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When is a function too long? [closed]
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Closed 5 years ago.
How small should I be making functions? For example, if I have a cake baking program.
bakeCake(){
if(cakeType == "chocolate")
fetchIngredients("chocolate")
else
if(cakeType == "plain")
fetchIngredients("plain")
else
if(cakeType == "Red velvet")
fetchIngredients("Red Velvet")
//Rest of program
My question is, while this stuff is simple enough on its own, when I add much more stuff to the bakeCake function it becomes cluttered. But lets say that this program has to bake thousands of cakes per second. From what I've heard, it takes significantly longer (relative to computer time) to use another function compared to just doing the statements in the current function. So something that's similar like this should be very easy to read, and if efficiency is important wouldn't I want to keep it in there?
Basically, at what point do I sacrifice readability for efficiency. And a quick bonus question, at what point does having too many functions decrease readability? Here's an example of Apple's swift tutorial.
func isCandyAmountAcceptable(bandMemberCount: Int, candyCount: Int) -> Bool {
return candyCount % bandMemberCount == 0
They said that because the function name isCandyAmountAcceptable was easier to read than candyCount % bandMemberCount == 0 that it'd be good to make a function for that. But from my perspective it may take a few seconds to figure out what the second option is saying, but it's also more readable when ti comes to knowing how it works.
Sorry about being all over the place and kinda asking 2 questions in one. Just to summarize my questions:
Does using functions extraneously make efficiency (speed) suffer? If it does how can I figure out what the cutoff between readability and efficiency is?
How small and simple should I make functions for? Obviously I'd make them if I ever have to repeat the function, but what about one time use functions?
Thanks guys, sorry if these questions are ignorant or anything but I'd really appreciate an answer.
Does using functions extraneously make efficiency (speed) suffer? If
it does how can I figure out what the cutoff between readability and
efficiency is?
For performance I would generally not factor in any overhead of direct function calls against any decent optimizer, since it can even make those come free of charge. When it doesn't, it's still a negligible overhead in, say, 99.9% of scenarios. That applies even for performance-critical fields. I work in areas like raytracing, mesh processing, and image processing and still the cost of a function call is typically on the bottom of the priority list as opposed to, say, locality of reference, efficient data structures, parallelization, and vectorization. Even when you're micro-optimizing, there are much bigger priorities than the cost of a direct function call, and even when you're micro-optimizing, you often want to leave a lot of the optimization for your optimizer to perform instead of trying to fight against it and do it all by hand (unless you're actually writing assembly code).
Of course with some compilers you might deal with ones that never inline function calls and have a bit of an overhead to every function call. But in that case I'd still say it's relatively negligible since you probably shouldn't be worrying about such micro-level optimizations when using those languages and interpreters/compilers. Even then it will probably often be bottom on the priority list, relatively speaking, as opposed to more impactful things like improving locality of reference and thread efficiency.
It's like if you're using a compiler with very simplistic register allocation that has a stack spill for every single variable you use, that doesn't mean you should be trying to use and reuse as few variables as possible to work around its tendencies. It means reach for a new compiler in those cases where that's a non-negligible overhead (ex: write some C code into a dylib and use that for the most performance-critical parts), or focus on higher-level optimizations like making everything run in parallel.
How small and simple should I make functions for? Obviously I'd make
them if I ever have to repeat the function, but what about one time
use functions?
This is where I'm going to go slightly off-kilter and actually suggest you consider avoiding the teeniest of functions for maintainability reasons. This is admittedly a controversial opinion although at least John Carmack seems to somewhat agree (specifically in respect to inlining code and avoiding excess function calls for cases where side effects occur to make the side effects easier to comprehend).
However, if you are going to make a lot of state changes, having them
all happen inline does have advantages; you should be made constantly
aware of the full horror of what you are doing.
The reason I believe it can sometimes be good to err on the side of meatier functions is because there's often more to comprehend than that of a simple function to understand all the information necessary to make a change or fix a problem.
Which is simpler to comprehend, a function whose logic consists of 80 lines of inlined code, or one distributed across a couple dozen functions and possibly ones that lead to disparate places throughout the codebase?
The answer is not so clear cut. Naturally if the teeny functions are used widely, like say sqrt or abs, then the reader can simply skim over the function call, knowing full well what it does like the back of his hand. But if there are a lot of teeny exotic functions that are only used one time, then the ability to comprehend the operation as a whole requires looking them up and understanding what they all individually do before you can get a proper comprehension of what's going on in terms of the big picture.
I actually disagree with that Apple Swift tutorial somewhat with that one-liner function because while it is easier to understand than figuring out what the arithmetic and comparison are supposed to do, in exchange it might require looking it up to see what it does in scenarios where you can't just say, isCandyAmountAcceptable is enough information for me and need to figure out exactly what makes an amount acceptable. Instead I would actually prefer a simple comment:
// Determine if candy amount is acceptable.
if (candyCount % bandMemberCount == 0)
...
... because then you don't have to jump to disparate places in code (the analogy of a book referring its reader to other pages in the book causing the readers to constantly have to flip back and forth between pages) to figure that out. Of course the idea behind this isCandyAmountAcceptable kind of function is that you shouldn't have to be concerned with such details about what makes a candy amount of acceptable, but too often in practice, we do end up having to understand the details more often than we optimally should to debug the code or make changes to it. If the code never needs to be debugged or changed, then it doesn't really matter how it's written. It could even be written in binary code for all we care. But if it's written to be maintained, as in debugged and changed in the future, then sometimes it is helpful to avoid making the readers have to jump through lots of hoops. The details do often matter in those scenarios.
So sometimes it doesn't help to understand the big picture by fragmenting it into the teeniest of puzzle pieces. It's a balancing act, but certain types of developers can err on the side of overly dicing up their systems into the most granular bits and pieces and finding maintenance problems that way. Those types are still often promising engineers -- they just have to find their balance. The other extreme is the one that writes 500-line functions and doesn't even consider refactoring -- those are kinda hopeless. But I think you fit in the former category, and for you, I'd actually suggest erring on the side of meatier functions ever-so-slightly just to keep the puzzle pieces a healthy size (not too small, not too big).
There's even a balancing act I see between code duplication and minimizing dependencies. An image library doesn't necessarily become easier to comprehend by shaving off a few dozen lines of duplicated math code if the exchange is a dependency to a complex math library with 800,000 lines of code and an epic manual on how to use it. In such cases, the image library might very well be easier to comprehend as well as use and deploy in new projects if it chooses instead to duplicate a few math functions here and there to avoid external dependencies, isolating its complexity instead of distributing it elsewhere.
Basically, at what point do I sacrifice readability for efficiency.
As stated above, I don't think readability of the small picture and comprehensibility of the big picture are synonymous. It can be really easy to read a two-line function and know what it does and still be miles away from understanding what you need to understand to make the necessary changes. Having many of those teeny one-shot two-liners can even delay the ability to comprehend the big picture.
But if I use "comprehensibility vs. efficiency" instead, I'd say upfront at the design-level for cases where you anticipate processing huge inputs. As an example, a video processing application with custom filters knows it's going to be looping over millions of pixels many times per frame. That knowledge should be utilized to come up with an efficient design for looping over millions of pixels repeatedly. But that's with respect to design -- towards the central aspects of the system that many other places will depend upon because big central design changes are too costly to apply late in hindsight.
That doesn't mean it has to start applying hard-to-understand SIMD code right off the bat. That's an implementation detail provided the design leaves enough breathing room to explore such an optimization in hindsight. Such a design would imply abstracting at the Image level, at the level of a million+ pixels, not at the level of a single IPixel. That's the worthy thing to take into consideration upfront.
Then later on, you can optimize hotspots and potentially use some difficult-to-understand algorithms and micro-optimizations here and there for those truly critical cases where there's a strong perceived business need for the operation to go faster, and hopefully with good tools (profilers, i.e.) in hand. The user cases guide you about what operations to optimize based on what the users do most often and find a strong desire to spend less time waiting. The profiler guides you about precisely what parts of the code involved in that operation need to be optimized.
Readability, performance and maintainability are three different things. Readability will make your code look simple and understandable, not necessarily best way to go. Performance is always going to be important, unless you are running this code in non-production environment where end result is more important than how it was achieved. Enter the world of enterprise applications, maintainability suddenly gains lot more importance. What you work on today will be handed over to somebody else after 6 months and they will be fixing/changing your code. This is why suddenly standard design patterns become so important. In a way, the readability is part of maintainability on larger scale. If the cake baking program above is something more complex than what its looking like, first thing stands out as a code smell is existence if if-else. Its gotta get replaced with polymorphism. Same goes with switch case kind of construct.
At what point do you decide to sacrifice one for other? That purely depends upon what business your code is achieving. Is it academic? Its got to be the perfect solution even if it means 90% devs struggle to figure out at first glance what the hell is happening. Is it a website belonging to retail store being maintained by distributed team of 50 devs working from 2 or more different geographic locations? Follow the conventional design patterns.
A rule of thumb I have always seen being followed in almost all situations is that if a function is growing beyond half the screen, its a candidate for refactoring. Do you have functions that end up you having your editor long length scroll bars? Refactor!!!
I want to make an application that plots mathematical functions, I'd like to know the best language for it. it should have the following features:
An area to draw the function.
Supports anti-aliasing.
A scroll bar to change other dependent variables (which is a in y=(x-a)*x).
It should be fast enough (calculations will be done hundreds of times).
Parsing mathematical expressions using regex (Is there a better way?).
any other suggestions would be useful.
edit: this can be useful in many ways such as discarding repeated calculations
ex: plotting y=4+1 using 1000 points have 999 repeated calculation, performance can be enhanced using a tree model that recalculates nodes with changed children only
Regex will not do for parsing math expressions.
Personally, I write recursive-descent parsers. You might be surprised how easy and flexible it is.
If you want the output to look like it's varying continuously, when it isn't actually, what I do is not paint to the output window.
Rather I paint to a memory bitmap, which I then block-transfer to the visible window.
This eliminates all flashing, and makes it look fast even if it's only actually being repainted a few times per second.
Remember, your time-hog is much more likely to be painting, not calculating, so don't waste time trying to figure out how to optimize the calculation.
As far as a "best language", it depends what you're trying to do.
I've done all this in C, C++, and C#.
I'm sure Java or other compiled languages would work just as well.
I think there isn't a "best language" for it, however I can give you some hints. I think one way would be to use C++ with gnuplot library. Another way would be to use C++ with Qt and qwt libraries. Qt will easily manage regex too.
The latest is a solution I've personally used in my past work and there aren't particular problems, while the first is only a theoretic idea.
I am using (in Matlab) a global statement inside an if command, so that I import the global variable into the local namespace only if it is really needed.
The code analyzer warns me that "global could be very inefficient unless it is a top-level statement in its function". Thinking about possible internal implementation, I find this restriction very strange and unusual. I am thinking about two possibilities:
What this warning really means is "global is very inefficient of its own, so don't use it in a loop". In particular, using it inside an if, like I'm doing, is perfectly safe, and the warning is issued wrongly (and poorly worded)
The warning is correct; Matlab uses some really unusual variable loading mechanism in the background, so it is really much slower to import global variables inside an if statement. In this case, I'd like to have a hint or a pointer to how this stuff really works, because I am interested and it seems to be important if I want to write efficient code in future.
Which one of these two explanations is correct? (or maybe neither is?)
Thanks in advance.
EDIT: to make it clearer: I know that global is slow (and apparently I can't avoid using it, as it is a design decision of an old library I am using); what I am asking is why the Matlab code analyzer complains about
if(foo==bar)
GLOBAL baz
baz=1;
else
do_other_stuff;
end
but not about
GLOBAL baz
if(foo==bar)
baz=1;
else
do_other_stuff;
end
I find it difficult to imagine a reason why the first should be slower than the second.
To supplement eykanals post, this technical note gives an explanation to why global is slow.
... when a function call involves global variables, performance is even more inhibited. This is because to look for global variables, MATLAB has to expand its search space to the outside of the current workspace. Furthermore, the reason a function call involving global variables appears a lot slower than the others is that MATLAB Accelerator does not optimize such a function call.
I do not know the answer, but I strongly suspect this has to do with how memory is allocated and shared at runtime.
Be that as it may, I recommend reading the following two entries on the Mathworks blogs by Loren and Doug:
Writing deployable code, the very first thing he writes in that post
Top 10 MATLAB code practices that make me cry, #2 on that list.
Long story short, global variables are almost never the way to go; there are many other ways to accomplish variable sharing - some of which she discusses - which are more efficient and less error-prone.
The answer from Walter Roberson here
http://mathworks.com/matlabcentral/answers/19316-global-could-be-very-inefficient#answer_25760
[...] This is not necessarily more work if not done in a top-level command, but people would tend to put the construct in a loop, or in multiple non-exclusive places in conditional structures. It is a lot easier for a person writing mlint warnings to not have to add clarifications like, "Unless you can prove those "global" will only be executed once, in which case it isn't less efficient but it is still bad form"
supports my option (1).
Fact(from Matlab 2014 up until Matlab 2016a, and not using parallell toolbox): often, the fastest code you can achieve with Matlab is by doing nested functions, sharing your variables between functions without passing them.
The step close to that, is using global variables, and splitting your project up into multiple files. This may pull down performance slightly, because (supposedly, although I have never seen it verified in any tests) Matlab incurs overhead by retrieving from the global workspace, and because there is some kind of problem (supposedly, although never seen any evidence of it) with the JIT acceleration.
Through my own testing, passing very large data matrices (hi-res images) between calls to functions, using nested functions or global variables are almost identical in performance.
The reason that you can get superior performance with global variables or nested functions, is because you can avoid having extra data copying that way. If you send a variable to function, Matlab does so by reference, but if you modify the variable in the function, Matlab makes a copy on the fly (copy-on-write). There is no way I know of to avoid that in Matlab, except by nested functions and global variables. Any small drain you get from hinderance to JIT or global fetch times, is totally gained by avoiding this extra data copying, (when using larger data).
This may have changed with never versions of Matlab, but from what i hear from friends, I doubt it. I cant submit any test, dont have a Matlab license anymore.
As proof, look no further then this toolbox of video processing i made back in the day I was working with Matlab. It is horribly ugly under the hood, because I had no way of getting performance without globals.
This fact about Matlab (that global variables is the most optimized way you can code when you need to modify large data in different functions), is an indication that the language and/or interpreter needs to be updated.
Instead, Matlab could use a better, more dynamic notion of workspace. But nothing I have seen indicates this will ever happen. Especially when you see the community of users seemingly ignore the facts, and push forward oppions without any basis: such as using globals in Matlab are slow.
They are not.
That said, you shouldnt use globals, ever. If you are forced to do real time video processing in pure Matlab, and you find you have no other option then using globals to reach performance, you should get the hint and change language. Its time to get into higher performance languages.... and also maybe write an occasional rant on stack overflow, in hopes that Matlab can get improved by swaying the oppinions of its users.
I'm looking make use of the advantages of parallel programming in linq by using plinq, im not sure I understand the use entirely apart from the fact its going to make use of all cpu cores more efficiently so for a large query it might be quicker. Can I just simply call AsParallel() on linq calls to make use of th eplinq functionality and it will always be quicker? Or should I only use it when there is a lot of data to query or process?
You can't just assume that execution in parallel is always faster. It depends. In some situations you will gain a lot on multi-core processors by doing things in parallel. In other cases, you will just slow the things down, since parallel loops have a small overheat over simple loops.
For example, see my other answer which explains why embedded parallel loops can be a disaster.
Now, the best way to know if it is a good idea to use parallel loop in a precise context is to test both parallel and not parallel implementations and to measure the time they take.
To further add to the answer, it also depends on your data. Going a little 'old school' for a moment you could go down the road of loop unrolling, using for instead of foreach and so on and so forth.
However, you really need to ensure you aren't micro-optimising. Depending on your data fetches and the size of data (certainly with paged data) then you can probably get away with not using it.
That's not to say that making your linq mult-core aware isn't cool. But be aware of the setup costs of doing something like that and so be able to weigh up the benefits against the complexities of maintaining and debugging that code.
If your algorithm is already top notch then looking at the plinq extensions, a map reduce mechanism or similar may be the way to go. But first check your algorithm and your overall benefits. Operating on the right kind of collection (etc) in the right kind of way will always bring its own benefits (and problems!).
What are you trying to solve?
I'd like to do some work with the nitty-gritties of computer imaging. I'm looking for a way to read single pixels of data, analyze them programatically, and change them. What is the best language to use for this (Python, c++, Java...)? What is the best fileformat?
I don't want any super fancy software/APIs... I'm looking for the bare basics.
If you need speed (you'll probably always want speed with image processing) you definitely have to work with raw pixel data.
Java has some real disadvantages as you cannot access memory directly which makes pixel access quite slow compared to accessing the memory directly.
C++ is definitely the language of choice for production use image processing. But you can, for example, also use C# as it allows for unsafe code in specific areas. (Take a look at the scan0 pointer property of the bitmapdata class.)
I've used C# successfully for image processing applications and they are definitely much faster than their java counterparts.
I would not use any scripting language or java for such a purpose.
It's very east to manipulate the large multi-dimensional or complex arrays of pixel information that are pictures using high-level languages such as Python. There's a library called PIL (the Python Imaging Library) that is quite useful and will let you do general filters and transformations (change the brightness, soften, desaturate, crop, etc) as well as manipulate the raw pixel data.
It is the easiest and simplest image library I've used to date and can be extended to do whatever it is you're interested in (edge detection in very little code, for example).
I studied Artificial Intelligence and Computer Vision, thus I know pretty well the kind of tools that are used in this field.
Basically: you can use whatever you want as long as you know how it works behind the scene.
Now depending on what you want to achieve, you can either use:
C language, but you will lose a lot of time in bugs checking and memory management when implementing your algorithms. So theoretically, this is the fastest language to do that kind of job, but if your algorithms are not computationnally efficient (in terms of complexity) or if you lose too much time in bugs checking, this is clearly not worth it. So I would advise to first implement your application in another language, and then later you can always optimize small parts of your code with C bindings.
Octave/MatLab: very efficient language, almost as much as C, and you can make very elegant and succinct algorithms. If you are into vectorization, matrix and linear operations, you should go with that. However, you won't be able to develop a whole application with this language, it's more focused on algorithms, but then you can always develop an interface using another language later.
Python: all-in-one elegant and accessible language, used in gigantically large scale applications such as Google and Facebook. You can do pretty much everything you want with Python, any kind of application. It will be perfectly adapted if you want to make a full application (with client interaction and all, not only algorithms), or if you want to quickly draft a prototype using existent libraries since Python has a very large set of high quality libraries, like OpenCV. However if you only want to make algorithms, you should better use Octave/MatLab.
The answer that was selected as a solution is very biaised, and you should be careful about this kind of archaic comment.
Nowadays, hardware is cheaper than wetware (humans), and thus, you should use languages where you will be able to produce results faster, even if it's at the cost of a few CPU cycles or memory space.
Also, a lot of people tends to think that as long as you implement your software in C/C++, you are making the Saint Graal of speedness: this is just not true. First, because algorithms complexity matters a lot more than the language you are using (a bad algorithm will never beat a better algorithm, even if implemented in the slowest language in the universe), and secondly because high-level languages are nowadays doing a lot of caching and speed optimization for you, and this can make your program run even faster than in C/C++.
Of course, you can always do everything of the above in C/C++, but how much of your time are you willing to waste to reinvent the wheel?
Not only will C/C++ be faster, but most of the image processing sample code you find out there will be in C as well, so it will be easier to incorporate things you find.
if you are looking to numerical work on your images (think matrix) and you into Python check out http://www.scipy.org/PyLab - this is basically the ability to do matlab in python, buddy of mine swears by it.
(This might not apply for the OP who only wanted the bare basics -- but now that the speed issue was brought up, I do need to write this, just for the record.)
If you really need speed, it's better to forget about working on the pixel-by-pixel level, and rather see whether the operations that you need to perform could be vectorized. For example, for your C/C++ code you could use the excellent Intel IPP library (no, I don't work for Intel).
It depends a little on what you're trying to do.
If runtime speed is your issue then c++ is the best way to go.
If speed of development is an issue, though, I would suggest looking at java. You said that you wanted low level manipulation of pixels, which java will do for you. But the other thing that might be an issue is the handling of the various file formats. Java does have some very nice APIs to deal with the reading and writing of various image formats to file (in particular the java2d library. You choose to ignore the higher levels of the API)
If you do go for the c++ option (or python come to think of it) I would again suggest the use of a library to get you over the startup issues of reading and writing files. I've previously had success with libgd
What language do you know the best? To me, this is the real question.
If you're going to spend months and months learning one particular language, then there's no real advantage in using Python or Java just for their (to be proven) development speed.
I'm particularly proficient in C++ and I think that for this particular task I can be as speedy as a Java programmer, for example. With the aid of some good library (OpenCV comes to mind) you can create anything you need in a matter of a couple of lines of C++ code, really.
Short answer: C++ and OpenCV
Short answer? I'd say C++, you have far more flexibility in manipulating raw chunks of memory than Python or Java.