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Design: Design aspects are considered, such as types, syntax, semantics, and library usage to develop a language. [10] Consideration: Syntax, implementation, and other factors are considered. Languages like Python interpret code at runtime, whereas languages like C++ follow an approach of basing its compiler off of C's compiler. [11]
A JIT compiler therefore has to make a trade-off between the compilation time and the quality of the code it hopes to generate. Startup time can include increased IO-bound operations in addition to JIT compilation: for example, the rt.jar class data file for the Java Virtual Machine (JVM) is 40 MB and the JVM must seek a lot of data in this ...
Otherwise, the bootstrap compiler is to be written in one of the programming languages which does exist on the target machine, and that compiler will generate something which can execute on the target, including a high-level programming language, an assembly language, an object file, or even machine code.
Compiler design. Regardless of the exact number of phases in the compiler design, the phases can be assigned to one of three stages. The stages include a front end, a middle end, and a back end. The front end scans the input and verifies syntax and semantics according to a specific source language.
When code generation occurs at runtime, as in just-in-time compilation (JIT), it is important that the entire process be efficient with respect to space and time. For example, when regular expressions are interpreted and used to generate code at runtime, a non-deterministic finite-state machine is often generated instead of a deterministic one, because usually the former can be created more ...
A tracing JIT compiler goes through various phases at runtime. First, profiling information for loops is collected. After a scope loop has been identified, a special tracing phase is entered, which records all executed operations of that loop. This sequence of operations is called a trace. The trace is then optimized and compiled to machine code.
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PyPy (/ ˈ p aɪ p aɪ /) is an implementation of the Python programming language. [2] PyPy often runs faster than the standard implementation CPython because PyPy uses a just-in-time compiler. [3] Most Python code runs well on PyPy except for code that depends on CPython extensions, which either does not work or incurs some overhead when run ...