Category Archives: C++

Exploring multilingual programming

Python is a prominent language in the ML and scientific computing space, and for good reason. Python is easy-to-learn and readable, and it offers a vast selection of libraries such as NumPy for numerical computation, Pandas for data manipulation, SciPy for scientific computing, TensorFlow, and PyTorch for deep learning, along with RDKit and Open Babel for cheminformatics. It is understandably an appealing choice for developers and researchers alike. However, a closer look at many common Python libraries reveals their foundations in C++. 

Revisiting C++ Advantages

Many of Python libraries including TensorFlow, PyTorch, and RDKit are all heavily-reliant on C++. C++ allows developers to manage memory and CPU resources more effectively than Python, making it a good choice when handling large volumes of data at a fast pace. A previous post on this blog discusses C++’s speed, its utility in GPU programming through CUDA, and the complexities of managing its libraries. Despite the steeper learning curve and verbosity compared to Python, the performance benefits of C++ are undeniable, especially in contexts where execution speed and memory management are critical.

Rust: A New Contender for High-Performance Computing

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C++ python bindings in 5 minutes

You don’t even need to use CMake!

Most of the time, we can use libraries like numpy (which is largely written in C) to speed up our calculations, which works when we are dealing with matrices or vectors – but sometimes loops are unavoidable. In those instances, it would be nice if we could use a compiled language such as C++ to remove the bottleneck.

This can be achieved extremely easily using pybind11, which enables us to export C++ functions and classes as importable python objects. We can do all of this very easily, without using CMake, using pybind11’s Pybind11Extension class, along with a modified setup.py. Pybind11 can be compiled from source or installed using:

pip install pybind11
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GEMMI: A Python Cookbook

General MacroMocelecular I/O, or GEMMI, is a C++ 11 header only library for low level crystalographic .

Because its header only it is certainly the easiest to access and use low level crystalographic C++ library, however GEMMI comes with python binding via Pybind11, making it arguably the easiest low level crystalographic library to access and use in python as well!

What follows is a cookbook of useful Python code that uses GEMMI to accomplish macromolecular crystalographic tasks.

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Should scientists learn C++?

Conventional wisdom dictates that compiled languages are slow to develop, can be slow to compile, but are fast to run. Interpreted languages are easy to use and do not require compilation but have sluggish performance. Like most people in scientific computing, the first two languages I learned were C++ and Python; I use Python every day but when, if ever, would I use C++?

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