When a program is written in a high-level language, the code usually needs to be translated into machine code before the processor can execute it. Two common approaches for doing this are Just-In-Time (JIT) compilation and Ahead-of-Time (AOT) compilation.
The main difference is simple:
JIT compiles code at runtime, while AOT compiles code before the program runs.
But the difference becomes more interesting when we look at how each approach handles performance and optimization.
What is JIT Compilation?
Just-In-Time (JIT) compilation translates code into machine code while the program is running.
Instead of compiling everything before execution, a JIT compiler observes the application as it runs. It identifies frequently executed sections of code, often called hot spots, and compiles those sections into optimized machine code.
This allows the compiler to make optimization decisions based on what is actually happening during execution.
For example, a JIT compiler might notice that a particular function is being called thousands of times with similar types of data. It can then optimize that function specifically for that situation.
This can lead to significant performance improvements.
JIT in JavaScript
Modern JavaScript engines use JIT compilation extensively.
A browser may initially execute JavaScript using a relatively fast compiler that prioritizes quick startup. While the program runs, the engine collects information about how the code behaves.
If certain parts become "hot," they can be passed to a more advanced compiler that performs stronger optimizations. The optimized version can then replace the earlier version.
So, a simplified JIT process looks like this:
Source Code → Initial Compilation → Execute → Profile → Detect Hot Code → Optimize → Execute Optimized Code
The major advantage is that optimization can be based on real runtime behavior.
What is AOT Compilation?
Ahead-of-Time (AOT) compilation compiles code before the application runs, usually during the build process.
Instead of waiting until runtime to generate machine code, the compiler produces a native executable or other lower-level output in advance.
Languages such as C and C++ traditionally follow this model. AOT compilation can also compile intermediate representations, such as Java bytecode or .NET's Common Intermediate Language (CIL), into native machine code before execution.
The process looks more like:
Source Code → Compilation → Optimization → Machine Code → Execute
Because most of the compilation work happens before the application starts, the runtime has less compilation work to perform.
JIT vs AOT: Where Does the Optimization Happen?
This is where the difference gets more interesting.
AOT compilers can perform aggressive optimizations before the program starts. They have more time during the build process to analyze the application and generate optimized code.
However, they usually don't know exactly how the application will behave in production.
A compiler might know what the program could do, but not necessarily what it will do most frequently.
JIT has a different advantage.
Because the program is already running, a JIT compiler can collect profiling information from actual execution. It can identify which functions are frequently called, what types of values are being passed around, and which paths through the program are most common.
This gives JIT compilers access to information that traditional AOT compilation doesn't have at compile time.
The Trade-Off
Neither approach is simply "better." They make different trade-offs, and it is easier to understand them when we separate them into categories.
*- Performance Timing
*
JIT may introduce delays during execution because compilation happens at runtime, which can impact performance during program startup or when hot code is first encountered. In contrast, AOT moves all compilation work to build time, resulting in faster startup and more predictable runtime performance since the code is already fully compiled before execution begins.
- Optimization Quality
JIT can optimize based on real runtime behavior, making it highly adaptive and often more precise in long-running applications. AOT relies on static analysis and compile-time assumptions, but it can still apply powerful optimizations when given sufficient information during the build process.
- Runtime Overhead
JIT requires additional CPU and memory resources during execution for profiling and dynamic compilation. AOT, on the other hand, has minimal runtime overhead because the code is already compiled into machine instructions before execution begins.
- Flexibility
JIT can adapt dynamically to changing workloads and usage patterns, continuously refining performance as the program runs. AOT produces a fixed binary that behaves consistently once deployed, offering stability but less adaptability.
- Startup Behavior
JIT often has slower startup performance because execution begins before full optimization is complete and compilation happens progressively. AOT typically starts faster since all compilation and optimization are completed ahead of time.
- Long-Term Performance
JIT can improve performance over time as it learns from actual execution patterns and optimizes hot paths. AOT performance remains stable and predictable, but it does not evolve or adapt after deployment.
JIT essentially says:
"Let's observe the application and optimize based on what actually happens."
AOT says:
"Let's do as much optimization as possible before the application starts."
Can AOT Get the Best of Both Worlds?
The limitations of AOT aren't absolute.
AOT compilers can also use profiling data to make better optimization decisions. A program can be run in a controlled environment, its behavior can be profiled, and the collected data can then be used during compilation.
This is often referred to as Profile-Guided Optimization (PGO).
The challenge is that the profiling environment may not perfectly represent real production workloads.
For example, an application might behave differently depending on its users, traffic patterns, hardware, or data.
JIT compilation has an advantage here because it can profile the application in the actual environment where it is running.
The Interesting Possibility
This raises an interesting question:
What if we could use AOT compilation while still taking advantage of runtime profiling?
One possible approach is to:
- Compile the application ahead of time.
- Run it and collect profiling information.
- Use that data to produce a more optimized version.
- Switch to the optimized version at runtime.
This could provide some of the benefits of JIT optimization while keeping the advantages of AOT compilation.
The idea isn't necessarily new, and similar techniques have existed in different forms. However, it isn't as common as the traditional JIT or AOT approaches.
Final Thoughts
JIT and AOT are two different strategies for turning code into something a processor can execute efficiently.
JIT compiles and optimizes during execution, giving it access to real runtime behavior.
AOT compiles before execution, allowing more work to be done ahead of time and reducing runtime compilation overhead.
The choice ultimately depends on what matters most for the application: startup time, runtime adaptability, compilation overhead, deployment environment, or the ability to optimize based on real-world behavior.
And the boundary between JIT and AOT isn't as strict as it might seem. With techniques such as profiling-guided optimization and runtime code replacement, it's possible to combine ideas from both approaches.
