#WINDOWS BUILT IN RAM OPTIMIZER HOW TO#
#WINDOWS BUILT IN RAM OPTIMIZER CODE#

Instead of writing double x = d * (lim / max) * sx For example, x > 2 can be used in place of x / 4, and x << 1 replaces x * 2.Ĭommon sub expression elimination removes redundant calculations. The most common example of strength reduction is using the shift operator to multiply and divide integers by a power of 2. Strength reduction occurs when an operation is replaced by an equivalent operation that executes faster.

#WINDOWS BUILT IN RAM OPTIMIZER CODE#

We'll focus on the techniques that basically involve restructuring code and substituting equivalent operations within a method.

Some of these techniques, such as global register allocation, are sophisticated strategies to allocate machine resources (for example, CPU registers) and don't apply to Java bytecodes. There are several common optimization techniques that apply regardless of the language being used. This isn't always where you expect it to be. So the first task in optimizing code is to identify the 10 percent (frequently it is less than this) of the program that consumes most of the execution time. If you were able to make that 90 percent of the code execute twice as fast, the program would only be 5 percent faster. (Some people use the 80 percent/20 percent rule, but my experience writing and optimizing commercial games in several languages for the last 15 years has shown that the 90 percent/10 percent formula is typical for performance-hungry programs since few tasks tend to be performed with great frequency.) Optimizing the other 90 percent of the program (where 10 percent of the execution time was spent) has no noticeable effect on performance. 90/10, 80/20, hut, hut, hike!Īs a rule, 90 percent of a program's excution time is spent executing 10 percent of the code.

#WINDOWS BUILT IN RAM OPTIMIZER HOW TO#

(See the Java Tip, ' Write native methods' for more information on this topic.) Our focus in this article is on how to make Java fast. But this is all I'm going to say on the subject of replacing Java with C code. It is possible to write both a Java version of a method and native versions for selected platforms this leads to increased performance on some platforms without giving up the ability to run on all platforms. While using native methods can give the ultimate performance boost, it does so at the cost of Java's platform independence. The focus will be on the core language rather than on the Java APIs.īy the way, one thing we won't discuss here is the use of native methods written in C or assembly. We'll primarily look at how to make Java run faster rather than making it fit into a smaller space - although we'll point out where and how these approaches affect memory or network bandwidth. Code fragments that occur multiple times throughout a program are likely to be size-sensitive, while code with many execution iterations may be speed-sensitive.Īs an interpreted language with a compact bytecode, speed, or the lack of it, is what most often pops up as a problem in Java. But the reality is that sometimes the biggest problem with a program is that it requires simply too many resources, and these resources (memory, CPU cycles, network bandwidth, or a combination) may be limited. If it's such a bad idea, why optimize at all? Well, in an ideal world you wouldn't. As a rule, high-level optimizations should be considered before doing low-level optimizations. Using a better algorithm probably will yield a bigger performance increase than any amount of low-level optimizations and is more likely to deliver an improvement under all execution conditions. Optimization in Java can be an elusive target since the execution environments vary.

If you're overly obsessed with optimizing code, people will call you a nerd behind your backīefore optimizing, you should carefully consider whether you need to optimize at all. Optimizing code for one platform may actually make it worse on another platformĪ lot of time can be spent optimizing, with little gain in performance, and can result in obfuscated code Some of the techniques presented here increase speed by reducing the extensibility of the code Optimization tends to make code harder to understand and maintain If your code already works, optimizing it is a sure way to introduce new, and possibly subtle, bugs According to the pioneering computer scientist Donald Knuth, 'Premature optimization is the root of all evil.' Any article on optimization must start by pointing out that there are usually more reasons not to optimize than to optimize.

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