Performance – how fast a program runs – matters a great deal in computing. In tasks like heavy numerical simulation, processing large files, or running many concurrent operations, a few extra seconds (or minutes) can make a big difference. At one end of the spectrum are low-level compiled languages like C++ that give programmers direct control of the hardware, and at the other end are high-level languages like Python that prioritize ease of use but may incur overhead. Java sits in the middle as a managed language with a sophisticated runtime, while Julia is a newer language explicitly designed for scientific and numerical computing with high performance in mind. In this article, we compare how Python, C++, Java, and Julia typically perform on three kinds of tasks – numerical computation, file I/O, and concurrency – and explain what language features and architectures cause the differences. Illustration: The speed of code Numerical Computation Python: Pure Python code (e...
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