The JIT compiler optimizes code through four methods: method inline, hotspot detection and compilation, type speculation and devirtualization, and redundant operation elimination. 1. Method inline reduces call overhead and inserts frequently called small methods directly into the call; 2. Hot spot detection and high-frequency execution of code and centrally optimize it to save resources; 3. Type speculation collects runtime type information to achieve devirtualization calls, improving efficiency; 4. Redundant operations eliminate useless calculations and inspections based on operational data deletion, enhancing performance.
The Just-In-Time (JIT) compiler optimizes code in fact, it is to dynamically translate bytecode or intermediate language into local machine code during the program operation, and to make a series of optimizations in this process to make the program run faster and more efficient. It does not complete all optimizations when writing code like static compilation, but "adapts to local conditions" according to actual operation.
1. Method Inlining
Method calls themselves are overhead, especially small functions that are frequently called. The JIT compiler will identify small methods that are frequently called, and then directly "stuck" their code into the call, eliminating the creation and jump of the call stack.
For example, simple methods like getter
and setter
, JIT may be directly inlined. This not only reduces the call overhead, but may further trigger other optimizations, such as constant propagation or dead code elimination.
This method is particularly suitable for the following situations:
- The method body is very small
- Methods are called frequently
- No complex branch logic
2. HotSpot Detection and Compilation
JIT does not compile all code from the beginning. It is usually executed first with an interpreter, and counts the number of calls or loops of the method. Compilation will only be triggered when a method is determined to be "hotspot", that is, execution frequency is very high.
There are several benefits to doing this:
- Save compilation time for infrequently used code
- The part that centralized resource optimization really affects performance
- More accurate optimization judgments can be made based on runtime data
For example, the Client and Server modes in JVM have different hot spot thresholds. In Server mode, deep optimization is more preferred, but the startup is slower.
3. Type Profiling & Devirtualization
JIT can collect actual type information of variables at runtime. For example, a reference to an interface type actually points to the same concrete implementation class in most cases. With this information, JIT can bypass virtual method table search and directly call the target method, which is called "de-virtualization".
For example: If you have a List<string></string>
, although it declares it as List
interface, JIT found that almost all are instances of ArrayList
, it can be directly optimized to call ArrayList.get()
to avoid checking the virtual function table every time.
In addition, this type of speculation can also help with more radical optimizations, such as the elimination of array boundary checking.
4. Redundant Operation Elimination
JIT will see if certain variables or calculations are really useful at runtime. for example:
- The same expression is calculated multiple times and the value remains unchanged
- Branch conditions always take a certain path when running
- Array boundary checking can be omitted in a specific context
This type of optimization depends on runtime data, so static compilation is difficult to be so meticulous. And JIT can make smarter judgments because it knows the "real world" data flow.
Basically that's it. The great thing about JIT is that it is not a one-time transaction, but a learning while running and dynamically adjusting its strategies. Although the mechanism is complex, its goal is clear: make your code run as fast as possible on your own machine.
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