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For the general concept, see Bootstrapping. In computer science, bootstrapping is the technique for producing a self-compiling compiler – that is, a compiler (or assembler) written in the source programming language that it intends to compile. An initial core version of the compiler (the bootstrap compiler) is generated in a different ...
v. t. e. A cross compiler is a compiler capable of creating executable code for a platform other than the one on which the compiler is running. For example, a compiler that runs on a PC but generates code that runs on Android devices is a cross compiler. A cross compiler is useful to compile code for multiple platforms from one development host.
Programming language design and implementation. Programming languages are typically created by designing a form of representation of a computer program, and writing an implementation for the developed concept, [1] usually an interpreter or compiler. Interpreters are designed to read programs, usually in some variation of a text format, and ...
Tombstone diagram representing an Ada compiler written in C that produces machine code. Representation of the process of bootstrapping a C compiler written in C, by compiling it using another compiler written in machine code. To explain, the lefthand T is a C compiler written in C that produces machine code.
Artificial intelligence and machine learning. Bootstrapping is a technique used to iteratively improve a classifier 's performance. Typically, multiple classifiers will be trained on different sets of the input data, and on prediction tasks the output of the different classifiers will be combined.
Peephole optimization is an optimization technique performed on a small set of compiler -generated instructions, known as a peephole or window, [1] that involves replacing the instructions with a logically equivalent set that has better performance. For example: The term peephole optimization was introduced by William Marshall McKeeman in 1965.
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 ...
Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate of the value function. These methods sample from the environment, like Monte Carlo methods, and perform updates based on current estimates, like dynamic programming methods. [1]