The JIT compiler improves performance through method inline, hotspot code recognition, escape analysis and scalar replacement, lock optimization and other means. 1. Method inline the method directly embeds small methods into the call, reducing call overhead and promoting other optimizations; 2. Hotspot code identification uses counter to find high-frequency execution code for compilation, and centralizes resources to optimize the key paths; 3. Escape analysis determines whether the object is escaping, and combines scalar replacement to reduce memory allocation; 4. Lock optimization includes mechanisms such as lock elimination, lock coarseness and bias locking to improve multi-thread synchronization efficiency. These optimizations enable Java programs to achieve higher performance at runtime.

Java's JIT (Just-In-Time) compiler is one of the key components of JVM performance optimization. It dynamically compiles bytecode into local machine code during program run, thereby significantly improving execution efficiency. But how does it do this? How do you determine which codes are worth optimizing?

Method inline: Reduce function call overhead

One of the most commonly used optimization methods for JIT compilers is method inline . Simply put, it is to replace the call of a small method with the code logic of the method itself, eliminating the stack push, jump and other operations of the call stack.

For example, a simple getter method:

 public int getValue() {
    return value;
}

If every call is really executed once, it is a waste of performance. JIT will recognize this short and concise method and "embed" it directly into the call, which is equivalent to directly accessing variables.

The benefits of this approach are obvious:

• Reduce call overhead
• It is easier to trigger other optimizations (such as constant propagation)

However, not all methods can be inlined. JIT usually only inline methods less than a certain instruction length, and will also determine whether to inline based on the call frequency.

Hot Spot Code Recognition: Focus on Optimizing High-Frequency Path

JIT will not make complex optimizations to all code, but focus on those hotspots . The so-called hot-spot code is a code segment that is frequently executed, such as a loop body or a method called multiple times.

The JVM tracks the number of executions of a method through a counter. When a method is executed exceeds a certain threshold, the JIT compiles it to local code. The benefits of doing this are:

• Avoid unnecessary compilation overhead
• Focus resources in places that really affect performance

For example, a piece of code that is repeatedly executed in a loop may be several times or even dozens of times faster than explanation after being compiled by JIT.

Escape Analysis and Scalar Replacement: Reduce object allocation pressure

Modern JIT compilers also support a technology called Escape Analysis . Its function is to determine whether the scope of an object is limited to the current thread or method. If you can be sure that the object will not "escape", then the JVM can use some tricks to optimize memory usage.

For example, the following code:

 public void someMethod() {
    MyObject obj = new MyObject();
    // do something with obj, but it doesn't escape
}

In this case, the JIT may decide not to actually create the object, but to process its fields as local variables (i.e., scalar replacement ). This not only reduces the allocation of heap memory, but also reduces the pressure of garbage collection.

It should be noted that escape analysis relies on the compiler's intelligent judgment, and sometimes different writing methods will affect the analysis results. For example, if you pass this object to another method or save it to a collection, it is likely to "escape" and lose the opportunity to optimize.

Lock optimization: Make synchronization more efficient

In multi-threaded environments, locks are a common performance bottleneck. JIT has also made a lot of optimizations in this regard, such as:

• Lock Elimination : If you find that a lock does not compete concurrently at all, just remove the lock.
• Lock Coarsening : Combine multiple consecutive small locks into one large lock to reduce the number of locking/unlocking times.
• Positive locks and lightweight locks : These are JVM-level mechanisms, but are also done in collaboration by JIT, with the goal of making lock operations in no competition situations with almost no overhead.

These optimizations are usually transparent to developers, but they do quietly improve the performance of concurrent programs behind the scenes.

Basically that's it. There are many other optimization methods for JIT compilers, such as public subexpression elimination, loop expansion, etc., but the above are the most common and easiest to understand. While we don't need to manually interfere with JIT's work, understanding how it works can help write Java code that is more suitable for optimization.

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