Time displayed in Task Manager for the same process.
This "CPU time" is the total time across all CPUs, and does not include time the process spent sleeping, waiting, or otherwise sitting idle. Because of that it can be either (a) smaller than the process "uptime" which is simply the time difference between the start and end times, in the case of mostly idle processes, or (b) larger than the process uptime in the case of heavy usage across multiple CPUs.
I was following the go blog here
I tried to profile my program but it looks a bit different. (Seems that go has moved from sampling to instrumentation?)
I wonder what these numbers mean
Especially showing nodes accounting for 2.59s, 92.5% of 2.8
What does total sample = 2.8s mean? The sample is drawn in an interval of 2.8 seconds?
Does it mean that only nodes that are running over 92.5% of sample
time are shown?
Also I wonder these numbers are generated. In the original go blog, the measure is how many times the function is detected in execution among all samples. However, we are dealing with seconds here. How does go profiling tool know how many seconds a function call takes.
Any help will be appreciated
Think of the graph as a graph of a resource, time. You'll start at the top with, for example, 10 seconds. Then you'll see that 5 seconds went to time.Sleep and 5 went to encoding/json. The particular divides in that time is represented by the arrows, so they show that 5 went to each part of the program. So now we have 3 nodes, the first node 10 seconds, time.Sleep 5 seconds, and encoding/json 5 seconds. Then those 5 seconds in encoding/json are broken down even further into the functions that took up most the time. The 0.01s (percentage) out of 0.02s (larger percentage) means that this function took 0.01s of processing time out of a total of 0.02s of the block of time (the arrow with the number) total by this particular call stack. The percentage represents the overall percentage of execution time this part took up from the whole pie. So you'll see that encoding/json string/encoder took 0.36 percent of the total execution time/resources of your program.
I am currently creating a program which identifies processes which are hung/out-of-control, and using an entire CPU core. The program then terminates them, so the CPU usage can be kept under control.
However, I have run into a problem: When I execute the 'tasklist' command on Windows, it outputs this:
Image Name: Blockland.exe
PID: 4880
Session Name: Console
Session#: 6
Mem Usage: 127,544 K
Status: Running
User Name: [removed]\[removed]
CPU Time: 0:00:22
Window Title: C:\HammerHost\Blockland\Blockland.exe
So I know that the line which says "CPU Time" is an indication of the total time, in seconds, used by the program ever since it started.
But let's suppose there are 4 CPU cores on the system. Does this mean that it used up 22 seconds of one core, and therefore used 5.5 seconds on the entire CPU in total? Or does this mean that the process used up 22 seconds on the entire CPU?
It's the total CPU time across all cores. So, if the task used 10 seconds on one core and then 15 seconds later on a different core it would report 25 seconds. If it used 5 seconds on all four cores simultaneously, it would report 20 seconds.
curious question on timing. When measuring wall clock time with any language such as python time.time() does the time include the CPU/System time.clock() time in it as well?
In Python, time.time() gives you the elapsed time (also known as wall time).That includes CPU time inasmuch as that's a subset of wall time but you cannot extract CPU time from time.time() itself.
For example, if your process runs for ten seconds but uses the CPU for only five of those seconds, the former includes the latter.
The last 3 lines of wget -i urls.txt:
FINISHED --2012-05-16 12:58:08--
Total wall clock time: 1h 56m 52s
Downloaded: 1069 files, 746M in 1h 52m 49s (113 KB/s)
There are two different times:
1h 56m 52s
1h 52m 49s
Why are they different? What do they stand for?
Wall clock time or real time is the human perception of passage of time. That will be the time as a human user, what we will experience. In this case wget might have took less than the real time to finish its job, but the real time is the sum of time the software took to finish its real job and the time it was waiting for resources like hard disk, network etc.
When you have wall clock time and a shorter time, the shorter time is usually user time and the missing time system time (time spend in the kernel) or time waiting for something like a file descriptor. (But I have not checked what's the case with wget). If you are curious start time wget http://some.url or look into /proc/<wget-pid>/stat while it's running (assuming you are running on linux).