The cputime (); returns the CPU time used by an Octave session. Here, a way is stated to measure the processing time of a code.
t = cputime;
...%your code
...%computing
...%when you are done
printf('Total cpu time: %f seconds\n', cputime-t);
In octave documentation we can use the etime(); function file, it returns the difference (in seconds) between two timestamps from the clock, like this:
t0 = clock ();
# many computations later...
elapsed_time = etime (clock (), t0);
What's the difference between the above two ways? The etime uses the hour of clock of my system? And what's a good way to collect the processing time of an code?
Your first code snippet with cputime measures the time your code was execuded on a CPU. The second snippet measures the wall clock time (btw: use tic and toc to measure this). The wall clock includes time where the CPU was executing some other threads or waiting for IO.
It is up to you what you want to measure. I normally use wall clock with tic/toc if I want to benchmark my code.
On single threaded apps the wall clock time is greater than or equal the cputime (because it includes the time the CPU was also processing some other threads). On multithreaded apps with multiple cores cputime can be greater than the wall clock time.
Related
I would like to know if it is possible to modify easily perf linux with stat module to create an interval of cycles (or instructions by cycle) instead of an interval of time ? The goal is to optimize the precision of the counters got by interval. The time unit is not accurate enough.
I have a friend which submits this idea but I looked the source code a little and I understood (maybe I have wrong) that we :
create a condition for a time calculated with the rdtsc library (some clock_gettime)
create a "wait" in the perf processus
launch the program to test
test if we respect the time condition : we continue or we break the wait function with a save on the mapped register system information in perf (and call the wait function if it is not over)
I would like this result :
cycles counts unit events
10000 25000 instructions
10000 450 branch-misses
20000 21000 instructions
20000 850 branch-misses
Unfortunately, I'm seeing a big problem if I want to use the result of a counter like a condition I have not yet. Or should I get all the time this or these counter(s) which define my "interval condition" ? I also saw that for a time interval, we shouldn't get counters with a frequency lower than 100ms because it generates overhead. If I get some counters every 10000 cycles, would I have the same problems ? I don't know where is this overhead (calls system ?).
How to convert the time difference given by the GPU timer while rendering into the equivalent CPU timing?
Let's say,
glGetQueryObjectuiv(query, GL_QUERY_RESULT, &elapsed_time) - will return the elapsed time for that query and I presume this elapsed time will correspond to GPU frequency.
How to get the corresponding CPU time which is equivalent to the GPU elapsed time?
It's a timer query - it returns a time in nanoseconds. Time doesn't change with frequency ...
I'm working on a code in which I have to perform a vector-matrix multiplication on a chunk of data, copying the results back to CPU and then start multiplying another chunk. I perform the vector to matrix multiplication using cublas library (following code).
clock_t a,b;
a = clock();
for(int i=0;i<n;i++)
{
cublasSgemv(handle,CUBLAS_OP_T,m,k,&alpha, dev_b1+((i+1)*m), m, dev_b1+(i*m),1, &beta,out,1);
out+=(n-(i+1));
cudaMemcpy(b3,dev_b3, sizeof(float)*(cor_size), cudaMemcpyDeviceToHost);
}
b = clock();
cout<<"Running time is: "<<(double)(b-a)/clocks_per_sec;
I have to measure the running time of this for loop. I read something about CudaEvent but in my case, I want to measure the time of total loop not a kernel so I used clock function. I am wondering is this a correct way to measure the time for this chunk of code or there are more accurate ways to do that?
I know that for measuring elapsed time we have to repeat running the code multiple times and take the average of elapsed times of all runs, so another question is that is there any trade-off for the number of times that running code should be repeated?
Thanks
cudaMemcpy synchronizes host and device, so a CPU timer such as clock_t should give results that are identical with those produced by a CUDA timer, making the necessary allowances for the granularity/resolution of clock_t.
As regards the accuracy of the measurements is concerned, from what I have seen, the first iteration timings could be disregarded in the calculations. Subsequent timing measurements should yield numbers depending on factors such as load imbalance in the algorithm being run, which might decide on whether we get the same numbers at every iteration. I would reckon that that would not be an issue here, with Sgemm.
You can still use CUDA events to measure the entire loop runtime, by recording two events (one before starting the loop, one after the end, i.e. in the positions where you are currently using clock()), synchronizing on the second event and then getting the elapsed time using cudaEventElapsedTime(). This should have the advantage of being more accurate than clock().
Many scientific computing languages make a distinction between absolute time (wall clock) and CPU time (processor cycles). For example, in Matlab we have:
>> tic; pause(1); toc
Elapsed time is 1.009068 seconds.
>> start = cputime; pause(1); elapsed = cputime - start
elapsed =
0
and in Mathematica we have:
>>In[1]:= AbsoluteTiming[Pause[1]]
>>Out[1]= {1.0010572, Null}
>>In[2]:= Timing[Pause[1]]
>>Out[2]= {0., Null}
This distinction is useful when benchmarking code run on computation servers, where there may be high variance in the absolute timing results depending on what other processes are running concurrently.
The Julia standard library provides support for timing of expressions through tic(), toc(), #time and a few other functions/macros all based on time_ns(), a function that measures absolute time.
>>julia> #time sleep(1)
elapsed time: 1.017056895 seconds (135788 bytes allocated)
My question: Is there a simple way to get the elapsed CPU time for an expression evaluation in Julia?
(Side note: doing some digging, it appears that Julia timing is based on the uv_hrtime() function from libuv. It seems to me that using uv_getrusage from the same library might give a way to access elapsed CPU time in Julia, but I'm no expert. Has anybody tried using anything like this?)
I couldn't find any existing solutions, so I've put together a package with some simple CPU timing functionality here: https://github.com/schmrlng/CPUTime.jl. The package is completely untested on parallel code and may have other bugs, but if anybody else would like to try it out calling
>> Pkg.clone("https://github.com/schmrlng/CPUTime.jl.git")
from the julia> prompt should install the package.
Julia does have the commands tic() and toc() which work just like tic and toc in Matlab:
julia> tic(); 7^1000000000; toc()
elapsed time: 0.046563597 seconds
0.046563597
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.