In modular arithmetic, Barrett reduction is an algorithm designed to optimize the calculation of [1] without needing a fast division algorithm. It replaces divisions with multiplications, and can be used when is constant and . It was introduced in 1986 by P.D. Barrett.[2]

Historically, for values , one computed by applying Barrett reduction to the full product . In 2021, Becker et al. showed that the full product is unnecessary if we can perform precomputation on one of the operands.[3]

General idea

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We call a function   an integer approximation if  . For a modulus   and an integer approximation  , we define   as

 .

Common choices of   are floor, ceiling, and rounding functions.

Generally, Barrett multiplication starts by specifying two integer approximations   and computes a reasonably close approximation of   as

 ,

where   is a fixed constant, typically a power of 2, chosen so that multiplication and division by   can be performed efficiently.

The case   was introduced by P.D. Barrett [2] for the floor-function case  . The general case for   can be found in NTL.[4] The integer approximation view and the correspondence between Montgomery multiplication and Barrett multiplication was discovered by Hanno Becker, Vincent Hwang, Matthias J. Kannwischer, Bo-Yin Yang, and Shang-Yi Yang.[3]

Single-word Barrett reduction

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Barrett initially considered an integer version of the above algorithm when the values fit into machine words. We illustrate the idea for the floor-function case with   and  .

When calculating   for unsigned integers, the obvious analog would be to use division by  :

func reduce(a uint) uint {
    q := a / n  // Division implicitly returns the floor of the result.
    return a - q * n
}

However, division can be expensive and, in cryptographic settings, might not be a constant-time instruction on some CPUs, subjecting the operation to a timing attack. Thus Barrett reduction approximates   with a value   because division by   is just a right-shift, and so it is cheap.

In order to calculate the best value for   given   consider:

 

For   to be an integer, we need to round   somehow. Rounding to the nearest integer will give the best approximation but can result in   being larger than  , which can cause underflows. Thus   is used for unsigned arithmetic.

Thus we can approximate the function above with the following:

func reduce(a uint) uint {
    q := (a * m) >> k // ">> k" denotes bitshift by k.
    return a - q * n
}

However, since  , the value of q in that function can end up being one too small, and thus a is only guaranteed to be within   rather than   as is generally required. A conditional subtraction will correct this:

func reduce(a uint) uint {
    q := (a * m) >> k
    a := a - q * n
    if a >= n {
        a := a - n
    }
    return a
}

Single-word Barrett multiplication

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Suppose   is known. This allows us to precompute   before we receive  . Barrett multiplication computes  , approximates the high part of   with  , and subtracts the approximation. Since   is a multiple of  , the resulting value   is a representative of  .

Correspondence between Barrett and Montgomery multiplications

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Recall that unsigned Montgomery multiplication computes a representative of   as

 .

In fact, this value is equal to  .

We prove the claim as follows.

 

Generally, for integer approximations  , we have

 .[3]

Range of Barrett multiplication

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We bound the output with

 .

Similar bounds hold for other kinds of integer approximation functions. For example, if we choose  , the rounding half up function, then we have

 

It is common to select R such that   (or   in the    case) so that the output remains within   and   (  and   resp.), and therefore only one check is performed to obtain the final result between   and  . Furthermore, one can skip the check and perform it once at the end of an algorithm at the expense of larger inputs to the field arithmetic operations.

Barrett multiplication non-constant operands

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The Barrett multiplication previously described requires a constant operand b to pre-compute   ahead of time. Otherwise, the operation is not efficient. It is common to use Montgomery multiplication when both operands are non-constant as it has better performance. However, Montgomery multiplication requires a conversion to and from Montgomery domain which means it is expensive when a few modular multiplications are needed.

To perform Barrett multiplication with non-constant operands, one can set   as the product of the operands and set   to  . This leads to

 

A quick check on the bounds yield the following in   case

 

and the following in   case

 

Setting   will always yield one check on the output. However, a tighter constraint on   might be possible since   is a constant that is sometimes significantly smaller than  .

A small issue arises with performing the following product   since   is already a product of two operands. Assuming   fits in   bits, then   would fit in   bits and   would fit in   bits. Their product would require a   multiplication which might require fragmenting in systems that cannot perform the product in one operation.

An alternative approach is to perform the following Barrett reduction:

 

where  ,  ,  , and   is the bit-length of  .

Bound check in the case   yields the following

 

and for the case   yields the following

 

For any modulus and assuming  , the bound inside the parenthesis in both cases is less than or equal:

 

where   in the   case and   in the   case.

Setting   and   (or   in the   case) will always yield one check. In some cases, testing the bounds might yield a lower   and/or   values.

Small Barrett reduction

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It is possible to perform a Barrett reduction with one less multiplication as follows

  where   and   is the bit-length of  

Every modulus can be written in the form   for some integer  .

 

Therefore, reducing any   for   or any   for   yields one check.

From the analysis of the constraint, it can be observed that the bound of   is larger when   is smaller. In other words, the bound is larger when   is closer to  .

Barrett Division

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Barrett reduction can be used to compute floor, round or ceil division   without performing expensive long division. Furthermore it can be used to compute  . After pre-computing the constants, the steps are as follows:

  1. Compute the approximate quotient  .
  2. Compute the Barrett remainder  .
  3. Compute the quotient error   where  . This is done by subtracting a multiple of   to   until   is obtained.
  4. Compute the quotient  .

If the constraints for the Barrett reduction are chosen such that there is one check, then the absolute value of   in step 3 cannot be more than 1. Using   and appropriate constraints, the error   can be obtained from the sign of  .

Multi-word Barrett reduction

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Barrett's primary motivation for considering reduction was the implementation of RSA, where the values in question will almost certainly exceed the size of a machine word. In this situation, Barrett provided an algorithm that approximates the single-word version above but for multi-word values. For details see section 14.3.3 of the Handbook of Applied Cryptography.[5]

Barrett algorithm for polynomials

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It is also possible to use Barrett algorithm for polynomial division, by reversing polynomials and using X-adic arithmetic.[6]

See also

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References

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  1. ^ The remainder of integer division of   by  .
  2. ^ a b Barrett, P. (1986). "Implementing the Rivest Shamir and Adleman Public Key Encryption Algorithm on a Standard Digital Signal Processor". Advances in Cryptology – CRYPTO' 86. Lecture Notes in Computer Science. Vol. 263. pp. 311–323. doi:10.1007/3-540-47721-7_24. ISBN 978-3-540-18047-0.
  3. ^ a b c Becker, Hanno; Hwang, Vincent; Kannwischer, Matthias J.; Yang, Bo-Yin; Yang, Shang-Yi (2021), "Neon NTT: Faster Dilithium, Kyber, and Saber on Cortex-A72 and Apple M1", Transactions on Cryptographic Hardware and Embedded Systems, 2022 (1): 221–244, doi:10.46586/tches.v2022.i1.221-244
  4. ^ Shoup, Victor. "Number Theory Library".
  5. ^ Menezes, Alfred; Oorschot, Paul; Vanstone, Scott (1997). Handbook of Applied Cryptography (5th ed.). CRC Press. doi:10.1201/9780429466335. ISBN 0-8493-8523-7.
  6. ^ "Barrett reduction for polynomials". www.corsix.org. Retrieved 2022-09-07.

Sources

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