On the other hand, interpreted languages execute the source code instruction-by-instruction, avoiding translating the entire source code to machine code. This two-step process (compile then execute) is evident from the way we work with C code. Perhaps the most famous compiled languages are C and C++. When the code is actually executed, it uses this optimized machine code to run the original source code. This compilation step typically finds certain optimizations to make the code faster. Roughly, a compiled language translates the entire program to machine code prior to running the code. Note that the distinction between these two is less black-and-white than I say below, as many hybrids exist today. To understand the advantage of JIT compilation, we first have to understand the difference between compiled and interpreted languages. Specifically, we’ll show how JAX implements a JIT option and the advantages of using it. Here, we explore one recent application of ideas from compilers to machine learning: just-in-time (JIT) compilation for machine learning libraries. However, a less-hyped topic - but an extremely important one - is the actual implementation of these methods using programming languages and compilers. Most research in machine learning and computational statistics focuses on advancing methodology.
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