cli/vendor/curve25519-dalek/docs/parallel-formulas.md

Vectorized implementations of field and point operations, using a
modification of the 4-way parallel formulas of Hisil, Wong, Carter,
and Dawson.

These notes explain the parallel formulas and our strategy for using
them with SIMD operations.  There are two backend implementations: one
using AVX2, and the other using AVX512-IFMA.

# Overview

The 2008 paper [_Twisted Edwards Curves Revisited_][hwcd08] by Hisil,
Wong, Carter, and Dawson (HWCD) introduced the “extended coordinates”
and mixed-model representations which are used by most Edwards curve
implementations.

However, they also describe 4-way parallel formulas for point addition
and doubling: a unified addition algorithm taking an effective
\\(2\mathbf M + 1\mathbf D\\), a doubling algorithm taking an
effective \\(1\mathbf M + 1\mathbf S\\), and a dedicated (i.e., for
distinct points) addition algorithm taking an effective \\(2 \mathbf M
\\).  They compare these formulas with a 2-way parallel variant of the
Montgomery ladder.

Unlike their serial formulas, which are used widely, their parallel
formulas do not seem to have been implemented in software before.  The
2-way parallel Montgomery ladder was used in 2015 by Tung Chou's
`sandy2x` implementation.  Curiously, however, although the [`sandy2x`
paper][sandy2x] also implements Edwards arithmetic, and cites HWCD08,
it doesn't mention their parallel Edwards formulas.
A 2015 paper by Hernández and López describes an AVX2 implementation
of X25519. Neither the paper nor the code are publicly available, but
it apparently gives only a [slight speedup][avx2trac], suggesting that
it uses a 4-way parallel Montgomery ladder rather than parallel
Edwards formulas.

The reason may be that HWCD08 describe their formulas as operating on
four independent processors, which would make a software
implementation impractical: all of the operations are too low-latency
to effectively synchronize.  But a closer inspection reveals that the
(more expensive) multiplication and squaring steps are uniform, while
the instruction divergence occurs in the (much cheaper) addition and
subtraction steps.  This means that a SIMD implementation can perform
the expensive steps uniformly, and handle divergence in the
inexpensive steps using masking.

These notes describe modifications to the original parallel formulas
to allow a SIMD implementation, and this module contains
implementations of the modified formulas targeting either AVX2 or
AVX512-IFMA.

# Parallel formulas in HWCD'08

The doubling formula is presented in the HWCD paper as follows:

| Cost             | Processor 1                    | Processor 2                    | Processor 3                    | Processor 4                    |
|------------------|--------------------------------|--------------------------------|--------------------------------|--------------------------------|
|                  | idle                           | idle                           | idle                           | \\( R\_1 \gets X\_1 + Y\_1 \\) |
| \\(1\mathbf S\\) | \\( R\_2 \gets X\_1\^2 \\)     | \\( R\_3 \gets Y\_1\^2   \\)   | \\( R\_4 \gets Z\_1\^2   \\)   | \\( R\_5 \gets R\_1\^2   \\)   |
|                  | \\( R\_6 \gets R\_2 + R\_3 \\) | \\( R\_7 \gets R\_2 - R\_3 \\) | \\( R\_4 \gets 2 R\_4 \\)      | idle                           |
|                  | idle                           | \\( R\_1 \gets R\_4 + R\_7 \\) | idle                           | \\( R\_2 \gets R\_6 - R\_5 \\) |
| \\(1\mathbf M\\) | \\( X\_3 \gets R\_1 R\_2 \\)   | \\( Y\_3 \gets R\_6 R\_7 \\)   | \\( T\_3 \gets R\_2 R\_6 \\)   | \\( Z\_3 \gets R\_1 R\_7 \\)   |

and the unified addition algorithm is presented as follows:

| Cost             | Processor 1                    | Processor 2                    | Processor 3                    | Processor 4                    |
|------------------|--------------------------------|--------------------------------|--------------------------------|--------------------------------|
|                  | \\( R\_1 \gets Y\_1 - X\_1 \\) | \\( R\_2 \gets Y\_2 - X\_2 \\) | \\( R\_3 \gets Y\_1 + X\_1 \\) | \\( R\_4 \gets Y\_2 + X\_2 \\) |
| \\(1\mathbf M\\) | \\( R\_5 \gets R\_1 R\_2 \\)   | \\( R\_6 \gets R\_3 R\_4 \\)   | \\( R\_7 \gets T\_1 T\_2 \\)   | \\( R\_8 \gets Z\_1 Z\_2 \\)   |
| \\(1\mathbf D\\) | idle                           | idle                           | \\( R\_7 \gets k R\_7 \\)      | \\( R\_8 \gets 2 R\_8 \\)      |
|                  | \\( R\_1 \gets R\_6 - R\_5 \\) | \\( R\_2 \gets R\_8 - R\_7 \\) | \\( R\_3 \gets R\_8 + R\_7 \\) | \\( R\_4 \gets R\_6 + R\_5 \\) |
| \\(1\mathbf M\\) | \\( X\_3 \gets R\_1 R\_2 \\)   | \\( Y\_3 \gets R\_3 R\_4 \\)   | \\( T\_3 \gets R\_1 R\_4 \\)   | \\( Z\_3 \gets R\_2 R\_3 \\)   |

Here \\(\mathbf M\\) and \\(\mathbf S\\) represent the cost of
multiplication and squaring of generic field elements, \\(\mathbf D\\)
represents the cost of multiplication by a curve constant (in this
case \\( k = 2d \\)).

Notice that the \\(1\mathbf M\\) and \\(1\mathbf S\\) steps are
uniform.  The non-uniform steps are all inexpensive additions or
subtractions, with the exception of the multiplication by the curve
constant \\(k = 2d\\):
$$
R\_7 \gets 2 d R\_7.
$$

HWCD suggest parallelising this step by breaking \\(k = 2d\\) into four
parts as \\(k = k_0 + 2\^n k_1 + 2\^{2n} k_2 + 2\^{3n} k_3 \\) and
computing \\(k_i R_7 \\) in parallel.  This is quite awkward, but if
the curve constant is a ratio \\( d = d\_1/d\_2 \\), then projective
coordinates allow us to instead compute 
$$
(R\_5, R\_6, R\_7, R\_8) \gets (d\_2 R\_5, d\_2 R\_6, 2d\_1 R\_7, d\_2 R\_8).
$$
This can be performed as a uniform multiplication by a vector of
constants, and if \\(d\_1, d\_2\\) are small, it is relatively
inexpensive.  (This trick was suggested by Mike Hamburg).
In the Curve25519 case, we have 
$$
d = \frac{d\_1}{d\_2} = \frac{-121665}{121666};
$$
Since \\(2 \cdot 121666 < 2\^{18}\\), all the constants above fit (up
to sign) in 32 bits, so this can be done in parallel as four
multiplications by small constants \\( (121666, 121666, 2\cdot 121665,
2\cdot 121666) \\), followed by a negation to compute \\( - 2\cdot 121665\\).

# Modified parallel formulas

Using the modifications sketched above, we can write SIMD-friendly
versions of the parallel formulas as follows.  To avoid confusion with
the original formulas, temporary variables are named \\(S\\) instead
of \\(R\\) and are in static single-assignment form.

## Addition

To add points
\\(P_1 = (X_1 : Y_1 : Z_1 : T_1) \\)
and
\\(P_2 = (X_2 : Y_2 : Z_2 : T_2 ) \\),
we compute
$$
\begin{aligned}
(S\_0 &&,&& S\_1 &&,&& S\_2 &&,&& S\_3 )
&\gets
(Y\_1 - X\_1&&,&& Y\_1 + X\_1&&,&& Y\_2 - X\_2&&,&& Y\_2 + X\_2)
\\\\
(S\_4 &&,&& S\_5 &&,&& S\_6 &&,&& S\_7 )
&\gets
(S\_0 \cdot S\_2&&,&& S\_1 \cdot S\_3&&,&& Z\_1 \cdot Z\_2&&,&& T\_1 \cdot T\_2)
\\\\
(S\_8    &&,&& S\_9    &&,&& S\_{10} &&,&& S\_{11} )
&\gets
(d\_2 \cdot S\_4 &&,&& d\_2 \cdot S\_5 &&,&& 2 d\_2 \cdot S\_6 &&,&& 2 d\_1 \cdot S\_7 )
\\\\
(S\_{12} &&,&& S\_{13} &&,&& S\_{14} &&,&& S\_{15})
&\gets
(S\_9 - S\_8&&,&& S\_9 + S\_8&&,&& S\_{10} - S\_{11}&&,&& S\_{10} + S\_{11})
\\\\
(X\_3&&,&& Y\_3&&,&& Z\_3&&,&& T\_3)
&\gets
(S\_{12} \cdot S\_{14}&&,&& S\_{15} \cdot S\_{13}&&,&& S\_{15} \cdot S\_{14}&&,&& S\_{12} \cdot S\_{13})
\end{aligned}
$$
to obtain \\( P\_3 = (X\_3 : Y\_3 : Z\_3 : T\_3) = P\_1 + P\_2 \\).
This costs \\( 2\mathbf M + 1 \mathbf D\\).

## Readdition

If the point \\( P\_2 = (X\_2 : Y\_2 : Z\_2 : T\_2) \\) is fixed, we
can cache the multiplication of the curve constants by computing
$$
\begin{aligned}
(S\_2\' &&,&& S\_3\' &&,&& Z\_2\' &&,&& T\_2\' )
&\gets
(d\_2 \cdot (Y\_2 - X\_2)&&,&& d\_2 \cdot (Y\_1 + X\_1)&&,&& 2d\_2 \cdot Z\_2 &&,&& 2d\_1 \cdot T\_2).
\end{aligned}
$$
This costs \\( 1\mathbf D\\); with \\( (S\_2\', S\_3\', Z\_2\', T\_2\')\\)
in hand, the addition formulas above become
$$
\begin{aligned}
(S\_0 &&,&& S\_1 &&,&& Z\_1 &&,&& T\_1 )
&\gets
(Y\_1 - X\_1&&,&& Y\_1 + X\_1&&,&& Z\_1 &&,&& T\_1)
\\\\
(S\_8    &&,&& S\_9    &&,&& S\_{10} &&,&& S\_{11} )
&\gets
(S\_0 \cdot S\_2\' &&,&& S\_1 \cdot S\_3\'&&,&& Z\_1 \cdot Z\_2\' &&,&& T\_1 \cdot T\_2\')
\\\\
(S\_{12} &&,&& S\_{13} &&,&& S\_{14} &&,&& S\_{15})
&\gets
(S\_9 - S\_8&&,&& S\_9 + S\_8&&,&& S\_{10} - S\_{11}&&,&& S\_{10} + S\_{11})
\\\\
(X\_3&&,&& Y\_3&&,&& Z\_3&&,&& T\_3)
&\gets
(S\_{12} \cdot S\_{14}&&,&& S\_{15} \cdot S\_{13}&&,&& S\_{15} \cdot S\_{14}&&,&& S\_{12} \cdot S\_{13})
\end{aligned}
$$
which costs only \\( 2\mathbf M \\).  This precomputation is
essentially similar to the precomputation that HWCD suggest for their
serial formulas.  Because the cost of precomputation and then
readdition is the same as addition, it's sufficient to only
implement caching and readdition.

## Doubling

The non-uniform portions of the (re)addition formulas have a fairly
regular structure.  Unfortunately, this is not the case for the
doubling formulas, which are much less nice.

To double a point \\( P = (X\_1 : Y\_1 : Z\_1 : T\_1) \\), we compute
$$
\begin{aligned}
(X\_1 &&,&& Y\_1 &&,&& Z\_1 &&,&& S\_0)
&\gets
(X\_1 &&,&& Y\_1 &&,&& Z\_1 &&,&& X\_1 + Y\_1)
\\\\
(S\_1 &&,&& S\_2 &&,&& S\_3 &&,&& S\_4 )
&\gets
(X\_1\^2 &&,&& Y\_1\^2&&,&& Z\_1\^2 &&,&& S\_0\^2)
\\\\
(S\_5 &&,&& S\_6 &&,&& S\_8 &&,&& S\_9 )
&\gets
(S\_1 + S\_2 &&,&& S\_1 - S\_2 &&,&& S\_1 + 2S\_3 - S\_2 &&,&& S\_1 + S\_2 - S\_4)
\\\\
(X\_3 &&,&& Y\_3 &&,&& Z\_3 &&,&& T\_3 )
&\gets
(S\_8 \cdot S\_9 &&,&& S\_5 \cdot S\_6 &&,&& S\_8 \cdot S\_6 &&,&& S\_5 \cdot S\_9)
\end{aligned}
$$
to obtain \\( P\_3 = (X\_3 : Y\_3 : Z\_3 : T\_3) = \[2\]P\_1 \\).

The intermediate step between the squaring and multiplication requires
a long chain of additions.  For the IFMA-based implementation, this is not a problem; for the AVX2-based implementation, it is, but with some care and finesse, it's possible to arrange the computation without requiring an intermediate reduction.

# Implementation

These formulas aren't specific to a particular representation of field
element vectors, whose optimum choice is determined by the details of
the instruction set.  However, it's not possible to perfectly separate
the implementation of the field element vectors from the
implementation of the point operations.  Instead, the [`avx2`] and
[`ifma`] backends provide `ExtendedPoint` and `CachedPoint` types, and
the [`scalar_mul`] code uses one of the backend types by a type alias.

# Comparison to non-vectorized formulas

In theory, the parallel Edwards formulas seem to allow a \\(4\\)-way
speedup from parallelism.  However, an actual vectorized
implementation has several slowdowns that cut into this speedup.

First, the parallel formulas can only use the available vector
multiplier.  For AVX2, this is a \\( 32 \times 32 \rightarrow 64
\\)-bit integer multiplier, so the speedup from vectorization must
overcome the disadvantage of losing the \\( 64 \times 64 \rightarrow
128\\)-bit (serial) integer multiplier.  The effect of this slowdown
is microarchitecture-dependent, since it requires accounting for the
total number of multiplications and additions and their relative
costs.  IFMA allows using a \\( 52 \times 52 \rightarrow 104 \\)-bit
multiplier, but the high and low halves need to be computed
separately, and the reduction requires extra work because it's not
possible to pre-multiply by \\(19\\).

Second, the parallel doubling formulas incur both a theoretical and
practical slowdown.  The parallel formulas described above work on the
\\( \mathbb P\^3 \\) “extended” coordinates.  The \\( \mathbb P\^2 \\)
model introduced earlier by [Bernstein, Birkner, Joye, Lange, and
Peters][bbjlp08] allows slightly faster doublings, so HWCD suggest
mixing coordinate systems while performing scalar multiplication
(attributing the idea to [a 1998 paper][cmo98] by Cohen, Miyagi, and
Ono).  The \\( T \\) coordinate is not required for doublings, so when
doublings are followed by doublings, its computation can be skipped.
More details on this approach and the different coordinate systems can
be found in the [`curve_models` module documentation][curve_models].

Unfortunately, this optimization is not compatible with the parallel
formulas, which cannot save time by skipping a single variable, so the
parallel doubling formulas do slightly more work when counting the
total number of field multiplications and squarings.

In addition, the parallel doubling formulas have a less regular
pattern of additions and subtractions than the parallel addition
formulas, so the vectorization overhead is proportionately greater.
Both the parallel addition and parallel doubling formulas also require
some shuffling to rearrange data within the vectors, which places more
pressure on the shuffle unit than is desirable.

This means that the speedup from using a vectorized implementation of
parallel Edwards formulas is likely to be greatest in applications
that do fewer doublings and more additions (like a large multiscalar
multiplication) rather than applications that do fewer additions and
more doublings (like a double-base scalar multiplication).

Third, Amdahl's law says that the speedup is limited to the portion
which can be parallelized.  Normally, the field multiplications
dominate the cost of point operations, but with the IFMA backend, the
multiplications are so fast that the non-parallel additions end up as
a significant portion of the total time.

Fourth, current Intel CPUs perform thermal throttling when using wide
vector instructions.  A detailed description can be found in §15.26 of
[the Intel Optimization Manual][intel], but using wide vector
instructions prevents the core from operating at higher frequencies.
The core can return to the higher-frequency state after 2
milliseconds, but this timer is reset every time high-power
instructions are used.

Any speedup from vectorization therefore has to be weighed against a
slowdown for the next few million instructions.  For a mixed workload,
where point operations are interspersed with other tasks, this can
reduce overall performance.  This implementation is therefore probably
not suitable for basic applications, like signatures, but is
worthwhile for complex applications, like zero-knowledge proofs, which
do sustained work.

# Future work

There are several directions for future improvement:

* Using the vectorized field arithmetic code to parallelize across
  point operations rather than within a single point operation.  This
  is less flexible, but would give a speedup both from allowing use of
  the faster mixed-model arithmetic and from reducing shuffle
  pressure.  One approach in this direction would be to implement
  batched scalar-point operations using vectors of points (AoSoA
  layout).  This less generally useful but would give a speedup for
  Bulletproofs.

* Extending the IFMA implementation to use the full width of AVX512,
  either handling the extra parallelism internally to a single point
  operation (by using a 2-way parallel implementation of field
  arithmetic instead of a wordsliced one), or externally,
  parallelizing across point operations.  Internal parallelism would
  be preferable but might require too much shuffle pressure.  For now,
  the only available CPU which runs IFMA operations executes them at
  256-bits wide anyways, so this isn't yet important.

* Generalizing the implementation to NEON instructions.  The current
  point arithmetic code is written in terms of field element vectors,
  which are in turn implemented using platform SIMD vectors.  It
  should be possible to write an alternate implementation of the
  `FieldElement2625x4` using NEON without changing the point
  arithmetic.  NEON has 128-bit vectors rather than 256-bit vectors,
  but this may still be worthwhile compared to a serial
  implementation.


[sandy2x]: https://eprint.iacr.org/2015/943.pdf
[avx2trac]: https://trac.torproject.org/projects/tor/ticket/8897#comment:28
[hwcd08]: https://www.iacr.org/archive/asiacrypt2008/53500329/53500329.pdf
[curve_models]: https://docs.rs/curve25519-dalek/latest/curve25519-dalek/backend/serial/curve_models/index.html
[bbjlp08]: https://eprint.iacr.org/2008/013
[cmo98]: https://link.springer.com/content/pdf/10.1007%2F3-540-49649-1_6.pdf
[intel]: https://software.intel.com/sites/default/files/managed/9e/bc/64-ia-32-architectures-optimization-manual.pdf