I tried to create a test code by octave to evaluate the time efficiency on my Windows machine with an 8-core processor (parallelization on a single machine); starting with the simple code example provided in the documentation, as follows:
pkg load parallel
fun = #(x) x^2;
vector_x = 1:20000;
# Serial Format of the Program
tic()
for i=1:20000
vector_y(1,i)=vector_x(1,i)^2;
endfor
toc()
# Parallel Format of the Program
tic()
vector_y1 = pararrayfun(nproc, fun, vector_x);
toc()
To my surprise, the time required for a serial code is much faster than using the parallel function. The serial case ran in 0.0758219 s, the parallel one in 3.79864 s.
Would someone explain me if it is a parallel overhead or I should set up something in my Octave setting, or in which cases is the parallization really helpful?
TL;DR: open up your pool outside the timer and choose a more difficult operation.
There's two main issues. One is what Ander mentioned in his comment, starting up the parallel pool takes a second or two. You can open up it beforehand (in MATLAB you can do this via parpool) to speed that up. Alternatively, run a single parallel operation, thus opening the pool, and then redo the timing.
The second issue is the simplicity of your operation. Just squaring a number cannot go much faster than it already goes in serial. There's no point in passing data back-and-forth between workers for such a simple operation. Redo your test with a more expensive function, e.g. eig() as MATLAB does in their examples.
Parallellisation is thus useful if the runtime of your operations greatly outweighs the overhead of passing data to and from workers. Basically this means you either have a very large data set, which you need to perform the same operation on each item (e.g. taking the mean of every 1000 rows or so), or you have a few heavy, but independent, tasks to perform.
For a more in-depth explanation I can recommend this answer of mine and references therein.
Just as a sidenote, I'm surprised your serial for is that fast, given that you do not initialise your output vector. Preallocation is very important, as "growing" arrays in loops requires the creation of a new array and copying all previous content to it every iteration.
You also might want to consider not using i or j as variable names, as they denote the imaginary unit. It won't affect runtime much, but can result in very hard to debug errors. Simply use idx, ii, or a more descriptive variable name.
Related
I'm trying to make the code faster in Julia using parallelization. My code has nested serial for-loops and performs value function iteration. (as decribed in http://www.parallelecon.com/vfi/)
The following link shows the serial and parallelized version of the code I wrote:
https://github.com/minsuc/MyProject/blob/master/VFI_parallel.ipynb (You can find the functions defined in DefinitionPara.jl in the github page too.) Serial code is defined as main() and parallel code is defined as main_paral().
The third for-loop in main() is the step where I find the maximizer given (nCapital, nProductivity). As suggested in the official parallel documentation, I distribute the work over nCapital grid, which consists of many points.
When I do #time for the serial and the parallel code, I get
Serial: 0.001041 seconds
Parallel: 0.004515 seconds
My questions are as follows:
1) I added two workers and each of them works for 0.000714 seconds and 0.000640 seconds as you can see in the ipython notebook. The reason why parallel code is slower is due to the cost of overhead?
2) I increased the number of grid points by changing
vGridCapital = collect(0.5*capitalSteadyState:0.000001:1.5*capitalSteadyState)
Even though each worker does significant amount of work, serial code is way faster than the parallel code. When I added more workers, serial code is still faster. I think something is wrong but I haven't been able to figure out... Could it be related to the fact that I pass too many arguments in the parallelized function
final_shared(mValueFunctionNew, mPolicyFunction, pparams, vGridCapital, mOutput, expectedValueFunction)?
I will really appreciate your comments and suggestions!
If the amount of work is really small between synchronizations, the task sync overhead may be too long. Remember that a common OS timeslicing quantum is 10ms, and you are measuring in the 1ms range, so with a bit of load, 4ms latency for getting all work threads synced is perfectly reasonable.
In the case of all tasks accessing the same shared data structure, access locking overhead may well be the culprit, if the shared data structure is thread safe, even with longer parallel tasks.
In some cases, it may be possible to use non-thread-safe shared arrays for both input and output, but then it must be ensured that the workers don't clobber each other's results.
Depending on what exactly the work threads are doing, for example if they are outputting to the same array elements, it might be necessary to give each worker its own output array, and merge them together in the end, but that doesn't seem to be the case with your task.
There is a program that my matlab runs, since there are two gigantic nested for-loop, we expect this program to run more than 10 hours. We ask matlab to print out the loop number every time when it is looping.
Initially (the first 1 hour), wee see the loop number increment very fast in our screen; as time goes by, it goes slower and slower..... now (more than 20 consecutive hours of executing the same ".m" file and it still haven't finished yet), it is almost 20 times slower than it had been initially.
The ram usage initially was about 30%, right now after 20 hours of executing time, it is as shown below:
My computer spec is below.
What can I do to let matlab maintain its initially speed?
I can only guess, but my bet is that you have some array variables that have not been preallocated, and thus their size increases at each iteration of the for loop. As a result of this, Matlab has to reallocate memory in each iteration. Reallocating slows things down, and more so the larger those variables are, because Matlab needs to find an ever larger chunk of contiguous memory. This would explain why the program seems to run slower as time passes.
If this is indeed the cause, the solution would be to preallocate those variables. If their size is not known beforehand, you can make a guess and preallocate to an approximate size, thus avoiding at least some of the reallocating. Or, if your program is not memory-limited, maybe you can use an upper-bound on variable size when preallocating; then, after the loop, trim the arrays by removing unused entries.
Some general hints, if they don't help I suggest to add the code to the question.
Don't print to console, this output slows down execution and the output is kept in memory. Write a logfile instead if you need it. For a simple status, use waitbar
Verify you are preallocating all variables
Check which function calls depend on the loop index, for example increasing size of variables.
If any mex-functions are used, double check them for memory leaks. The standard procedure to do this: Call the function with random example data, don't store the output. If the memory usage increases there is a memory leak in the function.
Use the profiler. Profile your code for the first n iterations (where n matches about 10 minutes), generate a HTML report. Then let it run for about 2 hours and generate a report for n iterations again. Now compare both reports and see where the time is lost.
I'd like to point everybody to the following page in the MATLAB documentation: Strategies for Efficient Use of Memory. This page contains a collection of techniques and good practices that MATLAB users should be aware of.
The OP reminded me of it when saying that memory usage tends to increase over time. Indeed there's an issue with long running instances of MATLAB on win32 systems, where a memory leak exists and is exacerbated over time (this is described in the link too).
I'd like to add to Luis' answer the following piece of advice that a friend of mine once received during a correspondence with Yair Altman:
Allocating largest vars first helps by assigning the largest free contiguous blocks to the highest vars, but it is easily shown that in some cases this can actually be harmful, so it is not a general solution.
The only sure way to solve memory fragmentation is by restarting Matlab and even better to restart Windows.
More information on memory allocation performance can be found in the following undocumentedMatlab posts:
Preallocation performance
Allocation performance take 2
Forgive me if this is not actually a race condition; I'm not that familiar with the nomenclature.
The problem I'm having is that this code runs slower with OpenMP enabled. I think the loop should be plenty big enough (k=100,000), so I don't think overhead is the issue.
As I understand it, a race condition is occurring here because all the loops are trying to access the same v(i,j) values all the time, slowing down the code.
Would the best fix here be to create as many copies of the v() array as threads and have each thread access a different one?
I'm using intel compiler on 16 cores, and it runs just slightly slower than on a single core.
Thanks all!
!$OMP PARALLEL DO
Do 500, k=1,n
Do 10, i=-(b-1),b-1
Do 20, j=-(b-1),b-1
if (abs(i).le.l.and.abs(j).eq.d) then
cycle
endif
v(i,j)=.25*(v(i+1,j)+v(i-1,j)+v(i,j+1)+v(i,j-1))
if (k.eq.n-1) then
vtest(i,j,1)=v(i,j)
endif
if (k.eq.n) then
vtest(i,j,2)=v(i,j)
endif
20 continue
10 continue
500 continue
!$OMP END PARALLEL DO
You certainly have programmed a race condition though I'm not sure that that is the cause of your program's failure to execute more quickly. This line
v(i,j)=.25*(v(i+1,j)+v(i-1,j)+v(i,j+1)+v(i,j-1))
which will be executed by all threads for the same (set of) values for i and j is where the racing happens. Given that your program does nothing to coordinate reads and writes to the elements of v your program is, in practice, not deterministic as there is no way to know the order in which updates to v are made.
You should have observed this non-determinism on inspecting the results of the program, and have noticed that changing the number of threads has an impact on the results too. Then again, with a long-running stencil operation over an array the results may have converged to the same (or similar enough) values.
OpenMP gives you the tools to coordinate access to variables but it doesn't automatically implement them; there is definitely nothing going on under the hood to prevent quasi-simultaneous reads from and writes to v. So the explanation for the lack of performance improvement lies elsewhere. It may be down to the impact of multiple threads on cache at some level in your system's memory hierarchy. A nice, cache-friendly, run over every element of an array in memory order for a serial program becomes a blizzard of (as far as the cache is concerned) random accesses to memory requiring access to RAM at every go.
It's possible that the explanation lies elsewhere. If the time to execute the OpenMP version is slightly longer than the time to execute a serial version I suspect that the program is not, in fact, being executed in parallel. Failure to compile properly is a common (here on SO) cause of that.
How to fix this ?
Well the usual pattern of OpenMP across an array is to parallelise on one of the array indices. The statements
!$omp parallel do
do i=-(b-1),b-1
....
end do
ensure that each thread gets a different set of values for i which means that they write to different elements of v, removing (almost) the data race. As you've written the program each thread gets a different set of values of k but that's not used (much) in the inner loops.
In passing, testing
if (k==n-1) then
and
if (k==n) then
in every iteration looks like you are tying an anchor to your program, why not just
do k=1,n-2
and deal with the updates to vtest at the end of the loop.
You could separate the !$omp parallel do like this
!$omp parallel
do k=1,n-2
!$omp do
do i=-(b-1),b-1
(and make the corresponding changes at the end of the parallel loop and region). Now all threads execute the entire contents of the parallel region but each gets its own set of i values to use. I recommend that you add clauses to your directives to specify the accessibility (eg private or shared) of each variable; but this answer is getting a bit too long and I won't go into more detail on these. Or on using a schedule clause.
Finally, of course, even with the changes I've suggested your program will be non-deterministic because this statement
v(i,j)=.25*(v(i+1,j)+v(i-1,j)+v(i,j+1)+v(i,j-1))
will read neighbouring elements from v which are updated (at a time you have no control over) by another thread. To sort that out ... got to go back to work.
As far as I understand a sampling profiler works as follows: it interupts the program execution in regular intervals and reads out the call stack. It notes which part of the program is currently executing and increments a counter that represents this part of the program. In a post processing step: For each function of the program the ratio of the whole execution time is computed, for which the function is responsible for. This is done by looking at the counter C for this specific function and the total number of samples N:
ratio of the function = C / N
Finding the hotspots then is easy, as this are the parts of the program with a high ratio.
But how can this be done for a parallel program running on parallel hardware. As far as I know, when the program execution is interupted the executing parts of the program on ALL processors are determined. Due to that a function which is executed in parallel gets counted multiple times. Thus the number of samples C of this function can not be used for computing its share of the whole execution time anymore.
Is my thinking correct? Are there other ways how the hotspots of a parallel program can be identified - or is this just not possible using sampling?
You're on the right track.
Whether you need to sample all the threads depends on whether they are doing the same thing or different things.
It is not essential to sample them all at the same time.
You need to look at the threads that are actually working, not just idling.
Some points:
Sampling should be on wall-clock time, not CPU time, unless you want to be blind to needless I/O and other blocking calls.
You're not just interested in which functions are on the stack, but which lines of code, because they convey the purpose of the time being spent. It is more useful to look for a "hot purpose" than a "hot spot".
The cost of a function or line of code is just the fraction of samples it appears on. To appreciate that, suppose samples are taken every 10ms for a total of N samples. If the function or line of code could be made to disappear, then all the samples in which it is on the stack would also disappear, reducing N by that fraction. That's what speedup is.
In spite of the last point, in sampling, quality beats quantity. When the goal is to understand what opportunities you have for speedup, you get farther faster by manually scrutinizing 10-20 samples to understand the full reason why each moment in time is being spent. That's why I take samples manually. Knowing the amount of time with statistical precision is really far less important.
I can't emphasize enough the importance of finding and fixing more than one problem. Speed problems come in severals, and each one you fix has a multiplier effect on those done already. The ones you don't find end up being the limiting factor.
Programs that involve a lot of asynchronous inter-thread message-passing are more difficult, because it becomes harder to discern the full reason why a moment in time is being spent.
More on that.
the code I'm dealing with has loops like the following:
bistar = zeros(numdims,numcases);
parfor hh=1:nt
bistar = bistar + A(:,:,hh)*data(:,:,hh+1)' ;
end
for small nt (10).
After timing it, it is actually 100 times slower than using the regular loop!!! I know that parfor can do parallel sums, so I'm not sure why this isn't working.
I run
matlabpool
with the out-of-the-box configurations before running my code.
I'm relatively new to matlab, and just started to use the parallel features, so please don't assume that I'm am not doing something stupid.
Thanks!
PS: I'm running the code on a quad core so I would expect to see some improvements.
Making the partitioning and grouping the results (overhead in dividing the work and gathering results from the several threads/cores) is high for small values of nt. This is normal, you would not partition data for easy tasks that can be performed quickly in a simple loop.
Always perform something challenging inside the loop that is worth the partitioning overhead. Here is a nice introduction to parallel programming.
The threads come from a thread pool so the overhead of creating the threads should not be there. But in order to create the partial results n matrices from the bistar size must be created, all the partial results computed and then all these partial results have to be added (recombining). In a straight loop, this is with a high probability done in-place, no allocations take place.
The complete statement in the help (thanks for your link hereunder) is:
If the time to compute f, g, and h is
large, parfor will be significantly
faster than the corresponding for
statement, even if n is relatively
small.
So you see they mean exactly the same as what I mean, the overhead for small n values is only worth the effort if what you do in the loop is complex/time consuming enough.
Parforcomes with a bit of overhead. Thus, if nt is really small, and if the computation in the loop is done very quickly (like an addition), the parfor solution is slower. Furthermore, if you run parforon a quad-core, speed gain will be close to linear for 1-3 cores, but less if you use 4 cores, since the last core also needs to run system processes.
For example, if parfor comes with 100ms of overhead, and the computation in the loop takes 5ms, and if we assume that speed gain is linear up to 4 cores with a coefficient of 1 (i.e. using 4 cores makes the computation 4 times faster), nt needs to be about 30 for you to achieve a speed gain with parfor (150ms with for, 132ms with parfor). If you were to run only 10 iterations, parfor would be slower (50ms with for, 112ms with parfor).
You can calculate the overhead on your machine by comparing execution time with 1 worker vs 0 workers, and you can estimate speed gain by making a liner fit through the execution times with 1 to 4 workers. Then you'll know when it's useful to use parfor.
Besides the bad performance because of the communication overhead (see other answers), there is another reason not to use parfor in this case. Everything which is done within the parfor in this case uses built-in multithreading. Assuming all workers are running on the same PC there is no advantage because a single call already uses all cores of your processor.