Link: https://dl.acm.org/doi/pdf/10.1145/3276490

The paper outlines the Julia programming language’s some most important design choices, and explains how they build a bridge between user-friendliness and performance.

The paper provided with a few benchmarks, to compare its performance with a C baseline, along with other dynamic languages like Python, MATLAB, JavaScript, and so on. While other dynamic programming languages suffer great performance loss, due to its dynamism, Julia can compete relatively close with the C/C++ baseline, with up to native performance in a few cases, most of the benchmarks are within 2x of C or C++, while Python can suffer more than 70x slower performance than C++.

This is significant, as it may eliminate the “prototype in dynamic language, then reimplement in static language for faster performance” cycle, eliminating extra time on coding to achieve efficiency without sacrificing much performance.

Some key takeouts from this paper:

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This is a holiday reading summary. I recently came across two interesting blogs on fundamental concepts in computer science, both with the title “10 Papers Every Programmer Should Read (At Least Twice)”. One could be found in here, and another one in Fogus’ blog. Topics of these papers range from Programming Language theories, functional programming, to Lamport’s distributed system theories. I will read and summarize some of them in my blog. It’ll be 20 papers, and 40 paper-readings to do if I do read each one twice. So, it might be a long time before all is finished.

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I’ve been auditing a course in computer language implementation and particularly interested in parser generator. Just spent an afternoon reading about the Python parser generator PLY. It’s a pure Python Implementation of Lex and Yacc. And here is the PLY documentation I’ve been reading the whole afternoon.

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