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Comment by bscphil | Hacker News Reader

> Seq is a Python-compatible language, and the vast majority of Python programs should work without any modifications

> Seq is able to outperform Python code by up to 160x.

So ... a reimplementation of Python that can outperform cpython by over 100 times? I know literally nothing about this project, but I have to say that rings pretty false for me. Hell, even PyPy has trouble with many applications. (Plus they're claiming to outperform "equivalent" C code by 2x.)

Even if the performance claims are overblown, it's always nice to see new work on compiled languages with easy-to-read syntax. It's hard to beat Python for an education / prototyping language, so I will definitely be giving this a look.

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It's probably in the same sense that Numpy is much faster than doing matrix operations with pure Python arrays and Python for-loops.

I also know literally nothing about this particular project, but why not? If you support a small restricted subset of Python it's completely doable under certain conditions for specific types of programs. E.g., Numba can easily outperform Python 100-1000x in numerical applications (done it myself multiple times), simply because it jit-compiles the code by first translating it to LLVM IR.

(minor contributor) I’ve been following the project for a while and pleasantly surprised by the ability to manually convert Python programs to Seq without needing to make too many changes. Note, most of my experimentation has been with smallish programs I’ve written. I like that I can still think “Pythonically” and compose mostly correct Seq code using familiar idioms, e.g. list/set/dict comprehensions. The standard library is very readable and a source for “from import” type functionality. Some of the other features I’ve come to appreciate: pipeline operator |>, JIT compile or create an executable (seqc run, seqc build), match statements, and strong typing.

> If you support a small restricted subset of Python

That's why I quoted their claim that the "vast majority" of Python programs run unmodified. Even PyPy barely achieves that. To really get 100x performance over Python (and even supposedly beat C) with a compiler that works on most unmodified Python code would be an extraordinary achievement.

That seems misrepresenting the original points: it can run the vast majority of python programs unmodified AND in some cases outperform Python - not at the same time.

Look at the link: https://github.com/seq-lang/seq It says 96% of the code is C++ in the "Languages" box on the right. C ( and C++ and Rust) outperforms Python in benchmarks and certain optimized C code can do 160x over very naive Python. So this is very possible, though the routines tested are probably cherry picked for bragging rights.

> We show that many important and widely-used NGS algorithms can be made up to 160× faster than their Python counterparts as well as 2× faster than the existing hand-optimized C++ implementations

It seems it's better to think of this particular claim as "we made a C++ algorithm that is 2x faster than the previous SotA C++ algorithm" (with the help of a heavily optimized DSL).

Most newer languages will give you multiple orders of magnitude better performance than python.

Python’s main advantage was that it was easier than some of its competitors (C++/Java). But that is no longer the case with modern languages (Nim/Crystal/Julia/JavaScript) being both faster and comparably as easy (or easier).

It is now coasting off its momentum, mostly do to the vast amount of (usually poorly designed) open source libraries. That and Jupyter.

Probably, it can outperform generic python specifically for genomics payloads, versus python code/C code.

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