sparsepp
pybind11
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sparsepp | pybind11 | |
---|---|---|
4 | 42 | |
1,230 | 14,741 | |
- | 1.7% | |
1.1 | 8.7 | |
almost 1 year ago | 6 days ago | |
C++ | C++ | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
sparsepp
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Updating map_benchmarks: Send your hashmaps!
Also, I'm testing https://github.com/greg7mdp/sparsepp which is based on google's sparsehash
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Who here is ready to see some 64MB blocks on mainnet?
Hey thanks for the mention /u/jtoomim. I see that you used sparsepp in CoinsViewCache. A phmap::flat_hash_map would probably be similar for memory usage, but likely faster (because of parallel lookup with SSE instructions and contiguous memory storage in one array).
pybind11
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Experience using crow as web server
I'm investigating using C++ to build a REST server, and would love to know of people's experiences with Crow-- or whether they would recommend something else as a "medium-level" abstraction C++ web server. As background, I started off experimenting with Python/FastAPI, which is great, but there is too much friction to translate from pybind11-exported C++ objects to the format that FastAPI expects, and, of course, there are inherent performance limitations using Python, which could impact scaling up if the project were to be successful.
- Swig – Connect C/C++ programs with high-level programming languages
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returning numpy arrays via pybind11
I have a C++ function computing a large tensor which I would like to return to Python as a NumPy array via pybind11.
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I created smooth_lines python module, great for drawing software
This is based on the Google Ink Stroke Modeler C++ library, and using pybind11 to make it available on python.
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Facial Landmark Detection with C++
pybind11 makes it easy to call C++ from Python if you want to mix.
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Python’s Multiprocessing Performance Problem
If you've never used Pybind before these pybind tests[1] and this repo[2] have good examples you can crib to get started (in addition to the docs). Once you handle passing/returning/creating the main data types (list, tuple, dict, set, numpy array) the first time, then it's mostly smooth sailing.
Pybind offers a lot of functionality, but core "good parts" I've found useful are (a) use a numpy array in Python and pass it to a C++ method to work on, (b) pass your python data structure to pybind and then do work on it in C++ (some copy overhead), and (c) Make a class/struct in C++ and expose it to Python (so no copying overhead and you can create nice cache-aware structs, etc.).
[1] https://github.com/pybind/pybind11/blob/master/tests/test_py...
- Making Python Web Application with C++ Backend
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Using pybind11 with minGW to cross compile pyhton module for Windows
I have a python module for which the logic is written in C++ and I use pybind11 to expose the objects and functions to Python.
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IPC communication between rust, c++, and python
Reading from Python requires a wrapper, using pybind11 this is fairly done.
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[ADVICE] Python to C++
Also I can highly recommend starting using C++ to augment your Python code, i.e. find the parts that are slow or undoable in Python and write those in C++ then expose them as Python functions. You can use https://github.com/pybind/pybind11 to call C++ code from Python.
What are some alternatives?
PEGTL - Parsing Expression Grammar Template Library
PyO3 - Rust bindings for the Python interpreter
Inja - A Template Engine for Modern C++
nanobind - nanobind: tiny and efficient C++/Python bindings
Hashmaps - Various open addressing hashmap algorithms in C++
Optional Argument in C++ - Named Optional Arguments in C++17
LSHBOX - A c++ toolbox of locality-sensitive hashing (LSH), provides several popular LSH algorithms, also support python and matlab.
setuptools-rust - Setuptools plugin for Rust support
dynamic_bitset - Simple Useful Libraries: C++17/20 header-only dynamic bitset
sol2 - Sol3 (sol2 v3.0) - a C++ <-> Lua API wrapper with advanced features and top notch performance - is here, and it's great! Documentation: