Bit-reversal permutation

In applied mathematics, a bit-reversal permutation is a permutation of a sequence of n items, where n = 2k is a power of two. It is defined by indexing the elements of the sequence by the numbers from 0 to n − 1 and then reversing the binary representations of each of these numbers (padded so that each of these binary numbers has length exactly k). Each item is then mapped to the new position given by this reversed value. The bit reversal permutation is an involution, so repeating the same permutation twice returns to the original ordering on the items.

This permutation can be applied to any sequence in linear time while performing only simple index calculations. It has applications in the generation of low-discrepancy sequences and in the evaluation of fast Fourier transforms.

Example

Consider the sequence of eight letters abcdefgh. Their indexes are the binary numbers 000, 001, 010, 011, 100, 101, 110, and 111, which when reversed become 000, 100, 010, 110, 001, 101, 011, and 111. Thus, the letter a in position 000 is mapped to the same position (000), the letter b in position 001 is mapped to the fifth position (the one numbered 100), etc., giving the new sequence aecgbfdh. Repeating the same permutation on this new sequence returns to the starting sequence.

Writing the index numbers in decimal (but, as above, starting with position 0 rather than the more conventional start of 1 for a permutation), the bit-reversal permutations of size 2k, for k = 0, 1, 2, 3, ... are

  • k = 0: 0
  • k = 1: 0 1
  • k = 2: 0 2 1 3
  • k = 3: 0 4 2 6 1 5 3 7
  • k = 4: 0 8 4 12 2 10 6 14 1 9 5 13 3 11 7 15

(sequence A030109 in the OEIS)
Each permutation in this sequence can be generated by concatenating two sequences of numbers: the previous permutation, doubled, and the same sequence with each value increased by one. Thus, for example doubling the length-4 permutation 0 2 1 3 gives 0 4 2 6, adding one gives 1 5 3 7, and concatenating these two sequences gives the length-8 permutation 0 4 2 6 1 5 3 7.

Generalizations

The generalization to n = bm for an arbitrary integer b > 1 is a base-b digit-reversal permutation, in which the base-b (radix-b) digits of the index of each element are reversed to obtain the permuted index. A further generalization to arbitrary composite sizes is a mixed-radix digit reversal (in which the elements of the sequence are indexed by a number expressed in a mixed radix, whose digits are reversed by the permutation).

Permutations that generalize the bit-reversal permutation by reversing contiguous blocks of bits within the binary representations of their indices can be used to interleave two equal-length sequences of data in-place.[1]

There are two extensions of the bit-reversal permutation to sequences of arbitrary length. These extensions coincide with bit-reversal for sequences whose length is a power of 2, and their purpose is to separate adjacent items in a sequence for the efficient operation of the Kaczmarz algorithm. The first of these extensions, called Efficient Ordering[2], operates on composite numbers, and it is based on decomposing the number into its prime components.

The second extension, called EBR (Extended Bit-Reversal)[3], is similar in spirit to bit-reversal. Given an array of size n, EBR fills the array with a permutation of the numbers in the range in linear time. Successive numbers are separated in the permutation by at least positions.

Applications

Bit reversal is most important for radix-2 Cooley–Tukey FFT algorithms, where the recursive stages of the algorithm, operating in-place, imply a bit reversal of the inputs or outputs. Similarly, mixed-radix digit reversals arise in mixed-radix Cooley–Tukey FFTs.[4]

The bit reversal permutation has also been used to devise lower bounds in distributed computation.[5]

The Van der Corput sequence, a low-discrepancy sequence of numbers in the unit interval, is formed by reinterpreting the indexes of the bit-reversal permutation as the fixed-point binary representations of dyadic rational numbers.

In musical studies, the bit-reversal permutation has also been used to correlate ranking functions of metric weight and classical-corpus onset frequencies in a common-time (4/4) measure.[6]

Algorithms

Mainly because of the importance of fast Fourier transform algorithms, numerous efficient algorithms for applying a bit-reversal permutation to a sequence have been devised.[7]

Because the bit-reversal permutation is an involution, it may be performed easily in place (without copying the data into another array) by swapping pairs of elements. In the random-access machine commonly used in algorithm analysis, a simple algorithm that scans the indexes in input order and swaps whenever the scan encounters an index whose reversal is a larger number would perform a linear number of data moves.[8] However, computing the reversal of each index may take a non-constant number of steps. Alternative algorithms can perform a bit reversal permutation in linear time while using only simple index calculations.[9]

Another consideration that is even more important for the performance of these algorithms is the effect of the memory hierarchy on running time. Because of this effect, more sophisticated algorithms that consider the block structure of memory can be faster than this naive scan.[7][8] An alternative to these techniques is special computer hardware that allows memory to be accessed both in normal and in bit-reversed order.[10]

gollark: Lyricly can't win this round because I will.
gollark: hd!histohist <@677461592178163712>
gollark: *Technically* I can manually mess with the database to cancel reminders, but no.
gollark: Besides, this is funnier.
gollark: No.

References
  1. Yang, Qingxuan; Ellis, John; Mamakani, Khalegh; Ruskey, Frank (2013), "In-place permuting and perfect shuffling using involutions", Information Processing Letters, 113 (10–11): 386–391, doi:10.1016/j.ipl.2013.02.017, MR 3037467.
  2. Herman, Gabor T. (2009). Fundamentals of Computerized Tomography (2nd ed.). London: Springer. p. 209. ISBN 978-1-85233-617-2.
  3. Gordon, Dan (June 2017). "A derandomization approach to recovering bandlimited signals across a wide range of random sampling rates". Numerical Algorithms. 77 (4): 1141–1157. doi:10.1007/s11075-017-0356-3.
  4. B. Gold and C. M. Rader, Digital Processing of Signals (New York: McGraw–Hill, 1969).
  5. Frederickson, Greg N.; Lynch, Nancy A. (1984), "The impact of synchronous communication on the problem of electing a leader in a ring" (PDF), Proceedings of the Sixteenth Annual ACM Symposium on Theory of Computing (STOC '84), pp. 493–503, doi:10.1145/800057.808719, ISBN 978-0897911337.
  6. Murphy, Scott (2020), "Common Rhythm as Discrete Derivative of Its Common-Time Meter" (PDF), MusMat: Brazilian Journal of Music and Mathematics, 4, pp. 1–11.
  7. Karp, Alan H. (1996), "Bit reversal on uniprocessors", SIAM Review, 38 (1): 1–26, CiteSeerX 10.1.1.24.2913, doi:10.1137/1038001, MR 1379039. Karp surveys and compares 30 different algorithms for bit reversal, developed between 1965 and the 1996 publication of his survey.
  8. Carter, Larry; Gatlin, Kang Su (1998), "Towards an optimal bit-reversal permutation program", Proceedings of the 39th Annual Symposium on Foundations of Computer Science (FOCS), pp. 544–553, CiteSeerX 10.1.1.46.9319, doi:10.1109/SFCS.1998.743505, ISBN 978-0-8186-9172-0.
  9. Jeong, Jechang; Williams, W.J. (1990), "A fast recursive bit-reversal algorithm", International Conference on Acoustics, Speech, and Signal Processing (ICASSP-90), 3, pp. 1511–1514, doi:10.1109/ICASSP.1990.115695.
  10. Harley, T. R.; Maheshwaramurthy, G. P. (2004), "Address generators for mapping arrays in bit-reversed order", IEEE Transactions on Signal Processing, 52 (6): 1693–1703, doi:10.1109/TSP.2004.827148.
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