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
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
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
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
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
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
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:
![{\displaystyle a-\left[{\frac {\left[{\frac {a}{R_{0}}}\right]_{2}\,\left[{\frac {R}{n}}\right]_{0}}{R_{1}}}\right]_{1}\,n={\frac {a\left(R\,{\text{mod}}^{\left[\,\right]_{0}}\,n\right)+\left(a\,{\text{mod}}^{\left[\,\right]_{2}}\,R_{0}\right)\left(R-R\,{\text{mod}}^{\left[\,\right]_{0}}\,n\right)+\left(\left[{\frac {a}{R_{0}}}\right]_{2}\,\left[{\frac {R}{n}}\right]_{0}\,{\text{mod}}^{\left[\,\right]_{1}}\,R_{1}\right)R_{0}n}{R}}}](./ffbbadfb25ba00075bc11ad5115be6e44609cac2.svg)
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
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
.
![{\displaystyle {\begin{aligned}a-\left[{\frac {a}{R}}\right]_{1}\,n&=a-{\frac {a-(a\,{\text{mod}}^{\left[\,\right]_{1}}\,R)}{R}}n\\&={\frac {aR-an+(a\,{\text{mod}}^{\left[\,\right]_{1}}\,R)n}{R}}\\&={\frac {ac+(a\,{\text{mod}}^{\left[\,\right]_{1}}\,R)n}{R}}\\&=n\left({\frac {a\,{\text{mod}}^{\left[\,\right]_{1}}\,R}{R}}+{\frac {ac}{Rn}}\right)\\&=n\left({\frac {a\,{\text{mod}}^{\left[\,\right]_{1}}\,R}{R}}+{\frac {a}{{\frac {R^{2}}{c}}-R}}\right)\end{aligned}}}](./26beda136d607800e0c164d369d759b4cc6dac4f.svg)
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
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:
- Compute the approximate quotient
.
- Compute the Barrett remainder
.
- Compute the quotient error
where
. This is done by subtracting a multiple of
to
until
is obtained.
- 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
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
It is also possible to use Barrett algorithm for polynomial division, by reversing polynomials and using X-adic arithmetic.[6]
See also
References
- ^ The remainder of integer division of
by
.
- ^ 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.
- ^ 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
- ^
Shoup, Victor. "Number Theory Library".
- ^ Menezes, Alfred; Oorschot, Paul; Vanstone, Scott (1997). Handbook of Applied Cryptography (5th ed.). CRC Press. doi:10.1201/9780429466335. ISBN 0-8493-8523-7.
- ^ "Barrett reduction for polynomials". www.corsix.org. Retrieved 2022-09-07.
Sources