# Options parameters¶

Aside from the mandatory inputs (dimension, type, nonuniform points, strengths or coefficients, and, in C++/C/Fortran/MATLAB, sign of the imaginary unit and tolerance) FINUFFT has optional parameters. These adjust the workings of the algorithm, change the output format, or provide debug/timing text to stdout. Sensible default options are chosen, so that the new user need not worry about changing them. However, users wanting to try to increase speed or see more timing breakdowns will want to change options from their defaults. See each language doc page for how this is done, but is generally by creating an options structure, changing fields from their defaults, then passing this (or a pointer to it) to the simple, vectorized, or guru makeplan routines. Recall how to do this from C++:

// (... set up M,x,c,tol,N, and allocate F here...)
nufft_opts* opts;
finufft_default_opts(opts);
opts->debug = 1;
int ier = finufft1d1(M,x,c,+1,tol,N,F,opts);


This setting produces more timing output to stdout.

Warning

In C/C++ and Fortran, don’t forget to call the command which sets default options (finufft_default_opts or finufftf_default_opts) before you start changing them and passing them to FINUFFT.

Here is a 1-line summary of each option, taken from the code (the header include/nufft_opts.h):

  // FINUFFT options:
// data handling opts...
int modeord;            // (type 1,2 only): 0 CMCL-style increasing mode order
//                  1 FFT-style mode order
int chkbnds;            // 0 don't check NU pts in [-3pi,3pi), 1 do (<few % slower)

// diagnostic opts...
int debug;              // 0 silent, 1 some timing/debug, or 2 more
int spread_debug;       // spreader: 0 silent, 1 some timing/debug, or 2 tonnes
int showwarn;           // 0 don't print warnings to stderr, 1 do

// algorithm performance opts...
int nthreads;           // number of threads to use, or 0 uses all available
int fftw;               // plan flags to FFTW (FFTW_ESTIMATE=64, FFTW_MEASURE=0,...)
int spread_sort;        // spreader: 0 don't sort, 1 do, or 2 heuristic choice
double upsampfac;       // upsampling ratio sigma: 2.0 std, 1.25 small FFT, 0.0 auto
int maxbatchsize;       // (vectorized ntr>1 only): max transform batch, 0 auto


Here are their default settings (from src/finufft.cpp:finufft_default_opts):

  o->modeord = 0;
o->chkbnds = 1;

o->debug = 0;
o->showwarn = 1;

o->fftw = FFTW_ESTIMATE;
o->upsampfac = 0.0;
o->maxbatchsize = 0;


As for quick advice, the main options you’ll want to play with are:

• modeord to flip (“fftshift”) the Fourier mode ordering
• debug to look at timing output (to determine if your problem is spread/interpolation dominated, vs FFT dominated)
• nthreads to run with a different number of threads than the current maximum available through OpenMP (a large number can sometimes be detrimental, and very small problems can sometimes run faster on 1 thread)
• fftw to try slower plan modes which give faster transforms. The next natural one to try is FFTW_MEASURE (look at the FFTW3 docs)

See Troubleshooting for good advice on trying options, and read the full options descriptions below.

Warning

Some of the options are for experts only, and will result in slow or incorrect results. Please test options in a small known test case so that you understand their effect.

## Documentation of all options¶

### Data handling options¶

modeord: Fourier coefficient frequency index ordering in every dimension. For type 1, this is for the output; for type 2 the input. It has no effect in type 3. Here we use N to denote the size in any of the relevant dimensions:

• if modeord=0: frequency indices are in increasing ordering, namely $$\{-N/2,-N/2+1,\dots,N/2-1\}$$ if $$N$$ is even, or $$\{-(N-1)/2,\dots,(N-1)/2\}$$ if $$N$$ is odd. For example, if N=6 the indices are -3,-2,-1,0,1,2, whereas if N=7 they are -3,-2,-1,0,1,2,3. This is called “CMCL ordering” since it is that of the CMCL NUFFT.

• if modeord=1: frequency indices are ordered as in the usual FFT, increasing from zero then jumping to negative indices half way along, namely $$\{0,1,\dots,N/2-1,-N/2,-N/2+1,\dots,-1\}$$ if $$N$$ is even, or $$\{0,1,\dots,(N-1)/2,-(N-1)/2,\dots,-1\}$$ if $$N$$ is odd. For example, if N=6 the indices are 0,1,2,-3,-2,-1, whereas if N=7 they are 0,1,2,3,-3,-2,-1.

Note

The index sets are the same in the two modeord choices; their ordering differs only by a cyclic shift. The FFT ordering cyclically shifts the CMCL indices $$\mbox{floor}(N/2)$$ to the left (often called an “fftshift”).

chkbnds: whether to check the nonuniform points lie in the correct bounds.

• chkbnds=0: input nonuniform points in the arrays x, y, z, are fed straight into the spreader which assumes (for speed) that they lie in $$[-3\pi,3\pi)$$. Points outside of this will then cause a segfault.
• chkbnds=1: the nonuniform points are checked to lie in this interval, and if any are found not to, the library exits with an error code and message to stderr. The trade-off is that simply doing this checking can lose several % in overall speed, especially in low-precision 3D transforms.

### Diagnostic options¶

debug: Controls the amount of overall debug/timing output to stdout.

• debug=0 : silent
• debug=1 : print some information
• debug=2 : prints more information

• spread_debug=0 : silent
• spread_debug=1 : prints some timing information
• spread_debug=1 : prints lots. This can print thousands of lines since it includes one line per subproblem.

showwarn: Whether to print warnings (these go to stderr).

• showwarn=0 : suppresses such warnings
• showwarn=1 : prints warnings

### Algorithm performance options¶

nthreads: Number of threads to use. This sets the number of threads FINUFFT will use in FFTW, bin-sorting, and spreading/interpolation steps. This number of threads also controls the batch size for vectorized transforms (ie ntr>1 here). Setting nthreads=0 uses all threads available. For repeated small problems it can be advantageous to use a small number, such as 1.

fftw: FFTW planner flags. This number is simply passed to FFTW’s planner; the flags are documented here. A good first choice is FFTW_ESTIMATE; however if you will be making multiple calls, consider FFTW_MEASURE, which could spend many seconds planning, but will give a faster run-time when called again from the same process. These macros are bit-wise flags defined in /usr/include/fftw3.h on a linux system; they currently have the values FFTW_ESTIMATE=64 and FFTW_MEASURE=0. Note that FFTW plans are saved (by FFTW’s library) automatically from call to call in the same executable (incidentally, also in the same MATLAB/octave or python session); there is a small overhead for lookup of such plans, which with many repeated small problems can motivate use of the guru interface.

• spread_sort=0 : never sorts
• spread_sort=1 : always sorts
• spread_sort=2 : uses a heuristic to decide whether to sort or not.

The heuristic bakes in empirical findings such as: generally it is not worth sorting in 1D type 2 transforms, or when the number of nonuniform points is small. Do not change this from its default unless you obsever.

spread_kerevalmeth: Kernel evaluation method in spreader/interpolator. This should not be changed from its default value, unless you are an expert wanting to compare against outdated

• spread_kerevalmeth=0 : direct evaluation of sqrt(exp(beta(1-x*x))) in the ES kernel. This is outdated, and it’s only possible use could be in exploring upsampling factors $$\sigma$$ different from standard (see below).
• spread_kerevalmeth=1 : use Horner’s rule applied to piecewise polynomials with precomputed coefficients. This is faster, less brittle to compiler/glibc/CPU variations, and is the recommended approach. It only works for the standard upsampling factors listed below.

spread_kerpad: whether to pad the number of direct kernel evaluations per dimension and per nonuniform point to a multiple of four; this can help SIMD vectorization. It only applies to the (outdated) spread_kerevalmeth=0 choice. There is thus little reason for the nonexpert to mess with this option.

• spread_kerpad=0 : do not pad
• spread_kerpad=0 : pad to next multiple of four

upsampfac: This is the internal real factor by which the FFT (fine grid) is chosen larger than the number of requested modes in each dimension, for type 1 and 2 transforms. We have built efficient kernels for only two settings, as follows. Otherwise, setting it to zero chooses a good heuristic:

• upsampfac=0.0 : use heuristics to choose upsampfac as one of the below values, and use this value internally. The value chosen is visible in the text output via setting debug>=2. This setting is recommended for basic users; however, if you seek more performance it is quick to try the other of the below.
• upsampfac=2.0 : standard setting of upsampling. This is necessary if you need to exceed 9 digits of accuracy.
• upsampfac=1.25 : low-upsampling option, with lower RAM, smaller FFTs, but wider spreading kernel. The latter can be much faster than the standard when the number of nonuniform points is similar or smaller to the number of modes, and/or if low accuracy is required. It is especially much (2 to 3 times) faster for type 3 transforms. However, the kernel widths $$w$$ are about 50% larger in each dimension, which can lead to slower spreading (it can also be faster due to the smaller size of the fine grid). Because the kernel width is limited to 16, currently, thus only 9-digit accuracy can currently be reached when using upsampfac=1.25.

spread_thread: in the case of multiple transforms per call (ntr>1, or the “many” interfaces), controls how multithreading is used to spread/interpolate each batch of data.

• spread_thread=0 : makes an automatic choice between the below. Recommended.

• spread_thread=1 : acts on each vector in the batch in sequence, using multithreaded spread/interpolate on that vector. It can be slightly better than 2 for large problems.

• spread_thread=2 : acts on all vectors in a batch (of size chosen typically to be the number of threads) simultaneously, assigning each a thread which performs a single-threaded spread/interpolate. It is much better than 1 for all but large problems. (Historical note: this was used by Melody Shih for the original “2dmany” interface in 2018.)

Note

Historical note: A former option 3 has been removed. This was like 2 except allowing nested OMP parallelism, so multi-threaded spread-interpolate was used for each of the vectors in a batch in parallel. This was used by Andrea Malleo in 2019. We have not yet found a case where this beats both 1 and 2, hence removed it due to complications with changing the OMP nesting state in both old and new OMP versions.

maxbatchsize: in the case of multiple transforms per call (ntr>1, or the “many” interfaces), set the largest batch size of data vectors. Here 0 makes an automatic choice. If you are unhappy with this, then for small problems it should equal the number of threads, while for large problems it appears that 1 often better (since otherwise too much simultaneous RAM movement occurs). Some further work is needed to optimize this parameter.

spread_nthr_atomic: if non-negative: for numbers of threads up to this value, an OMP critical block for add_wrapped_subgrid is used in spreading (type 1 transforms). Above this value, instead OMP atomic writes are used, which scale better for large thread numbers. If negative, the heuristic default in the spreader is used, set in src/spreadinterp.cpp:setup_spreader().

spread_max_sp_size: if positive, overrides the maximum subproblem (chunking) size for multithreaded spreading (type 1 transforms). Otherwise the default in the spreader is used, set in src/spreadinterp.cpp:setup_spreader(), which we believe is a decent heuristic for Intel i7 and xeon machines.