Montgomery Reduction

Montgomery reduction provides an efficient way to perform modular arithmetic, particularly modular multiplication $a\cdot b \mod n$. This is crucial when the modulus $n$ is large and many operations are needed, as in cryptography. Montgomery reduction excels when $n$ is odd and aims to replace slow divisions by n.

The Core Idea

The method shifts calculations into a special "Montgomery domain" defined by an auxiliary modulus $R$. Arithmetic happens within this domain, where the reduction step is cleverly transformed into a division by $R$. Since $R$ is chosen as a power of 2, this division is just a fast bit shift and the remainder is equivalent to its last few bits. Results are converted back to the standard representation when the sequence of operations is complete.

The Math Explained

The Montgomery Representation

We choose an auxiliary modulus $R=b^k$ (where $b$ is the base, usually 2) such that $R>n$. Critically, $n$ and $R$ must be coprime: $\gcd(n,R)=1$. In this case, this just implies $n$ must be odd.

Definition 1. The Montgomery representation $\bar{x}$ of an integer $x$ (where $0\leq x<n$) is $\bar{x} = x \cdot R \mod n$.

Since $n$ and $R$ are coprime, we can define the following conversion functions:

• $\mathsf{fromMont}(\bar{x})$: Converts $\bar{x}$ back to standard form: $x=\bar{x}⋅R^{−1} \mod n$. This operation is also called Montgomery Reduction.

• $\mathsf{toMont}(x)$: Converts $x$ into Montgomery form: $\bar{x}=x⋅b^k \mod n$. This calculation itself can be optimized with Montgomery Reduction.

Lemma 1. Remainder of the division by $R=2^k$ can be efficiently calculated as $x \mod R =\mathsf{lowbits_k}(x)$ where $\mathsf{lowbits}_k(x)$ takes the least significant $k$ bits of $x$.

Lemma 2. Quotient $\lfloor x / R\rfloor$ can be efficiently computed using bit shift: $x \gg k$.

Montgomery Multiplication

Given two numbers in Montgomery form, $\bar{a}=a\cdot R \mod n$ and $\bar{b}=b⋅R \mod n$. We would like to calculate $\bar{c}\equiv (a\cdot b)\cdot R \mod n$. This can be calculated using $\bar{a}\cdot \bar{b} \cdot R^{-1}$ since:

$$\bar{a}\cdot\bar{b}\equiv (a\cdot R)\cdot(b\cdot R)\equiv a\cdot b \cdot R^2\equiv \bar{c}\cdot R\mod n$$

To do the Montgomery Multiplication efficiently, we need to be able to calculate:

$$\bar{c}=\mathsf{doMontMul}(\bar{a},\bar{b})=\bar{a}\cdot\bar{b}\cdot R^{-1} \mod n$$

To get the actual $c=a\cdot b \mod n$, we will have to additionally convert back from montgomery form using $\mathsf{fromMont}(\bar{c})$.

Montgomery Reduction

Definition 2. Montgomery Reduction is an efficient reduction method to calculate $T\cdot R^{-1}\mod n$ for a given $T < n\cdot R$. This operation can also be denoted as:

$$\mathsf{REDC}(T):=T\cdot R^{-1} \mod n$$

The key idea to perform Montgomery Reduction efficiently is to find an integer $m$ such that $T + m\cdot n$ is exactly divisible by $R$. Then the desired result can be computed as:

$$T\cdot R^{-1}\equiv\frac{T+m⋅n}{R}\equiv(T+m\cdot n)\gg k \mod n$$

What will happen if we precompute $R^{-1}$ to compute this?

We need to find $m$ such that $T+m⋅n≡0 \mod R$

$$\begin{align*} T+m\cdot n&\equiv 0 &\mod R\\ m\cdot n&\equiv -T &\mod R\\ m &\equiv -T\cdot n^{-1} &\mod R\\ m &\equiv T\cdot(-n^{-1}) &\mod R \end{align*}$$

To calculate $m$ efficiently, we use a precomputed value $n'=−n^{−1} \mod R$. This $n'$ exists because $\mathsf{gcd}(n,R)=1$. Now, we have $m\coloneqq T\cdot n'\mod R$ and $\mathsf{REDC}$ can be defined as:

$$\mathsf{REDC}(T)=(T+m⋅n)\gg k \mod n$$

And we have:

• $T<n\cdot R$ so $(T\gg k)=T/R< n$
• $m < R$ so $(m\cdot n \gg k) = n\cdot m / R< n$

Hence, $((T+m\cdot n)\gg k) < 2n$ so we just need to subtract $n$ once if $((T+m\cdot n)\gg k)\geq n$ to take the modulus.

Overview of REDC(T):

Given $T < n\cdot R$, and precomputed $n' \coloneqq −n^{−1} \mod R$.

1. Calculate $m=(T⋅n') \mod R=\mathsf{lowbits}_k(\mathsf{lowbits}_k(T)\cdot n')$. (Effectively: multiply the lowest $k$ bits of $T$ by $n'$, take the lowest $k$ bits of the product).
2. Compute $x=(T+m⋅n)/R$. (Involves one multiplication $m\cdot n$, one addition, and one right shift by $k$).
3. Return $x−n$ if $x\geq n$, otherwise return $x$.

Summary of Montgomery Reduction

With some precomputation and using Montgomery Reduction $\mathsf{REDC}$, we can optimize the $\mathsf{doMontMul}(\bar{a}, \bar{b})$, $\mathsf{toMont}(x)$, and $\mathsf{fromMont}(\bar{x})$ operations as following:

Precompute $Rsq' := R^2 \mod n$ and $n':=-(n^{-1})\mod R$:

$$\begin{aligned} \mathsf{toMont}(x)&=x\cdot R \mod n = \mathsf{REDC}(x\cdot R^2) = \mathsf{REDC}(x\cdot Rsq')\\ \mathsf{fromMont}(\bar{x}) &= \mathsf{REDC}(\bar{x}) \\ \mathsf{doMontMul}(\bar{a},\bar{b})&=\mathsf{REDC}(\bar{a}\cdot\bar{b}) \end{aligned}$$

Multi-precision Montgomery Reduction

Separated Operands Scanning (SOS)

Iterative Partial Reduction

In a multi-precision setting, suppose the modulo $n$ has $l$ limbs. Let $R=B^l$, where $w$ is limb bit width and $B=2^w$. To calculate $T\cdot R^{-1}=T\cdot B^{-l}\mod n$, the multi-precision Montgomery Reduction operates iteratively on:

$$T = T_{2l-1}\cdot B^{2l-1} + \dots + T_l\cdot B^l + T_{l-1}\cdot B^{l-1} + \dots + T_0$$

where $T_i$ represents limb values. At each iteration, the limbs of $T$ are reduced one by one which is done by partial reduction. The first partial reduction looks like so:

$$\begin{align*} T^{(1)}=T\cdot B^{-1} \mod n &= T_{2l-1}\cdot B^{2l-2} + \dots + T_l\cdot B^{l-1} + T_{l-1}\cdot B^{l-2} + \dots + T_1 + (T_0\cdot B^{-1} \mod n) &\mod n\\ &= T^{(1)}_{2l-1}B^{2l-2} + \dots + T^{(1)}_l\cdot B^{l-1} + T^{(1)}_{l-1}\cdot B^{l-2} + \dots + T^{(1)}_1 &\mod n \end{align*}$$

And after the next partial reduction, we will have:

$$\begin{align*} T^{(2)}=T\cdot B^{-2} \mod n &= T^{(1)}_{2l-1}\cdot B^{2l-3} + \dots + T^{(1)}_l\cdot B^{l-2} + T^{(1)}_{l-1}\cdot B^{l-3} + \dots + T^{(1)}_2+(T^{(1)}_1\cdot B^{-1} \mod n) &\mod n\\ &=T^{(2)}_{2l-1}\cdot B^{2l-3} + \dots + T^{(2)}_l\cdot B^{l-2} + T^{(2)}_{l-1}\cdot B^{l-3} + \dots + T^{(2)}_2 &\mod n\\ \end{align*}$$

and so on. After $l$ iterations, we have $T\cdot B^{-l}\mod n$ with $l$ limbs. The values of limbs change at each round because $T^{(i)}_i \cdot B^{-1} \mod n$ is also a multi-precision integer.

How to multiply by the inverse of $B$?

At round $i$, we need to calculate $(T^{(i-1)}_{i-1}\cdot B^{-1} \mod n)$. This can be done efficiently, similar to the single-precision case by adding some multiple of $n$ to make it divisible by $B$:

1. Precompute $n'\coloneqq -n^{-1}\mod B$.
2. Calculate $m_i\coloneqq T^{(i-1)}_{i-1}\cdot n' \mod B$. Then we have $T^{(i-1)}_{i-1} + m_i\cdot n \equiv T^{(i-1)}_{i-1} -T^{(i-1)}_{i-1}\cdot n^{-1}\cdot n \equiv 0\mod B$
3. Calculate $T^{(i-1)}_{i-1}\cdot B^{-1} \equiv (T^{(i-1)}_{i-1} + m_i\cdot n)\cdot B^{-1}\equiv (T^{(i-1)}_{i-1} + m_i\cdot n)\gg w\mod n$ .

Since $m_i < B$ and $T^{(i-1)}_{i-1} < B$, we have

$$(T^{(i-1)}_{i-1} +m_i\cdot n) / B \leq (B-1 + (B-1)\cdot n)/B=n + (B-1-n)/B < n$$

which means after step 3, we don't have to worry about subtracting $n$.

How many limbs are needed?

While trying to reduce, we add $m_i\cdot n$ every round which can cause potential overflow in the largest limb. Let's calculate how much we added in total. If we omit dividing by $B$, at each round, we can see that at each round $i$, we add $m_i \cdot n\cdot B^{i-1}$. This can be confusing but if you think of round $i$ as adding some value $m_i\cdot n$ to convert the $(i-1)$-th limb to 0, it can be more easier to see.

$$\text{Total added}=\sum_{i=1}^lm_i\cdot n \cdot B^{i-1}<n\sum_{i=1}^lB \cdot B^{i-1}=n\cdot B\bigg(\frac{B^l-1}{B-1}\bigg)<n\cdot R$$

This gives that total sum can be $T+n\cdot R <R^2+n\cdot R < 2R^2=2\cdot2^{2lw}=2^{2lw+1}$ which needs $2l+1$ limbs. For $T$, we already need $2l$ limbs and after the first step, the least significant limb will be 0's which can be discarded. In the first round, we have a maximum value of $T+n<n\cdot R + n=n(R+1)\leq(R-1)(R+1)=R^2-1$ so it fits within the $2l$ limbs. So, if we discard the least significant limb after first round, we don't actually need extra limb space to handle the overflowing.

Summary of the Iteration step

A complete round $i$ of partial reduction can be summarized by the following steps:

1. Compute $m_i=n' \cdot T^{(i-1)}_{i-1} \mod R$ (which costs $1\times 1$ limb multiplication).
2. Compute $m_i\cdot n$ (which costs $1 \times l$ limb multiplication).
3. Set $T^{(i)}=(T^{(i-1)} + m_i\cdot n ) \gg w$.

Now, let's calculate the upper bound on the final result of the reductions. First of all, we saw above that, the total added value has an upper bound of $n\cdot R$, and using $T<n\cdot R$, we have:

$$T^{(l)} < \frac{T+n\cdot R}{R}<\frac{2n\cdot R}{R}<2n$$

Therefore, the reduced value may require one additional subtraction to bring the value within the range $[0,\dots,n-1]$. Since we need $l+1$ limb multiplications for steps 1 and 2, over $l$ rounds, we will need a total of $l^2+l$ limb multiplications.

Analyzing and Comparing Montgomery Multiplication Algorithms

SOS with Ingonyama Optimization

We have seen that we need to do $l^2+l$ multiplications per round for multi-precision Montgomery Reduction. However, Ingonyama showed that we can reduce it to $l^2+1$. Let's see what happens if we just precompute $B'=B^{-1} \mod n$ and multiply the free coefficients with $B'$. Then, the partial reduction round $i$ becomes:

1. Compute $m'_i=T^{(i-1)}_{i-1}\cdot B'$ which is $1\times l$ limb multiplication, resulting in a $l+1$ limb integer.
2. Set $T^{(i)}=(T^{(i-1)}\gg w) + m'_i$.

Then, similar to the previous calculation, the upper bound on the total addition will become:

$$\text{Total added}=\sum_{i=1}^l m'_i \cdot B^{i-1}<\sum_{i=1}^l n\cdot B \cdot B^{i-1}=n\cdot B\bigg(\frac{B^l-1}{B-1}\bigg)<n\cdot R$$

So it hasn't changed. Which means the upper bound on the value after $l$ reductions will also be the same:

$$T^{(l)} < \frac{T+n\cdot R}{R}<\frac{2n\cdot R}{R}<2n$$

This gives us the total cost of $l\cdot l =l^2$ limb multiplications. Ingonyama's article mentions that this reduction cannot be applied in the last step because $T^{(l)}=(T^{(l-1)}\gg w) + m'_i$ will result in a $l+1$ limb integer while in the traditional case, we have $l$ limbs. Indeed it is true since we add it after the bit shift unlike the original version but if the result is less than $2\cdot n$, it shouldn't matter.

Coarsely Integrated Operands Scanning (CIOS)

In the SOS method above, we iteratively divided $t$ by $B$ $l$ times where $B$ denotes the limb base ($2^w$) and $l$ denotes the number of the limbs. $l$ iterations gives the desired result $t \cdot R^{-1} \mod n$ since $R = B^l$. Now we can discard the lower $l$ limbs, which is technically equivalent to dividing by $R$.

Naively speaking, SOS algorithm follows this sequence:

1. Multiply two large integers in Montgomery form using a textbook method.
2. Perform Montgomery reduction to produce $\bar{c} = \bar{a} \cdot \bar{b}$ from $\bar{a} \cdot \bar{b} \cdot R$.

which results in its namesake "separated" in SOS.

CIOS, on the other hand, is different from SOS in that it interleaves the multiplication and reduction steps, limb by limb.

Pseudocode

Now we alternate between iterations of the outer loops for multiplication and reduction.

for i = 0 to l-1
    C := 0
    for j = 0 to l-1                         // a * b[i]
        (C, t[j]) := t[j] + a[j]*b[i] + C  
    (t[l+1], t[l]) := t[l] + C.              
    C := 0
    m := t[0]*n'[0] mod W                    // Partial m for t[0] only
    (C, _) := t[0] + m*n[0]
    for j = 1 to s-1                         // Add(t, m*n) >> w 
        (C, t[j-1]) := t[j] + m*n[j] + C
    (C, t[l-1]) := t[l] + C                   
    t[l] := t[l+1] + C                       

Interleaving multiplication and reduction is valid since the value of $m$ in the $i$-th iteration of the outer loop for reduction depends only on the value $t[i]$, which is completely computed by the $i$-th iteration of the outer loop for the multiplication.

Cost analysis

	CIOS	SOS	SOS + Ingonyama
Addition	$4l^2 + 4l + 2$	$4l^2 + 4l + 2$	$4l^2 + 4l + 2$
Multiplication	$2l^2 +l$	$2l^2 +l$	$2l^2 +1$
Memory space	$l+3$	$2l+ 2$	$2l+ 2$

SOS with Ingonyama's optimization saves $l-1$ multiplications ($2l^2 + 1$ vs. $2l^2 + l$) whereas CIOS saves $l-1$ memory space compared to SOS ($2l+2$ vs. $l+3$) owing to not storing the total $t$. CIOS can be further optimized using EdMSM's trick.

References

• https://en.wikipedia.org/wiki/Montgomery_modular_multiplication
• https://hackmd.io/@Ingonyama/Barret-Montgomery#Barrett-Montgomery-duality
• Analyzing and Comparing Montgomery Multiplication Algorithms
• EdMSM: Multi-Scalar-Multiplication for SNARKs and Faster Montgomery multiplication

Written by BATZORIG ZORIGOO and Carson Lee from A41