Skip to content. Change Language. Related Articles. Table of Contents. Improve Article. Save Article. Like Article. This code shows how to use ranged for loops to iterate through an array and a vector: CPP. REP i, 1, size Bonus points if you figure out how to fold the 3 very similar but not entirely identical inner blocks into 1 function!
And so on. Just keep on improving step-wise, making your program more readable at every iteration. Edit: according to Wikipedia indentation is. The compiler doesn't at least normally have any way of knowing that strlen will return the same result from every call, so it'll end up being called on every iteration of the loop. To prevent that, you want to do something like:. You're currently doing the conversion of apparently directions using cascaded if statements.
I'd probably build a small table, and just do a table lookup:. Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams? Collectives on Stack Overflow. Learn more. If the item is at, say position 23, the loop will stop there and then, and skip the remaining iterations.
It is a good idea to keep functions small and simple. This enables the compiler to perform other optimizations, such as register allocation, more efficiently. Function call overhead on the processor is small, and is often small in proportion to the work performed by the called function. There are some limitations up to which words of arguments can be passed to a function in registers. These arguments can be integer-compatible char , short s, int s and float s all take one word , or structures of up to four words including the 2-word double s and long long s.
If the argument limitation is 4, then the fifth and subsequent words are passed on the stack. This increases the cost of storing these words in the calling function and reloading them in the called function. A function which does not call any other functions is known as a leaf function. In many applications, about half of all function calls made are to leaf functions. Leaf functions are compiled very efficiently on every platform, as they often do not need to perform the usual saving and restoring of registers.
The cost of pushing some registers on entry and popping them on exit is very small compared to the cost of the useful work done by a leaf function that is complicated enough to need more than four or five registers. If possible, we should try to arrange for frequently-called functions to be leaf functions. The number of times a function is called can be determined by using the profiling facility. There are several ways to ensure that a function is compiled as a leaf function:.
Function inlining is disabled for all debugging options. This results in faster code, but it adversely affects code size, particularly if the inline function is large and used often. The overhead of parameter passing is generally lower, since it is not necessary to copy variables. If some of the parameters are constants, the compiler can optimize the resulting code even further.
The big disadvantage of inline functions is that the code sizes increase if the function is used in many places. This can vary significantly depending on the size of the function, and the number of places where it is used.
It is wise to only inline a few critical functions. Note that when done wisely, inlining may decrease the size of the code: a call takes usually a few instructions, but the optimized version of the inlined code might translate to even less instructions. A function can often be approximated using a lookup table, which increases performance significantly.
A table lookup is usually less accurate than calculating the value properly, but for many applications, this does not matter. Many signal processing applications for example, modem demodulator software make heavy use of sin and cos functions, which are computationally expensive to calculate. When using lookup tables, try to combine as many adjacent operations as possible into a single lookup table.
This is faster and uses less space than multiple lookup tables. Although floating point operations are time consuming for any kind of processors, sometimes we need to used it in case of implementing signal processing applications.
However, when writing floating-point code, keep the following things in mind:. Division is typically twice as slow as addition or multiplication. The constant is calculated during compilation. Float variables consume less memory and fewer registers, and are more efficient because of their lower precision. Use float s whenever their precision is good enough. Transcendental functions, like sin , exp and log are implemented using series of multiplications and additions using extended precision.
As a result, these operations are at least ten times slower than a normal multiply. The compiler cannot apply many optimizations which are performed on integers to floating-point values. Therefore, it is beneficial to perform floating-point optimizations manually if it is known they are correct. However, it is still possible that the floating performance will not reach the required level for a particular application.
In such a case, the best approach may be to change from using floating-point to fixed point arithmetic. When the range of values needed is sufficiently small, fixed-point arithmetic is more accurate and much faster than floating-point arithmetic. In general, savings can be made by trading off memory for speed. If you can cache any often used data rather than recalculating or reloading it, it will help. Last, but definitely not least - turn compiler optimization on!
Seems obvious, but is often forgotten in that last minute rush to get the product out on time. The compiler will be able to optimize at a much lower level than can be done in the source code, and perform optimizations specific to the target processor. This article has no explicit license attached to it but may contain usage terms in the article text or the download files themselves.
If in doubt please contact the author via the discussion board below. Sign in Email. Forgot your password? Search within: Articles Quick Answers Messages. Tagged as VC6. Stats Koushik Ghosh Rate me:. Please Sign up or sign in to vote.
Introduction During a project for developing a light JPEG library which is enough to run on a mobile device without compromising quality graphics on a mobile device, I have seen and worked out a number of ways in which a given computer program can be made to run faster.
Declaration Actually, during my project, I have used the tips from this for optimization ARM because my project was on ARM platform, but I have also used many other articles from Internet. Where it is needed?
Integers We should use unsigned int instead of int if we know the value will never be negative. So, the best declaration for an int variable in a tight loop would be:. Copy Code. A list of licenses authors might use can be found here. Koushik Ghosh Software Developer Senior ibm.
First Prev Next Error paulushi Sep Member Mar Nipun Parikh Feb Fuxymaxy Jun Member May Ram VR 4-Apr Manoj Kumar Choubey Feb Viktor Signaievskyi 2-Jun Koushik Ghosh Jun Kevin Drzycimski Jul Lyra Belacqua May Ricky Lung 1-Sep Member 6-Sep Tomer Margolin Oct Go to top. Layout: fixed fluid. Copy Code something 0 ; something 1 ; something 2 ;.
Software Developer Senior ibm. First Prev Next. Error paulushi Sep You rock! Full 5! This is the best article about performance in C! Revisit this? Hi Koushik, This was an exceptionally well-written and useful post. Since it has been 10 years from when you first posted it, perhaps it is time to update it? Certainly you must have developed more ideas since Best, -Bearvarine. References and links are obselete Member Mar Member All the links and references given in this article are obselete.
They are throwing HTTP errors. Please do upadate the same if possible. Tips for beginners Nipun Parikh Feb Nipun Parikh.
My vote of 4 Fuxymaxy Jun Wonderful tips, clearly explained!
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