I'm just currently writing my various routines. What I am doing is at every routine that say needs blending, I enable blending at the beginning and disable it at the end. Is this a bad thing?
For example:
Public Sub DrawQuad()
GL.Enable(EnableCap.Blend)
GL.BlendFunc(BlendingFactorSrc.SrcAlpha, BlendingFactorDest.OneMinusSrcAlpha)
GL.Begin()
GL.End()
GL.Disable(EnableCap.Blend)
End Sub
I have this, but I might call this 500 times in the same go, which means blending will get enabled/disabled 500 times. Will this greatly effect performance compared to just enabling once, drawing 500 quads and than disabling?
Premature optimization is the root of all evil!
As bcrist pointed out in the comments, if you're looking to optimize your OpenGL code, do learn the programmable pipeline. It's faster, more customizable, less reliant on magical function calls, and generally more awesome.
In general, optimize your big problems first, then get specific.
The best way to find out the answer to your question is to profile your code. OpenGL drivers are implemented by the hardware vendors for each device. Yes, calling any function repeatedly when you could call it just once will cause a performance hit, but how does that stack up against the rest of your code?
The take home point is admittedly annoying - your question is misguided and the "answer" isn't going to answer your actual question about calls to glEnable. You should optimize many things before you start looking at small functions like that.
For example, once you get the rest of your design sorted out and you realize that grouping together certain drawing routines by glEnable is even possible in your program, much less doable without incurring more overhead than it eliminates, then you might look into doing something to get rid of them.
Related
I now have sufficent exposure to the Objective-C that if i'm stuck with anything, I know how to think of the problem in terms of a likely tool I need and go look for it. Simple really. There's A Method For That. So nothings a real problem anymore.
Now I'm looking deeper at the language in broader terms. We write stuff. The compiler hews out all the code to execute it. From a simple flashlight app thats a if/then decision to turn on, to a highly complex accelerometer driven 3D shoot 'em up with blood 'n guts and body parts following all sorts of physics, the compiler prepares the code ready to be executed like a giant railway layout. No matter how random it appears on the screen, everything possible can be generically described and prepared for.
So here's the question:
Are there cases where something completely unexpected to the software designer can still be handled without an execution halt? Maybe I'd better re-frame the question a few different ways: Can a ( objective-C ) program meta-compile within itself in response to an unplanned-for user request? or to re-put my opening remark, are there tools or methods for unlikely descriptions of unlikely problems?
I think #kfb has the right comment about metaprogramming. Check out the Runtime docs in conjunction with metaprogramming tutorials.
Parts of your last question might be in the realm of this doc.
If your looking for ways to reduce the size of your code base for the lesser used features, one idea might be to make the features internet based (assuming connectivity is not a problem).
In advance apologize if the question seems somewhat broad or strange, I don't mean to offend anyone, but maybe someone can actually make a recommendation. I tried looking for the similar questions, but cold not.
Which are the better resources (books, blogs etc.) that can teach about optimizing code?
There is quite a few resources on making code more human-readable (Code Complete being number one choice probably). But what about making it run faster, more memory-efficient?
Of course there are lots of books on each particular language, but I wonder if there are some that cover the problems of memory / speed of operations and are somewhat language-independent?
Here are some links that might be helpful in general on the subject of memory optimizations
What Every Programmer Should Know About Memory by Ulrich Drepper
Herb Sutter: The Free Lunch Is Over: A Fundamental Turn Toward Concurrency in Software
Slides: Herb Sutter: Machine Architecture (Things Your Programming Language Never Told You)
Video: Herb Sutter # NWCPP: Machine Architecture: Things Your Programming Language Never Told You
The microarchitecture of Intel, AMD and VIA CPUs
An optimization guide for assembly programmers and compiler makers, by Agner Fog
Read Structured Programming with go to Statements. While it's the source of the "premature optimisation is the source of all evil" quote that comes up the moment somebody wants to make anything faster or smaller - no matter how desperately important or late in the process they are - it's actually about the importance of making things efficient when you can.
Learn about time complexity, space complexity and the analysis of algorithms.
Come up with examples where you would want to sacrifice having worse space complexity for better time complexity, and vice versa.
Know the time and space complexities of the algorithms and data structures your languages and frameworks of choice offer, especially those you use most often.
Read the answers on this site on questions about creating a good hash code.
Study the approach HTTP took to having the advantage of caching, without the disadvantage of using stale data inappropriately. Consider how easy or difficult that is to apply to in-memory caches. Consider when you would say "screw it, I can live with being stale for the speed boost it gives me". Consider when you would say "screw it, I can live with being slow for the guarantee of freshness it gives me".
Learn how to multithread. Learn when it improves performance. Learn why it often doesn't or even makes things worse.
Look at a lot of Joe Duffy's blog where performance is a regular concern of his writing.
Learn how to process items as streams or iterations rather than building and rebuilding data-structures full of each item, each time. Learn when you're actually better off not doing that.
Know what things cost. You can't reasonably decide "I'll work so this is in the CPU cache rather than main-memory/main-memory rather than disk/disk rather than over a network" unless you've a good idea what actually causes each to be hit, and what the cost differences are. Worse, you can't dismiss something as premature optimisation if you don't know what they cost - not bothering to optimise something is often the best choice, but if you don't even consider it in passing you aren't "avoiding premature optimisation", you're muddling through and hoping it works.
Learn a bit about what optimisations are done for you by the script engine/jitter/compiler/etc you use. Learn how to work with them rather than against them. Learn not to re-do work it'll do for you anyway. In one or two cases, you may also be able to apply the same general principle to your work.
Search for cases on this site where something is dismissed as an implementation detail - yes, all of those are cases where the detail in question isn't the most important thing at the time, but all of those implementation details were chosen for a reason. Learn what they were. Learn the counter-arguments.
Edit (I'll keep adding a few more to this as I go):
Different books of course differ in the emphasis they put on efficiency concerns, but I remember Stroustrup's The C++ Programming Language as one where there were a good few times where he will explain a choice between a few different options as relating to efficiency, and also on how to not have decisions made for efficiency's sake impact on the usability of the classes "from the outside".
Which brings me to another point. Concentrate on the efficiency of the library code you reuse in different projects. You don't want to ever be thinking "maybe I should hand-roll a new one here to be more efficient", unless it's a very specialised case, you want to be confident that lots of work went into making that heavily used class efficient over a lot of case, and concentrate on identifying hot-spots.
As for specialised cases, some of the more obscure data structures are worth knowing for the cases they serve. For example, a DAWG is a very compact structure for storing strings with a lot of common prefixes and suffixes (which would be most words in most natural languages) where you just want to find those in the list that match a pattern. If you need a "payload" then a tree where each letter has a list of nodes for each subsequent letter (a generalisation of a DAWG but ending in that "payload" rather than the terminal node) has some but not all of the advantages. They also find the result in O(n) time where n is the length of the string sought.
How often will that come up? Not many. It came up for me once (a few times really, but they were variants of the same case), and as such it would not have been worth it for me to learn all there was to know about DAWGs until then. But I knew enough to know it was what I needed to research later, and it saved me gigabytes (really, from way too much for a machine with 16GB RAM to cope with, to less than 1.5GB). Going straight for a hand-rolled DAWG would totally be premature optimisation rather than putting the strings in a hashset, but flicking through the NIST datastructure site meant I could when it came up.
Consider: "Finding a string in a DAWG is O(n)" "Finding a string in a Hashset is O(1)" Both of these statements is true, but the speed of the two tends to be comparable. Why? Because the DAWG is O(n) in terms of the length of the string, and effectively O(1) in terms of the size of the DAWG. The Hashset is O(1) in terms of the size of the hashset, but working out the hash is typically O(n) in terms of the length of the string, and equality checks are also O(n) in terms of that length. Both statements were correct, but they were thinking about a different n! You always need to know what n means in any discussion of time and space complexity - most often it'll be the size of the structure, but not always.
Don't forget constant effects: O(n²) is the same as O(1) for sufficiently low values of n! Remember that the likes of O(n²) translates as n²*k + n * k₁ + k₂, with the assumption that k₁ & k₂ are low enough and k and the k of another algorithm or structure we are comparing of are close enough, that they don't really matter and it's only n² that we care about. This isn't true all the time, and we can sometimes find that k, k₁ or k₂ are high enough that we end up in trouble. It's also not true when n is going to be so small as to make the difference in the constant costs of different approaches matter. Of course normally when n is small we don't have a big efficiency concern, but what if we are doing m operations on structures averaging n in size, and m is large. If we are choosing between an O(1) and a O(n²) approach, we are choosing between an O(m) and O(n²m) approach overall. It still seems like a no-brainer in favour of the former, but with a low n it essentially becomes a choice between two different O(m) approaches, and the constant factors are much more important.
Learn about lock-free multi-threading. Or perhaps don't. Personally, I've two pieces of my own code I use professionally that use all but the simplest lock-free techniques. One is based on well-known approaches and I wouldn't bother now (it's .NET code first written for .NET2.0 and the .NET4.0 library supplies a class that does the same thing). The other I first wrote for fun, and only used after that just-for-fun period had given me something reliable (and it still gets beaten by something in the 4.0 library for a lot of cases, but not for some others that I care about). I would hate to have to write something like it with a deadline and a client in mind.
All that said, if you're coding out of interest, the challenges involved are interesting and it's an enjoyable thing to work with when you've the freedom to give up on a failed plan that you don't get when you're doing something for a paying client, and you'll certainly learn a lot about efficiency concerns generally. (Take a look at https://github.com/hackcraft/Ariadne if you want to see some of what I've done with this).
A Case Study
Actually, that contains a relatively good example of some of the above principles. Take a look at the method that's currently at line 511 at https://github.com/hackcraft/Ariadne/blob/master/Collections/ThreadSafeDictionary.cs (where I joke in the comments about it being flame-bait for people quoting Dijkstra. Let's use it as a case-study:
This method was first written to use recursion, because it's a naturally recursive problem - after doing the operation on the current table, if there's a "next" table we want to do the exact same operation on that, and so on until there's no further table.
Recursion is almost always slower than iteration, for a few different methods. Should we make all recursive calls iterative? No, it's often not worth it, and recursion is a wonderful way to write code that is clear about what it's doing. Here though I apply the principle above that since this is a library that might be called where performance is crucial, particular effort should be extended on it.
The decision to try to improve its speed being made, the next thing I did was make measurements. I don't depend on "I know that iteration is faster than recursion, so it must be faster when changed to avoid recursion". That's just not true - a poorly written iterative version may not be as good as a well-written recursive version.
The next question is, just how to re-write it. I've a tested method that I know works and I'm going to replace it with a different version. I don't want to replace it with a version that doesn't work, obviously, so how to re-write while taking the most advantage out of what's already there?
Well, I know about tail-call elimination; an optimisation normally done by compilers that changes the way the stack is managed so that recursive functions end up with properties closer to those of iterative (it's still recursive from the perspective of the source code, but it's iterative in terms of how the compiled code actually uses the stack).
This gives me two things to think about: 1. Maybe the compiler is already doing this, in which case my extra work isn't going to do anything to help. 2. If the compiler isn't already doing this, I can take the same basic approach manually.
That decision made, I replaced all of the points where the method called itself, with a change to the one parameter that would be different for that next call, and then go back to the beginning. I.e. instead of having:
CurrentMethod(param0.next, param1, param2, /*...*/);
We have:
param0 = param0.next;
goto startOfMethod;
That being done, I measure again. Running through the entire unit tests for the class is now consistently 13% faster than before. If it were closer I'd have tried more detail measurements, but a consistent 13% on runs that includes code that doesn't even call this method is something I'm pretty happy with. (It also tells me that the compiler wasn't doing the same optimisation, or I wouldn't have gained anything).
Then I clean up the method to make more changes that make sense with the new code. Most of them let me take out the goto because goto is indeed nasty (and there's other places the same optimisation was done that aren't as obvious because the goto was refactored entirely). In some, I left it in, because 13% is worth breaking the no-goto rule to my mind!
So the above gives an example of:
Deciding where to concentrate optimisation effort (based on how often it might be hit and my inability to predict all uses of the library)
Using knowledge of general costs (recursion costs more than iteration, most of the time).
Measuring rather than depending on assuming the above always applies.
Learning from what compilers do.
Understanding that because of that I may not gain anything - maybe the compiler already did it for me.
Avoiding optimisations leading to unreadable code (refactoring out most of the gotos the first pass introduced).
Some of these are matters of opinion and style (the decision to leave in some goto would not be without controversy), and it's certainly okay to disagree with my decisions, but knowledge of the points raised so far in this post would make it an informed disagreement, rather than a knee-jerk one.
In addition to the resources mentioned in other answers, Michael Abrash's Graphics Programming Black Book is a great read for learning about optimization. While the specifics are a bit dated in places, it is still a great resource for learning about how to approach optimization.
Any time you want to optimize code it is absolutely essential to measure, measure, measure. One of the best ways to learn about optimization is by doing - take some code you want to optimize, learn how to use a profiler to measure its performance and then make changes and measure the results.
In other words, do you spend time anticipating errors and writing code to get around these potential issues, or do you write the code as you see fit and then work through any errors on an issue by issue basis?
I've been thinking a lot about this lately and I'm very much a reactive person. I write my code, give it a whirl, go back correct error and repeat until application works as expected. However a friend of mine offered that he spends time thinking how each line is interpreted and fixes errors before they occur.
I must point out that re-active is pure PRE-live. I definitely make sure my application is working before it goes live.
There should always be a balance.
Too many error checking is slow and leads to garbage code. Not enough error checking makes your program crash on edge cases which is not very good to discover after having it shipped.
So you decide how reliable some piece of code should be and implement error checking accordingly. Some test utility can be not very reliable - less error checking. A COM server meant to be used by a third party search service in deep background should be super reliable - much more error checking.
I think asking this in isolation is kinda weird, and very subjective, however there are obviously a bunch of techniques that permit you to do each. I tend to use these two:
Test-driven development (this would seem to be proactive)
Strong, static typing (reactive, but part of a tight iterative development cycle, as in, it's enforced by my ML compiler, and I compile a lot)
Very occasionally I swerve into the world of formal verification of programs. That's definitely "reactive", but if you think a little more up-front, it tends to make the verification easier.
I must also say that I value a lot of up-front thought in programming. The easiest way to avoid bugs is to not write them in the first place. Sometimes it's inevitable, but often a little more time spent thinking about the problem can lead to better-quality solutions, and then the rest can be taken care of using the kinds of automated methods I talked about above.
I usually ask myself a bunch of what-ifs when coding, like
The user clicks the button, what if they didn't select a date?
The user is typing in the search box, what if they try to type html in there?
My label text depends on a value from a shared drive, what if it's not mapped?
and so on. By doing this I've found that when the application does go live, there are a ton fewer errors and I can focus on fixing more obscure bugs instead of correcting conditions that should have been in place to begin with.
I live by a simple principle when considering error-handling: garbage in, garbage out. If you don't want any garbage (e.g. invalid input) messing up your software, you have to find all the points in your software where it can get in and handle it. Of course, the more complicated your software is, the harder it is to find every point of entry, but I feel that the more you do up front the less reactive you will need to be later on.
I advocate the proactive approach.
I try to write the code in that style which results in maintainable and reliable code
I use the defensive programming techniques to prevent stupid errors in code due to my loss of attention and similar
I design the database model according to the fortress principle, SQL code checking for results after each singular operation
I think of potential problems that can happen with that part of the code and I account for that. Not for every possibility but for major ones I can think of right now.
This usually results in software operating rather smoothly. At times it even surprises me but that was the intended goal, so here we are.
IMHO, the word "Error" (or its loose synonym "bug") itself means that it is a program behavior that was not foreseen.
I usually try to design with all possible scenarios in mind. Of course, it is usually not possible to think of all possible cases. But thinking through and allowing for as many scenarios as possible is usually better than just getting something working as soon as possible. This saves a lot of time and effort debugging and redesigning the code. I often sit down with pen and paper for even the smallest of programing tasks before actually typing any code into my editor.
As I said, this will not eliminate all errors. For me it pays off many times over in terms of time spent debugging. Another benefit is that it results in a more solid and maintainable design with fewer bugfixing hacks and special cases added on later. But in any case, you will have to do a lot of debugging after the code is done.
This does not apply when all you want is a mockup or rapid prototype. Also practical constraints such as deadlines often makes a thorough evaluation difficult or impossible.
What kind of programming? It's impossible to answer this in any general way. (It's like asking "do you wear a helmet when playing?" -- well, playing what?)
At work, I'm working on a database-backed website. The requirements are strict, and if I don't anticipate how users will screw it up, I'm going to get a call at some odd hour of the day to fix it.
At home, I'm working on a program ... I don't even know what it'll do yet. I can't deal with 'errors' because I don't know what 'an error' is in this context, because I don't know what correct behavior is going to be. The entire purpose of the program can and frequently does change on a timescale of minutes to hours, so even a couple minutes spent thinking about errors this early is a complete waste of time. (It's even worse than browsing SO, since error-handling adds lines of code.)
I guess the only general answer is "I do what makes sense in terms of saving time in the long term", which is, after all, the whole reason to use machines to do work for us.
Is it a good practice to follow optimization techniques during initial coding itself or should one concentrate purely on realization of functionality first?
If one concentrates purely on functionality during initial coding, then how easy or difficult is it to take care of optimization later on?
Optimise your design and architecture - don't lock yourself into a design which will never scale - but don't micro-optimise your implementation. In particular, don't sacrifice simplicity and readability for micro-optimised implementation... at least not without benchmarking your code (ideally your whole system) first.
Measurement really is the key point when it comes to performance. Bottlenecks are almost never where you expect them to be. There are loads of different ways of measuring; optimisation without any measurement is futile IMO.
Donald Knuth said:
We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil
It depends what you see as "optimization". Micro-optimization should not be done in early stages, and afterwards only if you have a valid reason to do so (e.g. profiler results or similar).
However, writing well-structured, clean code following best practices and common coding guidelines is a good habit, and once you're used to it, it doesn't take much more time than writing sloppy code. This kind of "optimization" (not the correct word for it, but some see it as such) should be done from the beginning.
See http://en.wikipedia.org/wiki/Program_optimization for quotes by Knuth.
If you believe that optimization might make your code harder to (a) get right in the first place, or (b) maintain in the long run, then it's probably best to get it right first. Having good development processes, such as Test Driven Development, can help you make optimisations later.
It's always better to have it work right and slow, than wrong and fast.
Rightly said by Donald Knuth "Premature optimization is the the root of all evil " , and it makes your coding speed slow. The best way to optimize is by visiting the codebase again and refactoring. This way you know which part of the code is often used or is a bottleneck and should be fine tuned.
Premature optimization is not a good thing.
And that goes especially for low level optimization. But at a higher level your design shouldn't lock out any future optimization.
For example.
The retrieval of collections should be hidden behind methods call, in the end you can always decide to cache the retrieval of collections or not.
After you have a stable application and(!) you have developed regression unit tests. You can profile the application and optimize the hotspots. And remember to after every optimalization step you should run your complete unit test set.
Is it a good practice to follow optimization techniques during initial coding itself or should one concentrate purely on realization of functionality first?
If you know performance is critical (or important), consider it in your design and write it correctly the first time. If you don't also consider this in your design and it is important, you are wasting time or "developing a proof of concept".
Part of this comes down to experience; If you know optimizations and your program's problem areas or have already implemented similar functionalities in the past, your experience will certainly help you create an implementation closer to the end result the first time. If you still need a proof of concept, you should not be writing the actual program until that's completed -- kick out some tests to determine what solution is appropriate for the problem, then implement it properly.
If one concentrates purely on functionality during initial coding, then how easy or difficult is it to take care of optimization later on?
Some fixes are quick, others deserve complete rewrites. The more that needs to change and adapt after the fact, the more time you waste re-testing and maintaining a poorly implemented program. The libraries that are easiest to maintain and sustain the demands are typically the ones which the engineer had an understanding of what design is ideal, and strived to meet that ideal during initial implementation.
Of course, that also assumes you favor a long-lived program!
Premature optimization is the the root of all evil
To elaborate more on this famous quote, doing optimization early has the disadvantage of distracting you from doing a good design. Also, programmers are notoriously bad in finding which parts of the code cause the more trouble, and so try hard to optimize things that aren't that important. You should always measure first to find out what needs to be optimized and this can only happen in later phases.
Actually I am making a major project in implementing compiler optimization techniques. I already know about the existing techniques, but I am confused what technique to choose and how to implement it.
G'day,
What area of optimization are you talking about?
Compiler optimizations such as:
loop optimizations
dataflow optimizations
static single assignment based optimizations
code generator optimizations
etc.
etc.
Or optimization in the performance of the compiler itself, i.e. the speed with which it works?
Assuming that you have a compiler to optimize, and if it wasn't written by you, look up the documentation to see what is missing. Otherwise, if it was written by you, you can start off with the simplest. The definition for the simplest will depend on the language your compiler consumes. Or am I missing something?
I think you may have over optimized your question . Are you trying to decide where to start or trying to decide if some optimizations are worth implementing and others are not? I would assume all of the existing techniques have a place and are useful depending on the code they come across. If you are deciding which one to do first, pick the one you can do and do it. Pick the low hanging fruit. Get a few wins in your back pocket before you tackle a tough one and stumble and get frustrated. I would assume the real trick is having all the optimizations there and working but coming up with a way to decide which ones produce something better for a particular program and which ones get in the way and make things worse.
IMHO, the thing to do is implement the simple, obvious optimizations and then let it rest. Certainly it is very interesting to try to do weird and wonderful optimizations to rectify things that the user could simply have coded a little better, but if you really want to try to clean up after poor coding or poor design, the user can always outrun you. This is my favorite example.
My favorite example of compiler-optimizations-gone-nuts is Fortran compilers, where they go to such lengths to scramble code to shave a few hypothetical cycles that the code is almost impossible to debug, and typically the program counter is in there less than 1% of the time, so the effort is wasted.