https://github.com/ahbarnett/nufft-bench
Benchmarking for NUFFT libraries
https://github.com/ahbarnett/nufft-bench
Last synced: 3 months ago
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Benchmarking for NUFFT libraries
- Host: GitHub
- URL: https://github.com/ahbarnett/nufft-bench
- Owner: ahbarnett
- License: other
- Created: 2018-01-22T21:19:40.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-06-15T03:52:31.000Z (almost 7 years ago)
- Last Synced: 2025-01-23T18:51:36.181Z (5 months ago)
- Language: Matlab
- Homepage:
- Size: 4.24 MB
- Stars: 5
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# nufft-bench
Generate figures and tables which compare performance of various NUFFT
(non-uniform fast Fourier transform) codes.
Written in MATLAB/octave.
Alex Barnett and Jeremy Magland; contributions by Joakim Anden.
January-May 2018.
## Dependencies
Tested on: MATLAB R2016b
Some codes require: https://github.com/ahbarnett/memorygraph
as well as `vline` from matlabcentral.
## Installing various NUFFT packages
Here's how to set up the NUFFT codes so that `nufft-bench` works.
Each of the below package directories
should be placed within the parent directory
of `nufft-bench`, ie a sibling to `nufft-bench`.
If you want to change this location, edit
`setuppaths.m`.
First set up your compiler to be GCC 7.3.0 (apart from NFFT for which
we seem to have to use GCC 6.4.0; see below).
### Installing NFFT
Download current source from here:
https://www-user.tu-chemnitz.de/~potts/nfft/download.php
Extract to directory `nfft-3.3.2`, which should be placed alongside `nufft-bench`
as explained above.
`cd` to `nfft-3.3.2` and run
```
> ./configure --with-matlab-arch=glnxa64 --with-matlab=/home/magland/MATLAB/R2017a --enable-all --enable-openmp
```
where `/home/magland/MATLAB/R2017a` is the path to your matlab installation,
and set `glnxa64` to be the appropriate architecture.
```
> make
```
If you want to do single thread as well, repeat the above exercise and
omit the following flags in the configuration:
`--enable-all` and `--enable-openmp`
Then change that directory name to `nfft-3.3.2-single-thread`
If you use different locations, edit `addnfftpath.m` to match.
Note: we find success with gcc 4.8.5 and 6.4.0. We find compilation
fails for gcc 7.2.0 and 7.3.0 with:
```
cc1: internal compiler error: in gimplify_modify_expr, at gimplify.c:5638
```
This gcc bug seems to be slated to be fixed in 7.4. Thus we standardized
to 6.4.0 for NFFT.
### Installing FINUFFT
See https://github.com/ahbarnett/finufft
This is multithreaded.
To change the compiler cmd create a file `make.inc` in the main directory
containing `CXX=g++-7`, for instance.
### Installing CMCL NUFFT
This the Greengard-Lee fortran code from 2009-2014, and is single-thread only.
We use nufftall version 1.3.3.
MEX binaries are shipped. However, for fairness we recompiled the binaries
on our test machine with flags ``-fPIC -Ofast -funroll-loops -march=native``.
Our compilation is done by ``patches/rebuild_cmcl_mex.sh``
See https://cims.nyu.edu/cmcl/nufft/nufft.html
### Installing BART NUFFT
This is a new (2016) MRI reconstruction package with a MATLAB interface.
See: https://mrirecon.github.io/bart/
The NUFFT is 3D only, and is by Frank Ong and Martin Uecker.
It uses all available threads.
The MATLAB interface writes data to a tempfile, then makes a system
call to an executable `bart nufft` which reads in the data.
We have to be careful to use problem sizes that don't
penalize this.
We installed v 0.4.02 from
https://github.com/mrirecon/bart/releases/tag/v0.4.02
Instructions to make are in the `README`.
On our EL7 (centos) system
we found we had to change the following lines in their `Makefile`
in order for openblas to be found:
```
OPT = -O3 -ffast-math -I/usr/include/openblas/
BLAS_L := -L$(BLAS_BASE)/lib -llapack -lblas -lopenblas
BLAS_H := -I$(BLAS_BASE)/include -I$(BLAS_BASE)/include/openblas
```
We then did
```
make NOLAPACKE=1
```
which compiles a local copy of lapacke rather than have to install `lapacke-dev`.
We found the nonuniform point periodic box by trial and error;
it is [-N/2,N/2]^d where N is the number of modes in each dimension
(we assume equal).
We found the normalization is (sqrt(N^3)/prefac) where prefac was
found by trial and error to be the constant 1.00211.
This choice improves accuracy from 3 to 5 digits.
This is strange, and probably derives from BART's spreading kernel.
In addition, points near box edges incorrectly wrap, giving O(1) errors.
Uecker kindly sent us a patched `grid.c` which you may find in `patches`.
Replace `bart-0.4.02/src/noncart/grid.c` with this file and recompile.
See `bart_*.m`
### Installing MIRT (Fessler) NUFFT
The complete package is at:
https://web.eecs.umich.edu/~fessler/irt/fessler.tgz
There is no version number; however, the `nufft` directory has the most recent
modifications on Dec 13, 2016.
All MEX files are included; no compilation is needed.
The precomputation is very expensive in time and RAM (up to 100x slower than
the apply when limited to one thread).
The apply appears to be a single-threaded sparse matvec
followed by fft, both done in native MATLAB, which (amazingly) beats all
others in terms of speed in 1D and 2D.