I'm working on writing a larger procedure in Maple and I'm trying to figure out why it running slow. I was thinking that if I could test certain subfunctions in the procedure for their execution time I could determine a better way to code it to make it run faster.
Is there a good way to measure the execution time or cpu use for a Maple function?
To see the resource usage for an entire procedure, run it wrapped in CodeTools:-Usage. To see a line-by-line analysis of resource usage, use CodeTools:-Profiling:-Profile.
Related
I am attempting to generate a large workbook based report with 3 supporting worksheets of 100,12000 and 12000 rows and a final output sheet all formula based that ends up representing about 120 entities at 100 rows a piece. I generate a template range and copy and paste it replacing the entity ID cell after pasting each new range. It is working fine but I noticed that memory usage in the IIS Express process is approx 500mb and it is taking 100% processor usage as well.
Are there any guidelines for generating workbooks in this manner?
At least in terms of memory utilization, it would help to have some comparison, maybe against Excel, in how much memory is utilized to simply have the resultant workbook opened. For instance, if you were to open the final report in both Excel and the "SpreadsheetGear 2012 for Windows" application (available in the SpreadsheetGear folder under the Start menu), what does the Task Manager measure for each of these applications in terms of memory consumption? This may provide some insight as to whether the memory utilization you are seeing in the actual report-building process is unusually high (is there a lot of extra overhead for your routine?), or just typical given the size of the workbook you are generating.
In terms of CPU utilization, this one is a bit more difficult to pinpoint and is certainly dependent on your hardware as well as implementation details in your code. Running a VS Profiler against your routine certainly would be interesting to look into, if you have this tool available to you. Generally speaking, the CPU time could potentially be broken up into a couple broad categories—CPU cycles used to "build" your workbook and CPU cycles to "calculate" it. It could be helpful to better determine which of these is dominating the CPU. One way to do this might be to, if possible, ensure that calculations don't occur until you are finished actually generating the workbook. In fact, avoiding any unnecessary calculations could potentially speed things up...it depends on the workbook, though. You could avoid calculations by setting IWorkbookSet.Calculation to Manual mode and not calling any of the IWorkbook’s "Calculate" methods (Calculate/CalculateFull/CalculateFullRebuild) until you are fished up with this process. If you don't have access to a Profiler too, maybe set some timers, Console.WriteLines and monitor the Task Manager to see how your CPU fluctuates during different parts of your routine. With any luck you might be able to better isolate what part of the routine is taking the most amount of time.
I want to improve the running time of some code.
In order to that I first time the running time of all relevant code, using code like this:
before:= rdtsc;
myobject.run;
after:= rdtsc;
Then I zoom in and time a relevant part, like so:
procedure myobject.part;
begin
StartTime:= rdtsc;
...
EndTime:= rdtsc;
inc(TotalTime, (EndTime- StartTime));
end;
I have some code to copy paste the timings into Excel, a typical outcome would look like:
(the 89.8% and 10.2% adding up to 100% is a coincidence and has nothing to do with the data or the question)
(when the data shows 1 it means 0 to avoid divide by zero errors)
Note the difference between run A and run B.
I have not changed anything yet so run A and B should give the same running time.
Further note that I know that on both runs procedure part was invoked exactly the same number of times (the data is the same and the algorithm is deterministic).
The running time of procedure part is very short (it is just called many times).
If there was some way to block out other processes during these short bursts of runtime (less than 700 CPU cycles) my timings would be much more accurate.
How do I get these timings to be more reliable?
Is there a way to monopolize the CPU to only run my task when timing and nothing else?
Note that I'm not looking for obvious answers like:
- Close other running programs
- Disable the virusscanner etc...
I've tagged the question Delphi because I'm using Delphi right now (and there may be some Delphi specific option to achieve this result).
I've also tagged it language-agnostic because there may be some more general way.
Update
Because I'm using the CPU instruction RDTSC I'm not affected by CPU throttling. If the CPU slows down, the number of cycles stays the same.
Update2
I have 2 answers, but neither answers the question...
The question is how do I prevent these changes in running time?
Do I have to run the code 20x and always compare the lowest running time out of the 20 runs?
Or to I set my program priority to realtime?
Or is there some other trick to use so my code sample does not get interrupted?
To want to improve the running time of some code.
In order to that I first time the running time of all relevant code, ...
OK, I'm a bit of a stuck record on this subject, but lots of people think that to improve running time requires first measuring it accurately.
Not So.
Improving running time requires finding out what's taking a large fraction of time (the exact fraction does not matter) and doing it differently or maybe not at all.
What it's doing is often not revealed by timing individual routines.
Here's the method I use,
and here's a very amateur video of it.
The problem with profiling your code like that, by sticking special statements into it, is that those special statements themselves take time to run. And since the things taking the most time are likely to be things happening in tight loops, the more they run, the more they distort your timings. What you need for good information is something that will observe your program from outside, without modifying the executing code.
In other words, you need a sampling profiler. And there just happens to be a very good one for Delphi available for free, by the rather descriptive name of Sampling Profiler. It runs your program and watches what it's doing, then correlates that against the map file (make sure to set up your project options to generate a Detailed map file) to give you an intelligible readout on what your program is spending its time on.
And if you want to narrow things down, you can use OutputDebugString to output profiling commands to make it only pay attention to specific parts of your code. It's got instructions in the help file.
I've used a lot of different methods, and this is the most useful way I've found to figure out what Delphi programs are spending their time on. And it's free. Give it a try.
When I use YJP to do cpu-tracing profile on our own product, it is really slow.
The product runs in a 16 core machine with 8GB heap, and I use grinder to run a small load test (e.g. 10 grinder threads) which have about 7~10 steps during the profiling. I have a script to start the product with profiler, start profiling (using controller api) and then start grinder to emulate user operations. When all the operations finish, the script tells the profiler to stop profiling and save snapshot.
During the profiling, for each step in the grinder test, it takes more than 1 million ms to finish. The whole profiling often takes more than 10 hours with just 10 grinder threads, and each runs the test 10 times. Without profiler, it finishes within 500 ms.
So... besides the problems with the product to be profiled, is there anything else that affects the performance of the cpu tracing process itself?
Last I used YourKit (v7.5.11, which is pretty old, current version is 12) it had two CPU profiling settings: sampling and tracing, the latter being much faster and less accurate. Since tracing is supposed to be more accurate I used it myself and also observed huge slowdown, in spite of the statement that the slowdown were "average". Yet it was far less than your results: from 2 seconds to 10 minutes. My code is a fragment of a calculation engine, virtually no IO, no waits on whatever, just reading a input, calculating and output the result into the console - so the whole slowdown comes from the profiler, no external influences.
Back to your question: the option mentioned - samping vs tracing, will affect the performance, so you may try sampling.
Now that I think of it: YourKit can be setup such that it does things automatically, like making snapshots periodically or on low memory, profiling memory usage, object allocations, each of this measures will make profiling slowlier. Perhaps you should make an online session instead of script controlled, to see what it really does.
According to some Yourkit Doc:
Although tracing provides more information, it has its drawbacks.
First, it may noticeably slow down the profiled application, because
the profiler executes special code on each enter to and exit from the
methods being profiled. The greater the number of method invocations
in the profiled application, the lower its speed when tracing is
turned on.
The second drawback is that, since this mode affects the execution
speed of the profiled application, the CPU times recorded in this mode
may be less adequate than times recorded with sampling. Please use
this mode only if you really need method invocation counts.
Also:
When sampling is used, the profiler periodically queries stacks of
running threads to estimate the slowest parts of the code. No method
invocation counts are available, only CPU time.
Sampling is typically the best option when your goal is to locate and
discover performance bottlenecks. With sampling, the profiler adds
virtually no overhead to the profiled application.
Also, it's a little confusing what the doc means by "CPU time", because it also talks about "wall-clock time".
If you are doing any I/O, waits, sleeps, or any other kind of blocking, it is important to get samples on wall-clock time, not CPU-only time, because it's dangerous to assume that blocked time is either insignificant or unavoidable.
Fortunately, that appears to be the default (though it's still a little unclear):
The default configuration for CPU sampling is to measure wall time for
I/O methods and CPU time for all other methods.
"Use Preconfigured Settings..." allows to choose this and other
presents. (sic)
If your goal is to make the code as fast as possible, don't be concerned with invocation counts and measurement "accuracy"; do find out which lines of code are on the stack a large fraction of the time, and why.
More on all that.
The problem is: VisualVM sampler shows call tree by time. For some method sampler shows only "Self time" so I can't see what makes this method slow. Here is an example.
How can I increase the depth of profiling?
Unfortunately sampling profilers are rather limited when it comes down to in-depth profiling due to a number of reasons:
Samplers are limited by the sampling period: For example, VisualVM currently has a minimum sampling period of 20ms. Modern processors can execute several million instructions in that time - certainly more than enough to call several short methods and return from them.
While an obvious solution would be to decrease the sampling period, this would also increase the impact of the profiler on your application, presenting a nice example of the uncertainty principle.
Samplers are easily confused by inline code: Both the JVM and any decent compiler will inline trivial and/or frequently-called methods, thus incorporating their code in the code of their caller. Sampling profilers have no way to tell which parts of each method actually belong to it and which belong to inline calls.
In the case of VisualVM Self time actually includes the execution time of both the method and any inlined code.
Samplers can get confused by an advanced VM: For example, in modern JVM implementations methods do not have a stable representation. Imagine for example the following method:
void A() {
...
B();
...
}
When the JVM starts B() is interpreted straight from the bytecode, thus taking quite a bit of time which makes it visible to the sampler. Then, after a while the JVM decides that B() is a good candidate for optimization and compiles it to native code, thus making it much faster. And after yet another while, the JVM might decide to inline the call to B(), incorporating its code in A().
At best, a sampling profiler will show the cost of those first runs and then the cost of any subsequent runs will be included in the time spent by the caller. This, unfortunately, can confuse an inexperienced developer into underestimating the cost of the method that was inlined.
At worst, that cost may be assigned to a sibling call, rather than the caller. For example, I am currently profiling an application using VisualVM, where a hotspot seems to be the ArrayList.size() method. In my Java implementation that method is a simple field getter that any JVM should quickly inline. Yet the profiler shows it as a major time consumer, completely ignoring a bunch of nearby HashMap calls that are obviously far more expensive.
The only way to avoid these weaknesses is to use an instrumenting profiler, rather than a sampling one. Instrumenting profilers, such as the one provided by the Profiler tab in VisualVM essentially record each and every method entry and exit in the selected code. Unfortunately, instrumenting profilers have a rather heavy impact on the profiled code:
They insert their monitoring code around each method, which completely changes the way a method is treated by the JVM. Even simple field getter/setter methods may not be inlined any more due to the extra code, thus skewing any results. The profiler usually tries to account for these changes, but it is not always successful.
They cause massive slow-downs to the profiled code, which makes them completely unsuitable for monitoring complete applications.
For these reasons instrumenting profilers are mostly suitable for analyzing hotspots that have already been detected using another method such as a sampling profiler. By instrumenting only a selected set of classes and/or methods it is possible to restrict the profiling side-effects to specific parts of an application.
There is nothing wrong in the example. It looks like that updateInfoInDirection() calls new SequenceInfo() and SequenceInfo.next(). 'Self time' means that the time is spent in the code of the methods itself (the method updateInfoInDirection() is on the bottom of the stack at the time when thread sample was taken).
I am running a script that animates a plot (simulation of a water flow). After a while, I kill the loop by doing ctrl-c.
After doing this several times I get the error:
??? Error: Out of memory.
And after I start receiving that error, every call to my script will generate it.
Now, it happens before anything inside the function that I am calling is executed, i.e even if I add the line a=1 as the first line of the function I am calling, I still get the error and no printout, so the code inside the function doesn't even get executed.
What could be causing this?
There are several possible reasons.
Most likely your script creates some variables that are filling up the memory. Run
clear all
before restarting the script, so that all the variables are cleared, or change your script to a function (which will automatically erase all temporary variables after the function returns). Note that this also clears all loaded functions, so your next execution of the script has to load them again which will slow down the next execution by a (usually tiny) bit. It may be sufficient to call clear only.
Maybe you're animating by plotting several plots over one another (without clearing the axes first). Thus you might run out of Java heap space. You can close the open figures individually, or run
close all
You can also increase the amount of Java Memory Matlab uses on your system (see instructions here) - note that the limit is generally rather low, annoyingly so if you want to tons of figures.
Especially if you're running an older version of Windows, you may get your memory fragmented. Matlab needs contiguous blocks of free space to assign variables. To check for memory fragmentation, run
memory
and look at the number for the maximum possible variable size. If this is much smaller than the size available for all arrays, it's time to restart Matlab (I guess if you use a Windows version that would require a reboot to fix the problem, you may want to look into getting a new computer with Win7).
You can also try the pack command, eg:
close all;
clear all;
pack;
to clear memory. Although after a recent mathworks seminar I asked one of the mathworks guru's and he also conformed #Andrew Janke's comment regarding memory fragmentation. Usually quitting and restarting matlab sorts this out for me (on XP).
clear all close all are straight-forward ways to free memory, which are known by all non-beginners.
The main issue is that when you have done some data large data processing, and cleared/closed everything off - there is still significant memory used by matlab.
This is a currently major problem with matlab, and to my knowledge there is no solution rather than restarting matlab, which is a pity.
It sounds like you are not clearing any of your variables. You should either provide a way to stop the loop without hitting ctrl-c (write a simple GUI with a "Stop" button and your display) and then clean up your workspace in the script or clear your variables at the start of the script.
Are you intentionally storing all the data (or some large component) on each iteration of your loop?