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following the text book, I do measure performance whenever I try optimizing my code. Sometimes, however, the performance gain is rather small and I can't decisively decide whether I should implement that optimization.
For example, when a fix shortens an average response time of 100ms to 90ms under some conditions, should I implement that fix? What if it shortens 200ms to 190ms? How many condition should I try before I can conclude that it will be beneficial overall?
I guess it's not possible to give a straight forward answer to this, as it depends on too many things, but is there a good rule of thumb that I should follow? Are there any guideline/best-practices?
EDIT:Thanks for the great answers! I guess the moral of the story is, there is no easy way to tell whether you should, but there ARE guidelines that can aid that process.. Things you should consider, things you shouldn't do etc. This particular time I ended up implementing the fix, even though it made a few line of code into 20-30 lines of code. Because our app. is very performance critical, and it was a consistent 10% gain in various realistic cases.
I think the rule of thumb (at least for me) is two-fold:
"It matters if it matters"--in the business world, this generally means that it matters if the clients care. That is, if the end users will "notice" the difference between 100ms and 90ms (I'm not being facetious here), then it matters.
If "it matters," then you will want to test your code thoroughly against a realistic variety of use cases that are likely to arise or at least may arise. If an optimization speeds up code in 50% of cases, but actually runs slower than what you previously had the other 50% of the time, obviously, it may not be worth implementing.
Regarding point 1 above: by suggesting an end user of your software might "notice" a 10ms difference, I don't mean to suggest that they will actually visibly see a difference. But if your app runs on a server with millions of connections and every little speed increase takes a substantial load off the server, that might matter to the client running the server. Or if your app performs extremely time-critical work, this is another case where the result of a 10ms speedup might be noticeable, even if the speedup itself isn't.
The only sensible approach to your question is something along the lines of "when the benefit is large enough to warrant the time you invest in exploring, implementing and testing the optimization."
The "benefit is large enough" is extremely subjective. Can you or your employer sell more units of software if you make this change? Will your user base notice? Will it give you personal gratification to have the fastest-possible code? Which of those or similar questions apply is something only you can know.
By and large, most of the software I have written (in a 20+ year career) has been "fast enough" out of the box, and the code I cared to optimize presented itself as an obvious bottleneck to the end users: Queries taking a long time, scrolling too slow, that sort of thing.
Donald Knuth made the following two statements on optimization:
"We should forget about small
efficiencies, say about 97% of the
time: premature optimization is the
root of all evil" [2]
and
"In established engineering
disciplines a 12 % improvement, easily
obtained, is never considered marginal
and I believe the same viewpoint
should prevail in software
engineering"[5]
src: http://en.wikipedia.org/wiki/Program_optimization
Is the optimization obfuscating your code too much?
Do you really need an optimization? if your app just runs fine then readability of the code is probably more important
Did you work on the general design and algorithms of your application before trying small hacky optimizations?
You should focus optimisation efforts on the parts of code that account for the most runtime. If a particular piece of code takes up 80% of the total runtime, then optimising it to reduce the time is takes by 5% will have as much impact as reducing the time of the rest of the code by 20%.
In general, optimisations make code less readable (not always, but often). Therefore you should avoid optimising until you are sure that there is a problem.
If it speeds up your program at all, why not implement it? You have already done the work by creating the new implementation, so you are not doing extra work by applying the new implementation.
Unless the code is THAT much harder to understand.
Also, 100 ms to 90 ms is a 10% gain in performance. A 10% gain should not be taken lightly.
The real question is, if it only took 100 ms to run in the first place, what was the point in trying to optimize it?
As long as it's fast enough, then you don't need to optimise any more. But then, you wouldn't even bother profiling if that was the case...
If the performance gain is small, consider the other factors: maintainability, risk of making the change, understandability, etc. If it reduces the ability to maintain or understand the code, it probably isn't worth doing. If it improves those attributes, then it's more reason to implement the change.
In most cases, your time is more valuable than the computer's. If you think it'll take you half an hour longer to work out what the code is doing later (say if there's a bug in it), and it's only saved you a few seconds, ever, you're at a net loss.
It depends very much on the usage scenario. I'll assume here that the code in question has been profiled and thus it is known to be the bottleneck--i.e. not just "this could be faster", but "the program would give results/finish running faster if this were faster". In situations where this is not the case--e.g. if you spend 99% of your time waiting for more data to come over an ethernet connection--then you should care about correctness but not optimize for speed.
If you are writing a piece of user interface code, what you care about is perceived speed. Generally anything under ~100 ms is perceived as "instant"--no point speeding it up.
If you are writing a piece of code for a giant server farm, then if the cost of your salary to make the code fast is less than the cost of the extra electricity for the server farm, it's worthwhile. (But be sure to prioritize your time.)
If you are writing a piece of code that is used rarely or when unattended, as long as it completes in a semi-sane duration, don't worry about it. Install scripts tend to be of this sort (unless you start running into many minutes, at which point users might start abandoning the install because it's taking too long).
If you are writing code to automate a task for someone else, then if (your time spent coding + their time spent using the optimized code) is less than (their time spent using the slow code), it's worthwhile. If you're doing this in a commercial setting, weight this by your respective salaries.
If you are writing library code that will be used by many thousands of people, always make it faster if you have time to.
If you are under time pressure to simply have something working e.g. as a demo, don't optimize (except through sensible choice of algorithms from libraries) unless the result would be so slow that it isn't even "working".
One of the biggest annoyances for me personally is finding software which perhaps initially fell into one category and then later fell into another, but for which nobody went back to do needed optimizations. Until recently Javascript performance was a great example of this. Moral of the story is: don't just decide once; revisit the issue as the situation demands.
Its common to hear about "highly optimized code" or some developer needing to optimize theirs and whatnot. However, as a self-taught, new programmer I've never really understood what exactly do people mean when talking about such things.
Care to explain the general idea of it? Also, recommend some reading materials and really whatever you feel like saying on the matter. Feel free to rant and preach.
Optimize is a term we use lazily to mean "make something better in a certain way". We rarely "optimize" something - more, we just improve it until it meets our expectations.
Optimizations are changes we make in the hopes to optimize some part of the program. A fully optimized program usually means that the developer threw readability out the window and has recoded the algorithm in non-obvious ways to minimize "wall time". (It's not a requirement that "optimized code" be hard to read, it's just a trend.)
One can optimize for:
Memory consumption - Make a program or algorithm's runtime size smaller.
CPU consumption - Make the algorithm computationally less intensive.
Wall time - Do whatever it takes to make something faster
Readability - Instead of making your app better for the computer, you can make it easier for humans to read it.
Some common (and overly generalized) techniques to optimize code include:
Change the algorithm to improve performance characteristics. If you have an algorithm that takes O(n^2) time or space, try to replace that algorithm with one that takes O(n * log n).
To relieve memory consumption, go through the code and look for wasted memory. For example, if you have a string intensive app you can switch to using Substring References (where a reference contains a pointer to the string, plus indices to define its bounds) instead of allocating and copying memory from the original string.
To relieve CPU consumption, cache as many intermediate results if you can. For example, if you need to calculate the standard deviation of a set of data, save that single numerical result instead looping through the set each time you need to know the std dev.
I'll mostly rant with no practical advice.
Measure First. Optimization should be done to places where it matters. Highly optimized code is often difficult to maintain and a source of problems. In places where the code does not slow down execution anyway, I alwasy prefer maintainability to optimizations. Familiarize yourself with Profiling, both intrusive (instrumented) and non-intrusive (low overhead statistical). Learn to read a profiled stack, understand where the time inclusive/time exclusive is spent, why certain patterns show up and how to identify the trouble spots.
You can't fix what you cannot measure. Have your program report through some performance infrastructure the thing it does and the times it takes. I come from a Win32 background so I'm used to the Performance Counters and I'm extremely generous at sprinkling them all over my code. I even automatized the code to generate them.
And finally some words about optimizations. Most discussion about optimization I see focus on stuff any compiler will optimize for you for free. In my experience the greatest source of gains for 'highly optimized code' lies completely elsewhere: memory access. On modern architectures the CPU is idling most of the times, waiting for memory to be served into its pipelines. Between L1 and L2 cache misses, TLB misses, NUMA cross-node access and even GPF that must fetch the page from disk, the memory access pattern of a modern application is the single most important optimization one can make. I'm exaggerating slightly, of course there will be counter example work-loads that will not benefit memory access locality this techniques. But most application will. To be specific, what these techniques mean is simple: cluster your data in memory so that a single CPU can work an a tight memory range containing all it needs, no expensive referencing of memory outside your cache lines or your current page. In practice this can mean something as simple as accessing an array by rows rather than by columns.
I would recommend you read up the Alpha-Sort paper presented at the VLDB conference in 1995. This paper presented how cache sensitive algorithms designed specifically for modern CPU architectures can blow out of the water the old previous benchmarks:
We argue that modern architectures
require algorithm designers to
re-examine their use of the memory
hierarchy. AlphaSort uses clustered
data structures to get good cache
locality...
The general idea is that when you create your source tree in the compilation phase, before generating the code by parsing it, you do an additional step (optimization) where, based on certain heuristics, you collapse branches together, delete branches that aren't used or add extra nodes for temporary variables that are used multiple times.
Think of stuff like this piece of code:
a=(b+c)*3-(b+c)
which gets translated into
-
* +
+ 3 b c
b c
To a parser it would be obvious that the + node with its 2 descendants are identical, so they would be merged into a temp variable, t, and the tree would be rewritten:
-
* t
t 3
Now an even better parser would see that since t is an integer, the tree could be further simplified to:
*
t 2
and the intermediary code that you'd run your code generation step on would finally be
int t=b+c;
a=t*2;
with t marked as a register variable, which is exactly what would be written for assembly.
One final note: you can optimize for more than just run time speed. You can also optimize for memory consumption, which is the opposite. Where unrolling loops and creating temporary copies would help speed up your code, they would also use more memory, so it's a trade off on what your goal is.
Here is an example of some optimization (fixing a poorly made decision) that I did recently. Its very basic, but I hope it illustrates that good gains can be made even from simple changes, and that 'optimization' isn't magic, its just about making the best decisions to accomplish the task at hand.
In an application I was working on there were several LinkedList data structures that were being used to hold various instances of foo.
When the application was in use it was very frequently checking to see if the LinkedListed contained object X. As the ammount of X's started to grow, I noticed that the application was performing more slowly than it should have been.
I ran an profiler, and realized that each 'myList.Contains(x)' call had O(N) because the list has to iterate through each item it contains until it reaches the end or finds a match. This was definitely not efficent.
So what did I do to optimize this code? I switched most of the LinkedList datastructures to HashSets, which can do a '.Contains(X)' call in O(1)- much better.
This is a good question.
Usually the best practice is 1) just write the code to do what you need it to do, 2) then deal with performance, but only if it's an issue. If the program is "fast enough" it's not an issue.
If the program is not fast enough (like it makes you wait) then try some performance tuning. Performance tuning is not like programming. In programming, you think first and then do something. In performance tuning, thinking first is a mistake, because that is guessing.
Don't guess what to fix; diagnose what the program is doing.
Everybody knows that, but mostly they do it anyway.
It is natural to say "Could be the problem is X, Y, or Z" but only the novice acts on guesses. The pro says "but I'm probably wrong".
There are different ways to diagnose performance problems.
The simplest is just to single-step through the program at the assembly-language level, and don't take any shortcuts. That way, if the program is doing unnecessary things, then you are doing the same things, and it will become painfully obvious.
Another is to get a profiling tool, and as others say, measure, measure, measure.
Personally I don't care for measuring. I think it's a fuzzy microscope for the purpose of pinpointing performance problems. I prefer this method, and this is an example of its use.
Good luck.
ADDED: I think you will find, if you go through this exercise a few times, you will learn what coding practices tend to result in performance problems, and you will instinctively avoid them. (This is subtly different from "premature optimization", which is assuming at the beginning that you must be concerned about performance. In fact, you will probably learn, if you don't already know, that premature concern about performance can well cause the very problem it seeks to avoid.)
Optimizing a program means: make it run faster
The only way of making the program faster is making it do less:
find an algorithm that uses fewer operations (e.g. N log N instead of N^2)
avoid slow components of your machine (keep objects in cache instead of in main memory, or in main memory instead of on disk); reducing memory consumption nearly always helps!
Further rules:
In looking for optimization opportunities, adhere to the 80-20-rule: 20% of typical program code accounts for 80% of execution time.
Measure the time before and after every attempted optimization; often enough, optimizations don't.
Only optimize after the program runs correctly!
Also, there are ways to make a program appear to be faster:
separate GUI event processing from back-end tasks; priorize user-visible changes against back-end calculation to keep the front-end "snappy"
give the user something to read while performing long operations (every noticed the slideshows displayed by installers?)
However, as a self-taught, new programmer I've never really understood what exactly do people mean when talking about such things.
Let me share a secret with you: nobody does. There are certain areas where we know mathematically what is and isn't slow. But for the most part, performance is too complicated to be able to understand. If you speed up one part of your code, there's a good possibility you're slowing down another.
Therefore, anyone who tells you that one method is faster than another, there's a good possibility they're just guessing unless one of three things are true:
They have data
They're choosing an algorithm that they know is faster mathematically.
They're choosing a data structure that they know is faster mathematically.
Optimization means trying to improve computer programs for such things as speed. The question is very broad, because optimization can involve compilers improving programs for speed, or human beings doing the same.
I suggest you read a bit of theory first (from books, or Google for lecture slides):
Data structures and algorithms - what the O() notation is, how to calculate it,
what datastructures and algorithms can be used to lower the O-complexity
Book: Introduction to Algorithms by Thomas H. Cormen, Charles E. Leiserson, and Ronald L. Rivest
Compilers and assembly - how code is translated to machine instructions
Computer architecture - how the CPU, RAM, Cache, Branch predictions, out of order execution ... work
Operating systems - kernel mode, user mode, scheduling processes/threads, mutexes, semaphores, message queues
After reading a bit of each, you should have a basic grasp of all the different aspects of optimization.
Note: I wiki-ed this so people can add book recommendations.
I am going with the idea that optimizing a code is to get the same results in less time. And fully optimized only means they ran out of ideas to make it faster. I throw large buckets of scorn on claims of "fully optimized" code! There's no such thing.
So you want to make your application/program/module run faster? First thing to do (as mentioned earlier) is measure also known as profiling. Do not guess where to optimize. You are not that smart and you will be wrong. My guesses are wrong all the time and large portions of my year are spent profiling and optimizing. So get the computer to do it for you. For PC VTune is a great profiler. I think VS2008 has a built in profiler, but I haven't looked into it. Otherwise measure functions and large pieces of code with performance counters. You'll find sample code for using performance counters on MSDN.
So where are your cycles going? You are probably waiting for data coming from main memory. Go read up on L1 & L2 caches. Understanding how the cache works is half the battle. Hint: Use tight, compact structures that will fit more into a cache-line.
Optimization is lots of fun. And it's never ending too :)
A great book on optimization is Write Great Code: Understanding the Machine by Randall Hyde.
Make sure your application produces correct results before you start optimizing it.
I am currently working for a client who are petrified of changing lousy un-testable and un-maintainable code because of "performance reasons". It is clear that there are many misconceptions running rife and reasons are not understood, but merely followed with blind faith.
One such anti-pattern I have come across is the need to mark as many classes as possible as sealed internal...
*RE-Edit: I see marking everything as sealed internal (in C#) as a premature optimisation.*
I am wondering what are some of the other performance anti-patterns people may be aware of or come across?
The biggest performance anti-pattern I have come across is:
Not measuring performance before and
after the changes.
Collecting performance data will show if a certain technique was successful or not. Not doing so will result in pretty useless activities, because someone has the "feeling" of increased performance when nothing at all has changed.
The elephant in the room: Focusing on implementation-level micro-optimization instead of on better algorithms.
Variable re-use.
I used to do this all the time figuring I was saving a few cycles on the declaration and lowering memory footprint. These savings were of minuscule value compared with how unruly it made the code to debug, especially if I ended up moving a code block around and the assumptions about starting values changed.
Premature performance optimizations comes to mind. I tend to avoid performance optimizations at all costs and when I decide I do need them I pass the issue around to my collegues several rounds trying to make sure we put the obfu... eh optimization in the right place.
One that I've run into was throwing hardware at seriously broken code, in an attempt to make it fast enough, sort of the converse of Jeff Atwood's article mentioned in Rulas' comment. I'm not talking about the difference between speeding up a sort that uses a basic, correct algorithm by running it on faster hardware vs. using an optimized algorithm. I'm talking about using a not obviously correct home brewed O(n^3) algorithm when a O(n log n) algorithm is in the standard library. There's also things like hand coding routines because the programmer doesn't know what's in the standard library. That one's very frustrating.
Using design patterns just to have them used.
Using #defines instead of functions to avoid the penalty of a function call.
I've seen code where expansions of defines turned out to generate huge and really slow code. Of course it was impossible to debug as well. Inline functions is the way to do this, but they should be used with care as well.
I've seen code where independent tests has been converted into bits in a word that can be used in a switch statement. Switch can be really fast, but when people turn a series of independent tests into a bitmask and starts writing some 256 optimized special cases they'd better have a very good benchmark proving that this gives a performance gain. It's really a pain from maintenance point of view and treating the different tests independently makes the code much smaller which is also important for performance.
Lack of clear program structure is the biggest code-sin of them all. Convoluted logic that is believed to be fast almost never is.
Do not refactor or optimize while writing your code. It is extremely important not to try to optimize your code before you finish it.
Julian Birch once told me:
"Yes but how many years of running the application does it actually take to make up for the time spent by developers doing it?"
He was referring to the cumulative amount of time saved during each transaction by an optimisation that would take a given amount of time to implement.
Wise words from the old sage... I often think of this advice when considering doing a funky optimisation. You can extend the same notion a little further by considering how much developer time is being spent dealing with the code in its present state versus how much time is saved by the users. You could even weight the time by hourly rate of the developer versus the user if you wanted.
Of course, sometimes its impossible to measure, for example, if an e-commerce application takes 1 second longer to respond you will loose some small % money from users getting bored during that 1 second. To make up that one second you need to implement and maintain optimised code. The optimisation impacts gross profit positively, and net profit negatively, so its much harder to balance. You could try - with good stats.
Exploiting your programming language. Things like using exception handling instead of if/else just because in PLSnakish 1.4 it's faster. Guess what? Chances are it's not faster at all and that two years from now someone maintaining your code will get really angry with you because you obfuscated the code and made it run much slower, because in PLSnakish 1.8 the language maintainers fixed the problem and now if/else is 10 times faster than using exception handling tricks. Work with your programming language and framework!
Changing more than one variable at a time. This drives me absolutely bonkers! How can you determine the impact of a change on a system when more than one thing's been changed?
Related to this, making changes that are not warranted by observations. Why add faster/more CPUs if the process isn't CPU bound?
General solutions.
Just because a given pattern/technology performs better in one circumstance does not mean it does in another.
StringBuilder overuse in .Net is a frequent example of this one.
Once I had a former client call me asking for any advice I had on speeding up their apps.
He seemed to expect me to say things like "check X, then check Y, then check Z", in other words, to provide expert guesses.
I replied that you have to diagnose the problem. My guesses might be wrong less often than someone else's, but they would still be wrong, and therefore disappointing.
I don't think he understood.
Some developers believe a fast-but-incorrect solution is sometimes preferable to a slow-but-correct one. So they will ignore various boundary conditions or situations that "will never happen" or "won't matter" in production.
This is never a good idea. Solutions always need to be "correct".
You may need to adjust your definition of "correct" depending upon the situation. What is important is that you know/define exactly what you want the result to be for any condition, and that the code gives those results.
Michael A Jackson gives two rules for optimizing performance:
Don't do it.
(experts only) Don't do it yet.
If people are worried about performance, tell 'em to make it real - what is good performance and how do you test for it? Then if your code doesn't perform up to their standards, at least it's something the code writer and the application user agree on.
If people are worried about non-performance costs of rewriting ossified code (for example, the time sink) then present your estimates and demonstrate that it can be done in the schedule. Assuming it can.
I believe it is a common myth that super lean code "close to the metal" is more performant than an elegant domain model.
This was apparently de-bunked by the creator/lead developer of DirectX, who re-wrote the c++ version in C# with massive improvements. [source required]
Appending to an array using (for example) push_back() in C++ STL, ~= in D, etc. when you know how big the array is supposed to be ahead of time and can pre-allocate it.
Let's just assume for now that you have narrowed down where the typical bottlenecks in your app are. For all you know, it might be the batch process you run to reindex your tables; it could be the SQL queries that runs over your effective-dated trees; it could be the XML marshalling of a few hundred composite objects. In other words, you might have something like this:
public Result takeAnAnnoyingLongTime(Input in) {
// impl of above
}
Unfortunately, even after you've identified your bottleneck, all you can do is chip away at it. No simple solution is available.
How do you measure the performance of your bottleneck so that you know your fixes are headed in the right direction?
Two points:
Beware of the infamous "optimizing the idle loop" problem. (E.g. see the optimization story under the heading "Porsche-in-the-parking-lot".) That is, just because a routine is taking a significant amount of time (as shown by your profiling), don't assume that it's responsible for slow performance as perceived by the user.
The biggest performance gains often come not from that clever tweak or optimization to the implementation of the algorithm, but from realising that there's a better algorithm altogether. Some improvements are relatively obvious, while others require more detailed analysis of the algorithms, and possibly a major change to the data structures involved. This may include trading off processor time for I/O time, in which case you need to make sure that you're not optimizing only one of those measures.
Bringing it back to the question asked, make sure that whatever you're measuring represents what the user actually experiences, otherwise your efforts could be a complete waste of time.
Profile it
Find the top line in the profiler, attempt to make it faster.
Profile it
If it worked, go to 1. If it didn't work, go to 2.
I'd measure them using the same tools / methods that allowed me to find them in the first place.
Namely, sticking timing and logging calls all over the place. If the numbers start going down, then you just might be doing the right thing.
As mentioned in this msdn column, performance tuning is compared to the job of painting Golden Gate Bridge: once you finish painting the entire thing, it's time to go back to the beginning and start again.
This is not a hard problem. The first thing you need to understand is that measuring performance is not how you find performance problems. Knowing how slow something is doesn't help you find out why. You need a diagnostic tool, and a good one. I've had a lot of experience doing this, and this is the best method. It is not automatic, but it runs rings around most profilers.
It's an interesting question. I don't think anyone knows the answer. I believe that significant part of the problem is that for more complicated programs, no one can predict their complexity. Therefore, even if you have profiler results, it's very complicated to interpret it in terms of changes that should be made to the program, because you have no theoretical basis for what the optimal solution is.
I think this is a reason why we have so bloated software. We optimize only so that quite simple cases would work on our fast machines. But once you put such pieces together into a large system, or you use order of magnitude larger input, wrong algorithms used (which were until then invisible both theoretically and practically) will start showing their true complexity.
Example: You create a string class, which handles Unicode. You use it somewhere like computer-generated XML processing where it really doesn't matter. But Unicode processing is in there, taking part of the resources. By itself, the string class can be very fast, but call it million times, and the program will be slow.
I believe that most of the current software bloat is of this nature. There is a way to reduce it, but it contradicts OOP. There is an interesting book There is an interesting book about various techniques, it's memory oriented but most of them could be reverted to get more speed.
I'd identify two things:
1) what complexity is it? The easiest way is to graph time-taken verses size of input.
2) how is it bound? Is it memory, or disk, or IPC with other processes or machines, or..
Now point (2) is the easier to tackle and explain: Lots of things go faster if you have more RAM or a faster machine or faster disks or move over to gig ethernet or such. If you identify your pain, you can put some money into hardware to make it tolerable.
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Oftentimes a developer will be faced with a choice between two possible ways to solve a problem -- one that is idiomatic and readable, and another that is less intuitive, but may perform better. For example, in C-based languages, there are two ways to multiply a number by 2:
int SimpleMultiplyBy2(int x)
{
return x * 2;
}
and
int FastMultiplyBy2(int x)
{
return x << 1;
}
The first version is simpler to pick up for both technical and non-technical readers, but the second one may perform better, since bit shifting is a simpler operation than multiplication. (For now, let's assume that the compiler's optimizer would not detect this and optimize it, though that is also a consideration).
As a developer, which would be better as an initial attempt?
You missed one.
First code for correctness, then for clarity (the two are often connected, of course!). Finally, and only if you have real empirical evidence that you actually need to, you can look at optimizing. Premature optimization really is evil. Optimization almost always costs you time, clarity, maintainability. You'd better be sure you're buying something worthwhile with that.
Note that good algorithms almost always beat localized tuning. There is no reason you can't have code that is correct, clear, and fast. You'll be unreasonably lucky to get there starting off focusing on `fast' though.
IMO the obvious readable version first, until performance is measured and a faster version is required.
Take it from Don Knuth
Premature optimization is the root of all evil (or at least most of it) in programming.
Readability 100%
If your compiler can't do the "x*2" => "x <<1" optimization for you -- get a new compiler!
Also remember that 99.9% of your program's time is spent waiting for user input, waiting for database queries and waiting for network responses. Unless you are doing the multiple 20 bajillion times, it's not going to be noticeable.
Readability for sure. Don't worry about the speed unless someone complains
In your given example, 99.9999% of the compilers out there will generate the same code for both cases. Which illustrates my general rule - write for readability and maintainability first, and optimize only when you need to.
Readability.
Coding for performance has it's own set of challenges. Joseph M. Newcomer said it well
Optimization matters only when it
matters. When it matters, it matters a
lot, but until you know that it
matters, don't waste a lot of time
doing it. Even if you know it matters,
you need to know where it matters.
Without performance data, you won't
know what to optimize, and you'll
probably optimize the wrong thing.
The result will be obscure, hard to
write, hard to debug, and hard to
maintain code that doesn't solve your
problem. Thus it has the dual
disadvantage of (a) increasing
software development and software
maintenance costs, and (b) having no
performance effect at all.
I would go for readability first. Considering the fact that with the kind of optimized languages and hugely loaded machines we have in these days, most of the code we write in readable way will perform decently.
In some very rare scenarios, where you are pretty sure you are going to have some performance bottle neck (may be from some past bad experiences), and you managed to find some weird trick which can give you huge performance advantage, you can go for that. But you should comment that code snippet very well, which will help to make it more readable.
Readability. The time to optimize is when you get to beta testing. Otherwise you never really know what you need to spend the time on.
A often overlooked factor in this debate is the extra time it takes for a programmer to navigate, understand and modify less readible code. Considering a programmer's time goes for a hundred dollars an hour or more, this is a very real cost.
Any performance gain is countered by this direct extra cost in development.
Putting a comment there with an explanation would make it readable and fast.
It really depends on the type of project, and how important performance is. If you're building a 3D game, then there are usually a lot of common optimizations that you'll want to throw in there along the way, and there's no reason not to (just don't get too carried away early). But if you're doing something tricky, comment it so anybody looking at it will know how and why you're being tricky.
The answer depends on the context. In device driver programming or game development for example, the second form is an acceptable idiom. In business applications, not so much.
Your best bet is to look around the code (or in similar successful applications) to check how other developers do it.
If you're worried about readability of your code, don't hesitate to add a comment to remind yourself what and why you're doing this.
using << would by a micro optimization.
So Hoare's (not Knuts) rule:
Premature optimization is the root of all evil.
applies and you should just use the more readable version in the first place.
This is rule is IMHO often misused as an excuse to design software that can never scale, or perform well.
Both. Your code should balance both; readability and performance. Because ignoring either one will screw the ROI of the project, which in the end of the day is all that matters to your boss.
Bad readability results in decreased maintainability, which results in more resources spent on maintenance, which results in a lower ROI.
Bad performance results in decreased investment and client base, which results in a lower ROI.
Readability is the FIRST target.
In the 1970's the army tested some of the then "new" techniques of software development (top down design, structured programming, chief programmer teams, to name a few) to determine which of these made a statistically significant difference.
THe ONLY technique that made a statistically significant difference in development was...
ADDING BLANK LINES to program code.
The improvement in readability in those pre-structured, pre-object oriented code was the only technique in these studies that improved productivity.
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Optimization should only be addressed when the entire project is unit tested and ready for instrumentation. You never know WHERE you need to optimize the code.
In their landmark books Kernigan and Plauger in the late 1970's SOFTWARE TOOLS (1976) and SOFTWARE TOOLS IN PASCAL (1981) showed ways to create structured programs using top down design. They created text processing programs: editors, search tools, code pre-processors.
When the completed text formating function was INSTRUMENTED they discovered that most of the processing time was spent in three routines that performed text input and output ( In the original book, the i-o functions took 89% of the time. In the pascal book, these functions consumed 55%!)
They were able to optimize these THREE routines and produced the results of increased performance with reasonable, manageable development time and cost.
The larger the codebase, the more readability is crucial. Trying to understand some tiny function isn't so bad. (Especially since the Method Name in the example gives you a clue.) Not so great for some epic piece of uber code written by the loner genius who just quit coding because he has finally seen the top of his ability's complexity and it's what he just wrote for you and you'll never ever understand it.
As almost everyone said in their answers, I favor readability. 99 out of 100 projects I run have no hard response time requirements, so it's an easy choice.
Before you even start coding you should already know the answer. Some projects have certain performance requirements, like 'need to be able to run task X in Y (milli)seconds'. If that's the case, you have a goal to work towards and you know when you have to optimize or not. (hopefully) this is determined at the requirements stage of your project, not when writing the code.
Good readability and the ability to optimize later on are a result of proper software design. If your software is of sound design, you should be able to isolate parts of your software and rewrite them if needed, without breaking other parts of the system. Besides, most true optimization cases I've encountered (ignoring some real low level tricks, those are incidental) have been in changing from one algorithm to another, or caching data to memory instead of disk/network.
If there is no readability , it will be very hard to get performance improvement when you really need it.
Performance should be only improved when it is a problem in your program, there are many places would be a bottle neck rather than this syntax. Say you are squishing 1ns improvement on a << but ignored that 10 mins IO time.
Also, regarding readability, a professional programmer should be able to read/understand computer science terms. For example we can name a method enqueue rather than we have to say putThisJobInWorkQueue.
The bitshift versus the multiplication is a trivial optimization that gains next to nothing. And, as has been pointed out, your compiler should do that for you. Other than that, the gain is neglectable anyhow as is the CPU this instruction runs on.
On the other hand, if you need to perform serious computation, you will require the right data structures. But if your problem is complex, finding out about that is part of the solution. As an illustration, consider searching for an ID number in an array of 1000000 unsorted objects. Then reconsider using a binary tree or a hash map.
But optimizations like n << C are usually neglectible and trivial to change to at any point. Making code readable is not.
It depends on the task needed to be solved. Usually readability is more importrant, but there are still some tasks when you shoul think of performance in the first place. And you can't just spend a day or to for profiling and optimization after everything works perfectly, because optimization itself may require rewriting sufficiant part of a code from scratch. But it is not common nowadays.
I'd say go for readability.
But in the given example, I think that the second version is already readable enough, since the name of the function exactly states, what is going on in the function.
If we just always had functions that told us, what they do ...
You should always maximally optimize, performance always counts. The reason we have bloatware today, is that most programmers don't want to do the work of optimization.
Having said that, you can always put comments in where slick coding needs clarification.
There is no point in optimizing if you don't know your bottlenecks. You may have made a function incredible efficient (usually at the expense of readability to some degree) only to find that portion of code hardly ever runs, or it's spending more time hitting the disk or database than you'll ever save twiddling bits.
So you can't micro-optimize until you have something to measure, and then you might as well start off for readability.
However, you should be mindful of both speed and understandability when designing the overall architecture, as both can have a massive impact and be difficult to change (depending on coding style and methedologies).
It is estimated that about 70% of the cost of software is in maintenance. Readability makes a system easier to maintain and therefore brings down cost of the software over its life.
There are cases where performance is more important the readability, that said they are few and far between.
Before sacrifing readability, think "Am I (or your company) prepared to deal with the extra cost I am adding to the system by doing this?"
I don't work at google so I'd go for the evil option. (optimization)
In Chapter 6 of Jon Bentley's "Programming Pearls", he describes how one system had a 400 times speed up by optimizing at 6 different design levels. I believe, that by not caring about performance at these 6 design levels, modern implementors can easily achieve 2-3 orders of magnitude of slow down in their programs.
Readability first. But even more than readability is simplicity, especially in terms of data structure.
I'm reminded of a student doing a vision analysis program, who couldn't understand why it was so slow. He merely followed good programming practice - each pixel was an object, and it worked by sending messages to its neighbors...
check this out
Write for readability first, but expect the readers to be programmers. Any programmer worth his or her salt should know the difference between a multiply and a bitshift, or be able to read the ternary operator where it is used appropriately, be able to look up and understand a complex algorithm (you are commenting your code right?), etc.
Early over-optimization is, of course, quite bad at getting you into trouble later on when you need to refactor, but that doesn't really apply to the optimization of individual methods, code blocks, or statements.
How much does an hour of processor time cost?
How much does an hour of programmer time cost?
IMHO both things have nothing to do. You should first go for code that works, as this is more important than performance or how well it reads. Regarding readability: your code should always be readable in any case.
However I fail to see why code can't be readable and offer good performance at the same time. In your example, the second version is as readable as the first one to me. What is less readable about it? If a programmer doesn't know that shifting left is the same as multiplying by a power of two and shifting right is the same as dividing by a power of two... well, then you have much more basic problems than general readability.