Related
In some code I'm maintaining, I see two different methods used in TClientDataSet.OnCalcFields event handlers:
with DataSet do
begin
// 1. Call FieldByName twice
if AMinDate > FieldByName(SPlanAllocatieFromDate).AsDateTime then
AMinDate := FieldByName(sPlanAllocatieFromDate).AsDateTime;
// 2. Put the retrieved FieldByName value in a temp var
lEmpID := FieldByName(SPlanAllocatieEmpID).AsInteger;
if lEmpID <> 0 then lTSAllocatedEmpIDs.Add(IntToStr(lEmpID));
end;
Will the compiler (Delphi XE2, Win32 app) optimize method 2 to use a temp var? The two FieldByNames are quite close, you could even say nested.
If not, I should rewrite 1. because OnCalcFields executes often.
BTW. I know about Fields[] versus FieldByName(), or using a temp TField var when running an EOF loop, those are not the issue here.
No version of the Delphi compiler does anything like this.
Such optimizations would require the compiler to be able to prove that the two calls to FieldByName would always give the same result, and there is currently no provision for flagging a method as being deterministic.
Note that it is quite possible in theory (if unlikely in reality) for the two calls NOT to give the same result, in this case e.g. if a different thread deletes a field out of the collection between the first and second call. Generally, the compiler does not know or care at the call site what a particular method call actually does.
Does the compiler optimize (close) identical FieldByName calls?
No it does not.
The compiler does not look inside function calls to see what is within. It therefore has no way to prove that the value returned by successive calls to a function would be the same. Likewise it has no way to prove that the function has no side-effects. These are the two prerequisites for the optimisation under consideration.
You will need to perform the optimisation yourself, by explicitly adding and using a local variable to store the value returned by a single call to FieldByName.
Beyond the consideration of performance, I would argue that the use of a local variable to hold the field is semantically much better. This makes it clear to the reader that all actions are performed on the same field. That reason alone would be enough to persuade me to make the change you describe. Don't repeat yourself.
And while we are in code review mode, you might care to reconsider the use of with.
I can write for..do process for integer value..
But I can't write it for int64 value.
For example:
var
i:int64;
begin
for i:=1 to 1000 do
end;
The compiler refuses to compile this, why does it refuse?
The Delphi compiler simply does not support Int64 loop counters yet.
Loop counters in a for loop have to be integers (or smaller).
This is an optimization to speed up the execution of a for loop.
Internally Delphi always uses an Int32, because on x86 this is the fastest datatype available.
This is documented somewhere deep in the manual, but I don't have a link handy right now.
If you must have a 64 bit loop counter, use a while..do or repeat..until loop.
Even if the compiler did allow "int64" in a Delphi 7 for-loop (Delphi 7???), it probably wouldn't complete iterating through the full range until sometime after the heat death of the Sun.
So why can't you just use an "integer"?
If you must use an int64 value ... then simply use a "while" loop instead.
Problem solved :)
Why to use a Int64 on a for-loop?
Easy to answer:
There is no need to do a lot of iterations to need a Int64, just do a loop from 5E9 to 5E9+2 (three iterations in total).
It is just that values on iteration are bigger than what Int32 can hold
An example:
procedure Why_Int64_Would_Be_Great_On_For_Loop;
const
StartValue=5000000000; // Start form 5E9, 5 thousand millons
Quantity=10; // Do it ten times
var
Index:Int64;
begin
for Index:=StartValue to StartValue+Quantity-1
do begin // Bla bla bla
// Do something really fast (only ten times)
end;
end;
That code would take no time at all, it is just that index value need to be far than 32bit integer limit.
The solution is to do it with a while loop:
procedure Equivalent_For_Loop_With_Int64_Index;
const
StartValue=5000000000; // Start form 5E9, 5 thousand millons
Quantity=10; // Do it ten times
var
Index:Int64;
begin
Index:=StartValue;
while Index<=StartValue+Quantity
do begin // Bla bla bla
// Do something really fast (only ten times)
Inc(Index);
end;
end;
So why the compiler refuses to compile the foor loop, i see no real reason... any for loop can be auto-translated into a while loop... and pre-compiler could do such before compiler (like other optimizations that are done)... the only reason i see is the lazy people that creates the compiler that did not think on it.
If for is optimized and so it is only able to use 32 bit index, then if code try to use a 64 bit index it can not be so optimized, so why not let pre-compiler optimizator to chage that for us... it only gives bad image to programmers!!!
I do not want to make anyone ungry...
I only just say something obvious...
By the way, not all people start a foor loop on zero (or one) values... sometimes there is the need to start it on really huge values.
It is allways said, that if you need to do something a fixed number of times you best use for loop instead of while loop...
Also i can say something... such two versions, the for-loop and the while-loop that uses Inc(Index) are equally fast... but if you put the while-loop step as Index:=Index+1; it is slower; it is really not slower because pre-compiler optimizator see that and use Inc(Index) instead... you can see if buy doing the next:
// I will start the loop from zero, not from two, but i first do some maths to avoid pre-compiler optimizator to convert Index:=Index+Step; to Inc(Index,Step); or better optimization convert it to Inc(Index);
Index:=2;
Step:=Index-1; // Do not put Step:=1; or optimizator will do the convertion to Inc()
Index:=Step-2; // Now fix, the start, so loop will start from zero
while Index<1000000 // 1E6, one millon iterations, from 0 to 999999
do begin
// Do something
Index:=Index+Step; // Optimizator will not change this into Inc(Index), since sees that Step has changed it's value before
end;
The optimizer can see a variable do not change its value, so it can convert it to a constant, then on the increment assign if adding a constant (variable:=variable+constant) it will optimize it to Inc(variable,constant) and in the case it sees such constant is 1 it will also optimes it to Inc(variable)... and such optimizatons in low level computer language are very noticeble...
In Low level computer language:
A normal add (variable:=variable1+variable2) implies two memory reads plus one sum plus one memory write... lot of work
But if is a (variable:=variable+othervariable) it can be optimized holding variable inside the processor cache.
Also if it is a (variable:=variable1+constant) it can also be optimized by holding constant on the processor cache
And if it is (variable:=variable+constant) both are cached on processor cache, so huge fast compared with other options, no acces to RAM is needed.
In such way pre-compiler optimizer do another important optimization... for-loops index variables are holded as processor registers... much more faster than processor cache...
Most mother processor do an extra optimization as well (at hardware level, inside the processor)... some cache areas (32 bit variables for us) seen that are intensivly used are stored as special registers to fasten access... and such for-loop / while-loop indexes are ones of them... but as i said.. most mother AMD proccesors (the ones that uses MP technology does that)... i do not yet know any Intel that do that!!! such optimization is more relevant when multi-core and on super-computing... so maybe that is the reason why AMD has it and Intel not!!!
I only want to show one "why", there are a lot more... another one could be as simple as the index is stored on a database Int64 field type, etc... there are a lot of reasons i know and a lot more i did not know yet...
I hope this will help to understand the need to do a loop on a Int64 index and also how to do it without loosing speed by correctly eficiently converting loop into a while loop.
Note: For x86 compiles (not for 64bit compilation) beware that Int64 is managed internally as two Int32 parts... and when modifing values there is an extra code to do, on adds and subs it is very low, but on multiplies or divisions such extra is noticeble... but if you really need Int64 you need it, so what else to do... and imagine if you need float or double, etc...!!!
I will have to create a multi-threading project soon I have seen experiments ( delphitools.info/2011/10/13/memory-manager-investigations ) showing that the default Delphi memory manager has problems with multi-threading.
So, I have found this SynScaleMM. Anybody can give some feedback on it or on a similar memory manager?
Thanks
Our SynScaleMM is still experimental.
EDIT: Take a look at the more stable ScaleMM2 and the brand new SAPMM. But my remarks below are still worth following: the less allocation you do, the better you scale!
But it worked as expected in a multi-threaded server environment. Scaling is much better than FastMM4, for some critical tests.
But the Memory Manager is perhaps not the bigger bottleneck in Multi-Threaded applications. FastMM4 could work well, if you don't stress it.
Here are some (not dogmatic, just from experiment and knowledge of low-level Delphi RTL) advice if you want to write FAST multi-threaded application in Delphi:
Always use const for string or dynamic array parameters like in MyFunc(const aString: String) to avoid allocating a temporary string per each call;
Avoid using string concatenation (s := s+'Blabla'+IntToStr(i)) , but rely on a buffered writing such as TStringBuilder available in latest versions of Delphi;
TStringBuilder is not perfect either: for instance, it will create a lot of temporary strings for appending some numerical data, and will use the awfully slow SysUtils.IntToStr() function when you add some integer value - I had to rewrite a lot of low-level functions to avoid most string allocation in our TTextWriter class as defined in SynCommons.pas;
Don't abuse on critical sections, let them be as small as possible, but rely on some atomic modifiers if you need some concurrent access - see e.g. InterlockedIncrement / InterlockedExchangeAdd;
InterlockedExchange (from SysUtils.pas) is a good way of updating a buffer or a shared object. You create an updated version of of some content in your thread, then you exchange a shared pointer to the data (e.g. a TObject instance) in one low-level CPU operation. It will notify the change to the other threads, with very good multi-thread scaling. You'll have to take care of the data integrity, but it works very well in practice.
Don't share data between threads, but rather make your own private copy or rely on some read-only buffers (the RCU pattern is the better for scaling);
Don't use indexed access to string characters, but rely on some optimized functions like PosEx() for instance;
Don't mix AnsiString/UnicodeString kind of variables/functions, and check the generated asm code via Alt-F2 to track any hidden unwanted conversion (e.g. call UStrFromPCharLen);
Rather use var parameters in a procedure instead of function returning a string (a function returning a string will add an UStrAsg/LStrAsg call which has a LOCK which will flush all CPU cores);
If you can, for your data or text parsing, use pointers and some static stack-allocated buffers instead of temporary strings or dynamic arrays;
Don't create a TMemoryStream each time you need one, but rely on a private instance in your class, already sized in enough memory, in which you will write data using Position to retrieve the end of data and not changing its Size (which will be the memory block allocated by the MM);
Limit the number of class instances you create: try to reuse the same instance, and if you can, use some record/object pointers on already allocated memory buffers, mapping the data without copying it into temporary memory;
Always use test-driven development, with dedicated multi-threaded test, trying to reach the worse-case limit (increase number of threads, data content, add some incoherent data, pause at random, try to stress network or disk access, benchmark with timing on real data...);
Never trust your instinct, but use accurate timing on real data and process.
I tried to follow those rules in our Open Source framework, and if you take a look at our code, you'll find out a lot of real-world sample code.
If your app can accommodate GPL licensed code, then I'd recommend Hoard. You'll have to write your own wrapper to it but that is very easy. In my tests, I found nothing that matched this code. If your code cannot accommodate the GPL then you can obtain a commercial licence of Hoard, for a significant fee.
Even if you can't use Hoard in an external release of your code you could compare its performance with that of FastMM to determine whether or not your app has problems with heap allocation scalability.
I have also found that the memory allocators in the versions of msvcrt.dll distributed with Windows Vista and later scale quite well under thread contention, certainly much better than FastMM does. I use these routines via the following Delphi MM.
unit msvcrtMM;
interface
implementation
type
size_t = Cardinal;
const
msvcrtDLL = 'msvcrt.dll';
function malloc(Size: size_t): Pointer; cdecl; external msvcrtDLL;
function realloc(P: Pointer; Size: size_t): Pointer; cdecl; external msvcrtDLL;
procedure free(P: Pointer); cdecl; external msvcrtDLL;
function GetMem(Size: Integer): Pointer;
begin
Result := malloc(size);
end;
function FreeMem(P: Pointer): Integer;
begin
free(P);
Result := 0;
end;
function ReallocMem(P: Pointer; Size: Integer): Pointer;
begin
Result := realloc(P, Size);
end;
function AllocMem(Size: Cardinal): Pointer;
begin
Result := GetMem(Size);
if Assigned(Result) then begin
FillChar(Result^, Size, 0);
end;
end;
function RegisterUnregisterExpectedMemoryLeak(P: Pointer): Boolean;
begin
Result := False;
end;
const
MemoryManager: TMemoryManagerEx = (
GetMem: GetMem;
FreeMem: FreeMem;
ReallocMem: ReallocMem;
AllocMem: AllocMem;
RegisterExpectedMemoryLeak: RegisterUnregisterExpectedMemoryLeak;
UnregisterExpectedMemoryLeak: RegisterUnregisterExpectedMemoryLeak
);
initialization
SetMemoryManager(MemoryManager);
end.
It is worth pointing out that your app has to be hammering the heap allocator quite hard before thread contention in FastMM becomes a hindrance to performance. Typically in my experience this happens when your app does a lot of string processing.
My main piece of advice for anyone suffering from thread contention on heap allocation is to re-work the code to avoid hitting the heap. Not only do you avoid the contention, but you also avoid the expense of heap allocation – a classic twofer!
It is locking that makes the difference!
There are two issues to be aware of:
Use of the LOCK prefix by the Delphi itself (System.dcu);
How does FastMM4 handles thread contention and what it does after it failed to acquire a lock.
Use of the LOCK prefix by the Delphi itself
Borland Delphi 5, released in 1999, was the one that introduced the lock prefix in string operations. As you know, when you assign one string to another, it does not copy the whole string but merely increases the reference counter inside the string. If you modify the string, it is de-references, decreasing the reference counter and allocating separate space for the modified string.
In Delphi 4 and earlier, the operations to increase and decrease the reference counter were normal memory operations. The programmers that have used Delphi knew about and, and, if they were using strings across threads, i.e. pass a string from one thread to another, have used their own locking mechanism only for the relevant strings. Programmers did also use read-only string copy that did not modify in any way the source string and did not require locking, for example:
function AssignStringThreadSafe(const Src: string): string;
var
L: Integer;
begin
L := Length(Src);
if L <= 0 then Result := '' else
begin
SetString(Result, nil, L);
Move(PChar(Src)^, PChar(Result)^, L*SizeOf(Src[1]));
end;
end;
But in Delphi 5, Borland have added the LOCK prefix to the string operations and they became very slow, compared to Delphi 4, even for single-threaded applications.
To overcome this slowness, programmers became to use "single threaded" SYSTEM.PAS patch files with lock's commented.
Please see https://synopse.info/forum/viewtopic.php?id=57&p=1 for more information.
FastMM4 Thread Contention
You can modify FastMM4 source code for a better locking mechanism, or use any existing FastMM4 fork, for example https://github.com/maximmasiutin/FastMM4
FastMM4 is not the fastest one for multicore operation, especially when the number of threads is more than the number of physical sockets is because it, by default, on thread contention (i.e. when one thread cannot acquire access to data, locked by another thread) calls Windows API function Sleep(0), and then, if the lock is still not available enters a loop by calling Sleep(1) after each check of the lock.
Each call to Sleep(0) experiences the expensive cost of a context switch, which can be 10000+ cycles; it also suffers the cost of ring 3 to ring 0 transitions, which can be 1000+ cycles. As about Sleep(1) – besides the costs associated with Sleep(0) – it also delays execution by at least 1 millisecond, ceding control to other threads, and, if there are no threads waiting to be executed by a physical CPU core, puts the core into sleep, effectively reducing CPU usage and power consumption.
That’s why, on multithreded wotk with FastMM, CPU use never reached 100% - because of the Sleep(1) issued by FastMM4. This way of acquiring locks is not optimal. A better way would have been a spin-lock of about 5000 pause instructions, and, if the lock was still busy, calling SwitchToThread() API call. If pause is not available (on very old processors with no SSE2 support) or SwitchToThread() API call was not available (on very old Windows versions, prior to Windows 2000), the best solution would be to utilize EnterCriticalSection/LeaveCriticalSection, that don’t have latency associated by Sleep(1), and which also very effectively cedes control of the CPU core to other threads.
The fork that I've mentioned uses a new approach to waiting for a lock, recommended by Intel in its Optimization Manual for developers - a spinloop of pause + SwitchToThread(), and, if any of these are not available: CriticalSections instead of Sleep(). With these options, the Sleep() will never be used but EnterCriticalSection/LeaveCriticalSection will be used instead. Testing has shown that the approach of using CriticalSections instead of Sleep (which was used by default before in FastMM4) provides significant gain in situations when the number of threads working with the memory manager is the same or higher than the number of physical cores. The gain is even more evident on computers with multiple physical CPUs and Non-Uniform Memory Access (NUMA). I have implemented compile-time options to take away the original FastMM4 approach of using Sleep(InitialSleepTime) and then Sleep(AdditionalSleepTime) (or Sleep(0) and Sleep(1)) and replace them with EnterCriticalSection/LeaveCriticalSection to save valuable CPU cycles wasted by Sleep(0) and to improve speed (reduce latency) that was affected each time by at least 1 millisecond by Sleep(1), because the Critical Sections are much more CPU-friendly and have definitely lower latency than Sleep(1).
When these options are enabled, FastMM4-AVX it checks: (1) whether the CPU supports SSE2 and thus the "pause" instruction, and (2) whether the operating system has the SwitchToThread() API call, and, if both conditions are met, uses "pause" spin-loop for 5000 iterations and then SwitchToThread() instead of critical sections; If a CPU doesn't have the "pause" instrcution or Windows doesn't have the SwitchToThread() API function, it will use EnterCriticalSection/LeaveCriticalSection.
You can see the test results, including made on a computer with multiple physical CPUs (sockets) in that fork.
See also the Long Duration Spin-wait Loops on Hyper-Threading Technology Enabled Intel Processors article. Here is what Intel writes about this issue - and it applies to FastMM4 very well:
The long duration spin-wait loop in this threading model seldom causes a performance problem on conventional multiprocessor systems. But it may introduce a severe penalty on a system with Hyper-Threading Technology because processor resources can be consumed by the master thread while it is waiting on the worker threads. Sleep(0) in the loop may suspend the execution of the master thread, but only when all available processors have been taken by worker threads during the entire waiting period. This condition requires all worker threads to complete their work at the same time. In other words, the workloads assigned to worker threads must be balanced. If one of the worker threads completes its work sooner than others and releases the processor, the master thread can still run on one processor.
On a conventional multiprocessor system this doesn't cause performance problems because no other thread uses the processor. But on a system with Hyper-Threading Technology the processor the master thread runs on is a logical one that shares processor resources with one of the other worker threads.
The nature of many applications makes it difficult to guarantee that workloads assigned to worker threads are balanced. A multithreaded 3D application, for example, may assign the tasks for transformation of a block of vertices from world coordinates to viewing coordinates to a team of worker threads. The amount of work for a worker thread is determined not only by the number of vertices but also by the clipped status of the vertex, which is not predictable when the master thread divides the workload for working threads.
A non-zero argument in the Sleep function forces the waiting thread to sleep N milliseconds, regardless of the processor availability. It may effectively block the waiting thread from consuming processor resources if the waiting period is set properly. But if the waiting period is unpredictable from workload to workload, then a large value of N may make the waiting thread sleep too long, and a smaller value of N may cause it to wake up too quickly.
Therefore the preferred solution to avoid wasting processor resources in a long duration spin-wait loop is to replace the loop with an operating system thread-blocking API, such as the Microsoft Windows* threading API,
WaitForMultipleObjects. This call causes the operating system to block the waiting thread from consuming processor resources.
It refers to Using Spin-Loops on Intel Pentium 4 Processor and Intel Xeon Processor application note.
You can also find a very good spin-loop implementation here at stackoverflow.
It also loads normal loads just to check before issuing a lock-ed store, just to not flood the CPU with locked operations in a loop, that would lock the bus.
FastMM4 per se is very good. Just improve the locking and you will get an excelling multi-threaded memory manager.
Please also be aware that each small block type is locked separately in FastMM4.
You can put padding between the small block control areas, to make each area have own cache line, not shared with other block sizes, and to make sure it begins at a cache line size boundary. You can use CPUID to determine the size of the CPU cache line.
So, with locking correctly implemented to suit your needs (i.e. whether you need NUMA or not, whether to use lock-ing releases, etc., you may obtain the results that the memory allocation routines would be several times faster and would not suffer so severely from thread contention.
FastMM deals with multi-threading just fine. It is the default memory manager for Delphi 2006 and up.
If you are using an older version of Delphi (Delphi 5 and up), you can still use FastMM. It's available on SourceForge.
You could use TopMM:
http://www.topsoftwaresite.nl/
You could also try ScaleMM2 (SynScaleMM is based on ScaleMM1) but I have to fix a bug regarding to interthread memory, so not production ready yet :-(
http://code.google.com/p/scalemm/
Deplhi 6 memory manager is outdated and outright bad. We were using RecyclerMM both on a high-load production server and on a multi-threaded desktop application, and we had no issues with it: it's fast, reliable and doesn't cause excess fragmentation. (Fragmentation was Delphi's stock memory manager worst issue).
The only drawback of RecyclerMM is that it isn't compatible with MemCheck out of the box. However, a small source alteration was enough to render it compatible.
Here is a little test:
function inc(n:integer):integer;
begin
n := n+1;
result := n;
end;
procedure TForm1.Button1Click(Sender: TObject);
var
start,i,n:integer;
begin
n := 0;
start := getTickCount;
for i := 0 to 10000000 do begin
inc(n);//calling inc function takes 73 ms
//n := n+1; writing it directly takes 16 ms
end;
showMessage(inttostr(getTickCount-start));
end;
Yes, calling a function introduces an overhead. Before calling the function it's necessary to save the current state - which instruction was planned to execute next - and also to copy the function parameters. This requires extra work and extra time.
That's where inlining is helpful. If the compiler supports that it can just injsct the function code directly at the call site and avoid the overhead. With good optimization of surrounding code it can even decrease amount of generated code.
This doesn't mean you need to avoid functions. In most cases the function body executes much longer that the time needed to organize the call. Only in quite rare cases the overhead is worth optimizing. This should never be done without the help of the profiler - otherwise you waste time and most likely just get a lot of unmaintainable code.
Calling a function (whichever language you're working with) generally involves doing a bit more things, like saving some context, pushing parameters to some kind of stack, calling the function itself, reading the parameters, and then pushing the result back somewhere, returning from the function, extracting the return value, ...
So, of course, calling functions generally means having some overhead.
But the main point of functions is re-using some parts of code : maybe it will take a few micro-seconds more at execution, but if you only have to write some code once, instead of 10 (or more) times, there is a huge gain ; and that code will be much easier to maintain, which is really important in the long term.
After, you might want not using functions for some really small parts of code like the one you provided as an example (well, except if the language you're using provides some kind of inlining thing -- it's the case for C, if I remember correctly ; not sure about delphi, though) : the overhead of calling the function will be important, compared to the number of lines of code the function will save you from writing (here : none ! On the contrary ^^ ).
But for bigger parts of code, the overhead will me much smaller, compared to the time taken to execute the bunch of code the function contains...
Premature optimization is the root of all evil...
Write correct and maintainable code using the known features (here the built-in pseudo(magic) procedure inc), benchmark it and refactor where it's needed for performance reason (if any).
I bet that in 99.9% of the cases, avoiding calling a function or procedure is not the solution.
Here is an example where adding a call to a procedure actually IS the optimization.
Only optimize when there is a bottleneck.
Your current code is perfectly fine for about 99.9% of the cases.
If it gets slow, use a profiler to point you at the bottleneck.
When the bottleneck appears to be in the inc function, then you can always inline your function by marking it with the 'inline' directive.
I totally agree with Francois on this one.
One of the most expensive parts of a function call is the returning of the result.
If you did want to keep your program modular, but wanted to save a bit of time, change your function to a procedure and use a var parameter to retrieve the result.
So for your example:
procedure inc(var n:integer);
begin
n := n+1;
end;
should be considerably faster than using your inc function.
Also, in the loop in your example, you have the statement:
inc(n)
but this will not update the value of n. The loop will finish and n will have the value of 0. What you need instead is:
n := inc(n);
For your timings, do you have optimization on? If you do, then it may not be timing what you thing it is. The value of n is not used by the program and may be optimized right out of it.
To make sure that n is used for the timings, you can simply display the value of n in your showMessage line.
Finally, inc is a built in procedure. It is not good practice to use the same function name as that of a built in procedure as it can cause doubts as to which procedure is being executed - yours or the built in one.
Change your function's name to myinc, and then do a third test with the built in inc procedure itself, to see if it is faster than n := n + 1;
As others before me said. Yes it does. Every line of code you write does. Functions need to store current states of registers etc... before they can execute and restore it afterwards.
But the overhead is so minimal that optimizing that means nothing. It is more important to have a redable well structured code. Almost always. There may be rare cases when every nanosecond is important but I cannot imagine one right now.
Look here for general guidelines about performance in delphi programs:
http://effovex.com/OptimalCode/opguide.htm
just want to add some comments specific to Delphi:
I think i remember than getTickCount() got a minimal resolution a bit hight to do this kind of test. (+/- 10-15ms). You could use QueryPerformanceCounter() for a better result.
for small function called a lot of time (inside process loop, data convertion, ...) use INLINE (search the help)
but to know for real what a funciton take and if you should do something about it, use a profiler !! I use http://www.prodelphi.de/, it's pretty simple, very usefull and the price is very correct compare to other profiler (ie: +/-50€ instead of 500€)
In delphi, they is the inc() function. It's faster than "n := n+1". ( because inc() is not really a function, it is replaced by the compiler by asm. ie: they is no source code for the funcion inc() ).
All good comments.
Functions are supposed to be useful, that's why they're in the language. The assumption is that if they have a nominal cost, you are willing to pay that to get the utility they provide.
Here's the real problem with functions, no matter who writes them, but especially if somebody other than you wrote them.
They have an implied contract for what they're supposed to do, but they have no contract for how long they should take.
Usually the person who writes the function thinks "This function does something valuable, so the person who calls it will respect that, and use it sparingly."
Then the person who calls it thinks "This function does so much in only a single call that I can make my code really clean and powerful by calling it lots of times."
Now, with multiple layers of abstraction, this effect acts like compound interest.
So, the real performance problem with functions is not the cost of calls, it is the psychology of programmers, leading to exponential slowdown.
Fortunately, experience in performance tuning can ameliorate this problem.
How does a stack overflow occur and what are the ways to make sure it doesn't happen, or ways to prevent one?
Stack
A stack, in this context, is the last in, first out buffer you place data while your program runs. Last in, first out (LIFO) means that the last thing you put in is always the first thing you get back out - if you push 2 items on the stack, 'A' and then 'B', then the first thing you pop off the stack will be 'B', and the next thing is 'A'.
When you call a function in your code, the next instruction after the function call is stored on the stack, and any storage space that might be overwritten by the function call. The function you call might use up more stack for its own local variables. When it's done, it frees up the local variable stack space it used, then returns to the previous function.
Stack overflow
A stack overflow is when you've used up more memory for the stack than your program was supposed to use. In embedded systems you might only have 256 bytes for the stack, and if each function takes up 32 bytes then you can only have function calls 8 deep - function 1 calls function 2 who calls function 3 who calls function 4 .... who calls function 8 who calls function 9, but function 9 overwrites memory outside the stack. This might overwrite memory, code, etc.
Many programmers make this mistake by calling function A that then calls function B, that then calls function C, that then calls function A. It might work most of the time, but just once the wrong input will cause it to go in that circle forever until the computer recognizes that the stack is overblown.
Recursive functions are also a cause for this, but if you're writing recursively (ie, your function calls itself) then you need to be aware of this and use static/global variables to prevent infinite recursion.
Generally, the OS and the programming language you're using manage the stack, and it's out of your hands. You should look at your call graph (a tree structure that shows from your main what each function calls) to see how deep your function calls go, and to detect cycles and recursion that are not intended. Intentional cycles and recursion need to be artificially checked to error out if they call each other too many times.
Beyond good programming practices, static and dynamic testing, there's not much you can do on these high level systems.
Embedded systems
In the embedded world, especially in high reliability code (automotive, aircraft, space) you do extensive code reviews and checking, but you also do the following:
Disallow recursion and cycles - enforced by policy and testing
Keep code and stack far apart (code in flash, stack in RAM, and never the twain shall meet)
Place guard bands around the stack - empty area of memory that you fill with a magic number (usually a software interrupt instruction, but there are many options here), and hundreds or thousands of times a second you look at the guard bands to make sure they haven't been overwritten.
Use memory protection (ie, no execute on the stack, no read or write just outside the stack)
Interrupts don't call secondary functions - they set flags, copy data, and let the application take care of processing it (otherwise you might get 8 deep in your function call tree, have an interrupt, and then go out another few functions inside the interrupt, causing the blowout). You have several call trees - one for the main processes, and one for each interrupt. If your interrupts can interrupt each other... well, there be dragons...
High-level languages and systems
But in high level languages run on operating systems:
Reduce your local variable storage (local variables are stored on the stack - although compilers are pretty smart about this and will sometimes put big locals on the heap if your call tree is shallow)
Avoid or strictly limit recursion
Don't break your programs up too far into smaller and smaller functions - even without counting local variables each function call consumes as much as 64 bytes on the stack (32 bit processor, saving half the CPU registers, flags, etc)
Keep your call tree shallow (similar to the above statement)
Web servers
It depends on the 'sandbox' you have whether you can control or even see the stack. Chances are good you can treat web servers as you would any other high level language and operating system - it's largely out of your hands, but check the language and server stack you're using. It is possible to blow the stack on your SQL server, for instance.
A stack overflow in real code occurs very rarely. Most situations in which it occurs are recursions where the termination has been forgotten. It might however rarely occur in highly nested structures, e.g. particularly large XML documents. The only real help here is to refactor the code to use an explicit stack object instead of the call stack.
Most people will tell you that a stack overflow occurs with recursion without an exit path - while mostly true, if you work with big enough data structures, even a proper recursion exit path won't help you.
Some options in this case:
Breadth-first search
Tail recursion, .Net-specific great blog post (sorry, 32-bit .Net)
Infinite recursion is a common way to get a stack overflow error. To prevent - always make sure there's an exit path that will be hit. :-)
Another way to get a stack overflow (in C/C++, at least) is to declare some enormous variable on the stack.
char hugeArray[100000000];
That'll do it.
Aside from the form of stack overflow that you get from a direct recursion (eg Fibonacci(1000000)), a more subtle form of it that I have experienced many times is an indirect recursion, where a function calls another function, which calls another, and then one of those functions calls the first one again.
This can commonly occur in functions that are called in response to events but which themselves may generate new events, for example:
void WindowSizeChanged(Size& newsize) {
// override window size to constrain width
newSize.width=200;
ResizeWindow(newSize);
}
In this case the call to ResizeWindow may cause the WindowSizeChanged() callback to be triggered again, which calls ResizeWindow again, until you run out of stack. In situations like these you often need to defer responding to the event until the stack frame has returned, eg by posting a message.
Usually a stack overflow is the result of an infinite recursive call (given the usual amount of memory in standard computers nowadays).
When you make a call to a method, function or procedure the "standard" way or making the call consists on:
Pushing the return direction for the call into the stack(that's the next sentence after the call)
Usually the space for the return value get reserved into the stack
Pushing each parameter into the stack (the order diverges and depends on each compiler, also some of them are sometimes stored on the CPU registers for performance improvements)
Making the actual call.
So, usually this takes a few bytes depeding on the number and type of the parameters as well as the machine architecture.
You'll see then that if you start making recursive calls the stack will begin to grow. Now, stack is usually reserved in memory in such a way that it grows in opposite direction to the heap so, given a big number of calls without "coming back" the stack begins to get full.
Now, on older times stack overflow could occur simply because you exausted all available memory, just like that. With the virtual memory model (up to 4GB on a X86 system) that was out of the scope so usually, if you get an stack overflow error, look for an infinite recursive call.
I have recreated the stack overflow issue while getting a most common Fibonacci number i.e. 1, 1, 2, 3, 5..... so calculation for fib(1) = 1 or fib(3) = 2.. fib(n) = ??.
for n, let say we will interested - what if n = 100,000 then what will be the corresponding Fibonacci number ??
The one loop approach is as below -
package com.company.dynamicProgramming;
import java.math.BigInteger;
public class FibonacciByBigDecimal {
public static void main(String ...args) {
int n = 100000;
BigInteger[] fibOfnS = new BigInteger[n + 1];
System.out.println("fibonacci of "+ n + " is : " + fibByLoop(n));
}
static BigInteger fibByLoop(int n){
if(n==1 || n==2 ){
return BigInteger.ONE;
}
BigInteger fib = BigInteger.ONE;
BigInteger fip = BigInteger.ONE;
for (int i = 3; i <= n; i++){
BigInteger p = fib;
fib = fib.add(fip);
fip = p;
}
return fib;
}
}
this quite straight forward and result is -
fibonacci of 100000 is : 25974069347221724166155034021275915414880485386517696584724770703952534543511273686265556772836716744754637587223074432111638399473875091030965697382188304493052287638531334921353026792789567010512765782716356080730505322002432331143839865161378272381247774537783372999162146340500546698603908627509966393664092118901252719601721050603003505868940285581036751176582513683774386849364134573388343651587754253719124105003321959913300622043630352137565254218239986908485563740801792517616293917549634585586163007628199160811098365263529954406942842065710460449038056471363460330005208522777075544467947237090309790190148604328468198579610159510018506082649192345873133991501339199323631023018641725364771362664750801339824312317034314529641817900511879573167668349799016820118499077566864568450662873924856039140476051995500662888263458771894106803700918793650017330117100283104739474562560914449328213748555738640805798130282666402703542944121049199958031318768058991865134251759599115205631553377039969410355182752749199598022575079020377981030899229849963044962558140455170002502997643221934621653662108418767454282982613982344783665815880408190033073829395000821320093747154851310272208173054322648669496309879147143629255542526240439996153269798768075106468190687921182991679644091782718685617029181022126792674013626504997849688436809752547001310045741864064482994858725517447466956518791269169932445648176733222571493149677633458466238303338202397024368594782876418757885729107101337003000942293335972927791914092128049015459762627910570552481588840517794181929052167695766087488155678601288183543542923073978101547857013284386127286201766539534449930019800629538936985500723286651317181135886613537472684585432548981137176605194616937916884425342594781263103889520479565943807153019112539648471126389007133628569101551453423329441284357220996286746119420951661002309740709965531900508158669911445442647882872642845017253320486483194578920399848938236367456182203750973485668474338872490493370316338265717607297788917989136673251906232471180372801739215723908227692280772924566627505383375006926077210593619421268920302567443565378008318306375933345023502569729065152853271943677560156660399164048825639676930792905029514886934137991251748566670747175149389790386533381395346848378086126737554383821108448976538368483182588363399173104558509056638462025014631311831087429077292622159430204291594740306101839816855066950261973761508571761199475875722129872053120607918649803615960923395941041186351688548839119185179061511562752936158490008721501922265117853150892510275280451512386037921846921215338292871369243215273327141574788295902601571954853164447945467502858402360002383447905203451080332820138038807089807348326201227952633606773669875783326254859449060219173688677862411205621098369850197290177157801120404586491539351157834995461006366357454485082418882790675313599505192062229760153765297973085881648731173082370598284894044874039320535929359764541655607954724778620299692329561389719894679422187273605123365595211331087787582288795975803204596084790245063851941743126163775104599211024868794963417068620929088930685252348056925998333775103901013166178123051145719327066291671254465121517468025481903583516889717075706778656188008220346836321018130262329960275994035799977740462449521145315883703579044832931500072461734173558055678321534543411700202585608091662941986374015145695722728369219632295111877625307534025947814482046574602884855000628069348113982760168555840795421620575435572915106415375929390228843561207926437055600623679865443824643739469724719459965557955058380348255978396827760847315302517889517186307227611036305093600742622617173630586132915440246954329046162586917746305785076749374879923291817501634840688134655343709975893536074051729094126976575932951568186247471276364688365517570183534172746626073065104511957628663499228486787805910851189856535554349587616640164475880286336297040462890970677362565843002353147494612339120686321466370878446992104275415694109122465685712047172411333784898167640969249816334211768571503116710400681753031921154156119580425706586931272762137106974722260296555246110537155545324997508432752001992143019105053629960070429632978051030666506387862681576587726837451289768507963663710593809112254288358391941211547737599813019216509521401333060709873137329265181692268450634439540567298120315463923249817937804691037934221694952291007930299492375072993250630509428139027930841344730614116433556147640931044259184813639305423693789765205264563476483182726333715121120306292338892864879492097378478618848682608046473195392008403983080088038690495574197562192939221108257663976813610444900247209483403267967688376213967440757138872928630798218493143438797780887379588968409461434159271317578365114578289355818599029235343888888465874521308381377794436361197628390368945957601203165022798579015453447473527069728514545998614229027372911314637820455162254475353567736227936485450357102086445412089842350389087702230398493802147348096874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Now another approach I have applied is through Divide and Concur via recursion
i.e. Fib(n) = fib(n-1) + Fib(n-2) and then further recursion for n-1 & n-2.....till 2 & 1. which is programmed as -
package com.company.dynamicProgramming;
import java.math.BigInteger;
public class FibonacciByBigDecimal {
public static void main(String ...args) {
int n = 100000;
BigInteger[] fibOfnS = new BigInteger[n + 1];
System.out.println("fibonacci of "+ n + " is : " + fibByDivCon(n, fibOfnS));
}
static BigInteger fibByDivCon(int n, BigInteger[] fibOfnS){
if(fibOfnS[n]!=null){
return fibOfnS[n];
}
if (n == 1 || n== 2){
fibOfnS[n] = BigInteger.ONE;
return BigInteger.ONE;
}
// creates 2 further entries in stack
BigInteger fibOfn = fibByDivCon(n-1, fibOfnS).add( fibByDivCon(n-2, fibOfnS)) ;
fibOfnS[n] = fibOfn;
return fibOfn;
}
}
When i ran the code for n = 100,000 the result is as below -
Exception in thread "main" java.lang.StackOverflowError
at com.company.dynamicProgramming.FibonacciByBigDecimal.fibByDivCon(FibonacciByBigDecimal.java:29)
at com.company.dynamicProgramming.FibonacciByBigDecimal.fibByDivCon(FibonacciByBigDecimal.java:29)
at com.company.dynamicProgramming.FibonacciByBigDecimal.fibByDivCon(FibonacciByBigDecimal.java:29)
Above you can see the StackOverflowError is created. Now the reason for this is too many recursion as -
// creates 2 further entries in stack
BigInteger fibOfn = fibByDivCon(n-1, fibOfnS).add( fibByDivCon(n-2, fibOfnS)) ;
So each entry in stack create 2 more entries and so on... which is represented as -
Eventually so many entries will be created that system is unable to handle in the stack and StackOverflowError thrown.
For Prevention :
For Above example perspective
Avoid using recursion approach or reduce/limit the recursion by again one level division like if n is too large then split the n so that system can handle with in its limit.
Use other approach, like the loop approach I have used in 1st code sample. (I am not at all intended to degrade Divide & Concur or Recursion as they are legendary approaches in many most famous algorithms.. my intention is to limit or stay away from recursion if I suspect stack overflow issues)
Considering this was tagged with "hacking", I suspect the "stack overflow" he's referring to is a call stack overflow, rather than a higher level stack overflow such as those referenced in most other answers here. It doesn't really apply to any managed or interpreted environments such as .NET, Java, Python, Perl, PHP, etc, which web apps are typically written in, so your only risk is the web server itself, which is probably written in C or C++.
Check out this thread:
https://stackoverflow.com/questions/7308/what-is-a-good-starting-point-for-learning-buffer-overflow
Stack overflow occurs when your program uses up the entire stack. The most common way this happens is when your program has a recursive function which calls itself forever. Every new call to the recursive function takes more stack until eventually your program uses up the entire stack.