# 10.3: Goals and Background of the FFTW Project

The FFTW project, begun in 1997 as a side project of the authors Frigo and Johnson as graduate students at MIT, has gone through several major revisions, and as of 2008 consists of more than 40,000 lines of code. It is difficult to measure the popularity of a free-software package, but (as of 2008) FFTW has been cited in over 500 academic papers, is used in hundreds of shipping free and proprietary software packages, and the authors have received over 10,000 emails from users of the software. Most of this chapter focuses on performance of FFT implementations, but FFTW would probably not be where it is today if that were the only consideration in its design. One of the key factors in FFTW's success seems to have been its flexibility in addition to its performance. In fact, FFTW is probably the most flexible DFT library available:

• FFTW is written in portable C and runs well on many architectures and operating systems.
• FFTW computes DFTs in $$O(n\log n)$$ time for any length $$n$$. (Most other DFT implementations are either restricted to a subset of sizes or they become $$\Theta (n^2)$$ for certain values of $$n$$, for example when $$n$$ is prime.
• FFTW imposes no restrictions on the rank (dimensionality) of multi-dimensional transforms. (Most other implementations are limited to one-dimensional, or at most two- and three-dimensional data.)
• FFTW supports multiple and/or strided DFTs; for example, to transform a 3-component vector field or a portion of a multi-dimensional array. (Most implementations support only a single DFT of contiguous data.)
• FFTW supports DFTs of real data, as well as of real symmetric/anti-symmetric data (also called discrete cosine/sine transforms).

Our design philosophy has been to first define the most general reasonable functionality, and then to obtain the highest possible performance without sacrificing this generality. In this section, we offer a few thoughts about why such flexibility has proved important, and how it came about that FFTW was designed in this way.

FFTW's generality is partly a consequence of the fact the FFTW project was started in response to the needs of a real application for one of the authors (a spectral solver for Maxwell's equations, which from the beginning had to run on heterogeneous hardware. Our initial application required multi-dimensional DFTs of three-component vector fields (magnetic fields in electromagnetism), and so right away this meant: (i) multi-dimensional FFTs; (ii) user-accessible loops of FFTs of discontiguous data; (iii) efficient support for non-power-of-two sizes (the factor of eight difference between $$n\times n\times n$$ and $$2n\times 2n\times 2n$$ was too much to tolerate); and (iv) saving a factor of two for the common real-input case was desirable. That is, the initial requirements already encompassed most of the features above, and nothing about this application is particularly unusual.

Even for one-dimensional DFTs, there is a common misperception that one should always choose power-of-two sizes if one cares about efficiency. Thanks to FFTW's code generator (described in Generating Small FFT Kernels), we could afford to devote equal optimization effort to any $$n$$ with small factors (2, 3, 5, and 7 are good), instead of mostly optimizing powers of two like many high-performance FFTs. As a result, to pick a typical example on the 3 GHz Core Duo processor of Fig. 10.1.1 $$n=3600=2^4\cdot 3^2\cdot 5^2$$ and $$n=3840=2^8\cdot 3\cdot 5$$ both execute faster than $$n=4096=2^{12}$$. (And if there are factors one particularly cares about, one can generate code for them too.)

One initially missing feature was efficient support for large prime sizes; the conventional wisdom was that large-prime algorithms were mainly of academic interest, since in real applications (including ours) one has enough freedom to choose a highly composite transform size. However, the prime-size algorithms are fascinating, so we implemented Rader's $$O(n\log n)$$ prime-$$n$$ algorithm purely for fun, including it in FFTW 2.0 (released in 1998) as a bonus feature. The response was astonishingly positive—even though users are (probably) never forced by their application to compute a prime-size DFT, it is rather inconvenient to always worry that collecting an unlucky number of data points will slow down one's analysis by a factor of a million. The prime-size algorithms are certainly slower than algorithms for nearby composite sizes, but in interactive data-analysis situations the difference between 1 ms and 10 ms means little, while educating users to avoid large prime factors is hard.

Another form of flexibility that deserves comment has to do with a purely technical aspect of computer software. FFTW's implementation involves some unusual language choices internally (the FFT-kernel generator, described in Generating Small FFT Kernels, is written in Objective Caml, a functional language especially suited for compiler-like programs), but its user-callable interface is purely in C with lowest-common-denominator datatypes (arrays of floating-point values). The advantage of this is that FFTW can be (and has been) called from almost any other programming language, from Java to Perl to Fortran 77. Similar lowest-common-denominator interfaces are apparent in many other popular numerical libraries, such as LAPACK. Language preferences arouse strong feelings, but this technical constraint means that modern programming dialects are best hidden from view for a numerical library.

Ultimately, very few scientific-computing applications should have performance as their top priority. Flexibility is often far more important, because one wants to be limited only by one's imagination, rather than by one's software, in the kinds of problems that can be studied.

### Contributor

• ContribEEBurrus