Commit 1325906b authored by Martin Reinecke's avatar Martin Reinecke

more README

parent ab318a42
DUCC 0.1
========
Distinctly Useful Code Collection (DUCC)
========================================
This is a collection of basic programming tools which can be handy in many
situations.
This is a collection of basic programming tools for numerical computation,
including Fast Fourier Transforms, Spherical Harmonic Transforms, non-equispaced
Fourier transforms, as well as some concrete applications like 4pi convolution
on the sphere and gridding/degridding of radio interferometry data.
The code is written in C++17, but provides a simple and comprehensive Python
interface.
Installation
......@@ -31,6 +35,11 @@ DUCC and its mandatory dependencies can be installed via:
sudo apt-get install git python3 python3-pip python3-dev python3-pybind11 pybind11-dev
pip3 install --user git+https://gitlab.mpcdf.mpg.de/mtr/ducc.git
NOTE: compilation of the code will take a sinificant amount of time
(several minutes). Binary packages are deliberately not made available, since
much better performance can be achieved by compiling the code specifically for
the detected target CPU.
Installing multiple versions simultaneously
===========================================
......@@ -59,7 +68,8 @@ ducc.fft
--------
This package provides Fast Fourier, trigonometric and Hartley transforms with a
simple Python interface.
simple Python interface. It is an evolution of `popcketfft` and `pypocketfft`
which are currently used by `numpy` and `scipy`.
The central algorithms are derived from Paul Swarztrauber's FFTPACK code
(http://www.netlib.org/fftpack).
......@@ -75,20 +85,33 @@ Features
- supports prime-length transforms without degrading to O(N**2) performance
- has optional multi-threading support for multidimensional transforms
Design decisions and performance characteristics
- there is no internal caching of plans and twiddle factors, making the
interface as simple as possible
- 1D transforms are significantly slower than those provided by FFTW (if FFTW's
plan generation overhead is ignored)
- multi-D transforms in double precision perform fairly similar to FFTW with
FFTW_MEASURE; in single precision ducc.fft can be significantly faster.
ducc.sht
--------
This package provides efficient spherical harmonic trasforms (SHTs). Its code
is derived from `libsharp`.
is derived from `libsharp` ([https://arxiv.org/abs/1303.4945]), with accelerated
recurrence algorithms presented in
[https://www.jstage.jst.go.jp/article/jmsj/96/2/96_2018-019/_pdf].
ducc.healpix
------------
This library provides Python bindings for the most important
functionality in Healpix C++. The design goals are
- similarity to the C++ interface (while respecting some Python peculiarities)
This library provides Python bindings for the most important functionality
related to the HEALPix tesselation ([https://arxiv.org/abs/astro-ph/0409513]),
except for spherical harmonic transforms, which are covered vy `ducc.sht`.
The design goals are
- similarity to the interface of the HEALPix C++ library
(while respecting some Python peculiarities)
- simplicity (no optional function parameters)
- low function calling overhead
......@@ -99,6 +122,10 @@ ducc.totalconvolve
Library for high-accuracy 4pi convolution on the sphere, which generates a
total convolution data cube from a set of sky and beam `a_lm` and computes
interpolated values for a given list of detector pointings.
This code has evolved from the original `totalconvolver` algorithm described
in [https://arxiv.org/abs/astro-ph/0008227] vie the `conviqt` code
([https://arxiv.org/abs/1002.1050]).
Algorithmic details:
- the code uses `ducc.sht` SHTs and `ducc.fft` FFTs to compute the data cube
......@@ -111,12 +138,13 @@ Algorithmic details:
ducc.wgridder
-------------
Library for high-accuracy gridding/degridding of radio interferometry datasets
Library for high-accuracy gridding/degridding of radio interferometry datasets.
An earlier version of this code has been integrated into `wsclean`
([https://arxiv.org/abs/1407.1943], [https://sourceforge.net/projects/wsclean/])
as the `wgridder` component.
Programming aspects
- written in C++17, fully portable
- shared-memory parallelization via and C++ threads.
- Python interface available
- shared-memory parallelization via standard C++ threads.
- kernel computation is performed on the fly, avoiding inaccuracies
due to table lookup and reducing overall memory bandwidth
......
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