Thus, practically all complete programs contain significant opportunities for optimization. For more rushed or less experienced teams, I have seen performance improvements of 3 to 10 times. A speedup of more than 10x by tweaking code is less likely. However, selection of a better algorithm or data structure can be the difference between whether a feature can be deployed or is infeasibly slow.
For instance, famous computer scientist Donald Knuth said of optimization:. We should forget about small efficiencies, say about 97 percent of the time: premature optimization is the root of all evil. CiteSeerX: More computing sins are committed in the name of efficiency without necessarily achieving it than for any other single reason—including blind stupidity.
The advice not to optimize has become received wisdom, unquestioned even by many experienced coders, who wince reflexively when conversation turns to performance tuning. I think cynical promotion of this advice has been used too often to excuse weak development habits and avoid the small amount of analysis that might result in vastly faster code.
I also think uncritical acceptance of this advice is responsible for a lot of wasted CPU cycles, a lot of frustrated user-hours, and too much time spent reworking code that should have been more efficient from the start.
My advice is less dogmatic. It is OK to optimize. Why would anyone choose deliberately to write inefficient code? In other words, all the best practices of software development still apply. Chapter 3 provides tools to help determine where the hot spots in the code are. When I was in college, my professors warned that optimal algorithms might have a higher startup cost than simple ones.
They should therefore only be used on large data sets. I have also been advised to develop programs using whatever algorithm is easiest to code, and then go back and optimize it if the program runs too slowly. While it is undeniably good advice to continue making progress, once you have coded an optimal search or sort a couple of times, it is no more difficult to get running than a slower algorithm. You might as well do it right the first time and debug only one algorithm.
Received wisdom is, in fact, probably the single greatest enemy of performance improvement efforts. This received wisdom is valuable to the extent that it keeps developers from believing their O n 2 insertion sort is optimal, but not so good if it prevents them from checking the literature to find out that radix sort is faster at O n log r n where r is the radix or number of sorting buckets , that flashsort has even faster O n performance on randomly distributed data, or that quicksort, which received wisdom makes the benchmark against which other sorts are measured, has painfully bad O n 2 worst-case performance.
Aristotle said that women had fewer teeth than men The History of Animals , Book II, part 1 , a bit of received wisdom that stood for 1, years before someone was curious enough to count the teeth in a few mouths. The antidote to received wisdom is the scientific method, in the form of experimentation.
Chapter 3 covers instruments to measure software performance and experiments to validate optimizations. There is also received wisdom in the software development world that optimization is not relevant.
The reasoning goes that even if your code runs slowly today, each new year brings faster processors, solving your performance problems for free through the passage of time. Like most received wisdom, this nugget of thought was never really true. It might have seemed true in the s and s, when desktop computers and standalone applications dominated the development scene and single-core processors doubled in speed every 18 months.
Programs today must also run on mobile platforms, where battery life and heat dissipation constrain the instruction execution rate. Furthermore, while time may bring new customers with faster computers, it does nothing to improve the performance of existing hardware. The only speed upgrade an existing customer will ever get from your company comes from optimizing subsequent releases.
Optimization keeps your program fresh. Frequently misattributed to Senator Everett Dirkson — , who claims he never said this, though he admits saying a lot of things like it. Desktop computers are astonishingly fast.
They can dispatch a new instruction every nanosecond or better. It is seductively appealing to believe that optimization cannot possibly matter when a computer is so fast. The problem with this way of thinking is that the faster the processor is, the faster wasted instructions pile up. Efficiency matters very much on small embedded and mobile processors with memory, power, or speed constraints.
It also matters on servers running flat-out on big computers. Another way to say this is that efficiency matters for any application that must contend for constrained resources memory, power, CPU cycles.
Efficiency matters a great deal, too, any time your workload is big enough to be spread across multiple computers. In this case, efficiency can be the difference between spending on servers or cloud instances versus or 1, In 50 years, computer performance has improved by six orders of magnitude. And yet, here we are talking about optimization. Apart from that, I tend to profile regularly to get an idea of what's there, and how much time is left per frame.
To me time per frame is the most logical as it tells me directly where I'm at with framerate goals. Also try to find out where peaks are and what causes them - I prefer a stable framerate over a high framerate with spikes.
Once a game is ready to be released either final or a beta , or it is noticeably slow, that's probably the best time to profile your app. Of course, you can always run the profiler at any point; but yes, premature optimization is the root of all evil. Unfounded optimization, too; you need actual data to show that some code is slow, before you should go try to "optimize" it.
A profiler does that for you. If you don't know about a profiler, learn it! Here's a good blog post demonstrating the usefulness of a profiler. Most of game code optimization comes down to reducing the CPU cycles that you need for each frame.
One way to do this is to just optimize every routine as you write it, and make sure it's as fast as possible. This means that directing all of your optimization work to these bottleneck routines will have 10x the effect of optimizating everything uniformly. So how do you identify these routines? Profiling makes it easy. Otherwise, if your small game is running at FPS even though it has an inefficient algorithm in it, do you really have a reason to optimize?
You should have a good idea of your target machine's specs, and make sure the game runs well on that machine, but anything beyond that is arguably wasted time that could be better spent coding or polishing the game. I find it useful to build profiling in.
Even if you're not actively optimising it's good to have an idea on what is limiting your performance at any given time. Many games have some kind of overlayable HUD which displays a simple graphical chart usually just a coloured bar showing how long various parts of the game loop are taking each frame. It would be a bad idea to leave performance analysis and optimisation to too late a late stage. You need to know what the budgets are for graphics, physics, etc. You can't do that if you have no idea what your performance is going to be, and you can't guess at that without know both what your performance is, and how much slack there might be.
As to when to tackle stuff - again, probably best not to leave it too late, lest you have to refactor half your engine. On the other hand, don't get too wrapped up in optimising stuff to squeeze out every cycle if you think you might change the algorithm entirely tomorrow, or if you haven't put real game data through it. Pick off the low hanging fruit as you go along, tackle the big stuff periodically, and you should be fine.
If look at Knuth's quote in its context he goes on to explain, that we should optimize but with tools, like a profiler. You should constantly profile and memory profile your application after the very basic architecture gets laid. Profiling will not just help you increase the speed, it will help you find bugs. If your program suddenly drastically changes speed, this is usually because of a bug. If your not profiling it might go unnoticed. The trick to optimizing is to do it by design.
Don't wait till the last minute. Make sure the design of your program gives you the performance you need without really on nasty inner loop tricks. This ensures my floating point math is on cue with where I want it to be. Another good practice is to make a habit out of optimizations as you code instead of going back. Most of the time these little practices are just as time consuming as what you were coding anyway.
Before coding a feature, make sure you research the most efficient way to do it. I would say the easiest way would be using your common sense - if something looks like it's running slow, then have a look at it.
Time spent on making the code faster is time that is not spent on making the test suite complete, the documentation effective, the use cases compelling, or the API reasonable. And so I think it is useful to decompose a project into three steps: make it run which is a matter of hours , make it right on a timescale of days , and make it fast on a timescale of weeks or months, and a lot of pain and curse words. There are cases when optimization is necessary. But for the majority of cases in ecological research, optimization is the wrong target when it focuses only on the code.
In brief, code I can trust is more useful to me that code that is fast. Code maintenance usually gets little respect in developer circles, but it still can be a rewarding task if we follow best practices, such as using reliable version control, dependency management, staging and testing platforms, and properly take care of documentation. In the development realm of mobile, web, and desktop apps or JavaScript libraries, documentation plays an important role Extending a project, and adding new features to it is usually much faster if we work with a well-optimized, clean code base.
Apart from the already discussed code optimization methods, feature development can also gain momentum if we keep up with modern project management methods , for example if we use iterative lifecycle models instead of the traditional waterfall model.
The term " technical debt " was coined by Ward Cunningham , the programmer who also developed the first wiki. It compares the consequences of our bad programming decisions that accummulate over time to financial debt in which people pay interest in the future in order to quickly get money in the present.
These less-than-optimal decisions usually manifest themselves in the form of quick fixes, copy and paste programming, hard coding, cargo-cult programming, and other coding antipatterns and sloppy work habits.
Please enable JavaScript in your browser to enjoy a better experience. Faster Sites Optimizing code is similar to buying a faster car. The Importance of Documentation for Web Developers.
The Importance of Documentation for Web Developers In the development realm of mobile, web, and desktop apps or JavaScript libraries, documentation plays an important role
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