Boost the verifier

CHANGELOG.md

@@ -12,6 +12,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Modify the prover to match the paper [#831]
 - Modify the verifier to match the paper [#831]
 - Rename some variables to match the paper [#831]
+- Modify the parallelization to have a faster verifier [#834]
 
 ### Removed
 
@@ -592,6 +593,7 @@ is necessary since `rkyv/validation` was required as a bound.
 - Proof system module.
 
 <!-- ISSUES -->
+[#834]: https://github.com/dusk-network/plonk/issues/834
 [#831]: https://github.com/dusk-network/plonk/issues/831
 [#819]: https://github.com/dusk-network/plonk/issues/819
 [#818]: https://github.com/dusk-network/plonk/issues/818

README.md

@@ -34,7 +34,7 @@ as the documentation regarding the data structures that it exports. To check thi
 Benchmarks taken on `Apple M1`, for a circuit-size of `2^16` constraints:
 
 - Proving time: `7.871s`
-- Verification time: `3.732ms` **(This time does not vary depending on the circuit-size.)**
+- Verification time: `2.821ms` **(This time does not vary depending on the circuit-size.)**
 
 For more results, please run `cargo bench` to get a full report of benchmarks in respect of constraint numbers.
 

src/proof_system/proof.rs

@@ -358,91 +358,100 @@ pub(crate) mod alloc {
             v_coeffs_E.push(v_coeffs_E[V_MAX_DEGREE] * v_w_challenge);
             v_coeffs_E.push(v_coeffs_E[V_MAX_DEGREE + 1] * v_w_challenge);
 
-            // Coefficients to compute [F]_1
-            let mut v_coeffs_F = v_coeffs_E[..V_MAX_DEGREE].to_vec();
+            // Evaluations to compute [E]_1
+            let E_evals = vec![
+                self.evaluations.a_eval,
+                self.evaluations.b_eval,
+                self.evaluations.c_eval,
+                self.evaluations.d_eval,
+                self.evaluations.s_sigma_1_eval,
+                self.evaluations.s_sigma_2_eval,
+                self.evaluations.s_sigma_3_eval,
+                self.evaluations.a_w_eval,
+                self.evaluations.b_w_eval,
+                self.evaluations.d_w_eval,
+            ];
 
-            // As we include the shifted coefficients when computing [F]_1,
-            // we group them to save scalar multiplications when multiplying
-            // by [a]_1, [b]_1, and [d]_1
-            v_coeffs_F[0] += v_coeffs_E[V_MAX_DEGREE];
-            v_coeffs_F[1] += v_coeffs_E[V_MAX_DEGREE + 1];
-            v_coeffs_F[3] += v_coeffs_E[V_MAX_DEGREE + 2];
+            // Compute E = (-r_0 + (v)a + (v^2)b + (v^3)c + (v^4)d +
+            // + (v^5)s_sigma_1 + (v^6)s_sigma_2 + (v^7)s_sigma_3 +
+            // + (u)z_w + (u * v_w)a_w + (u * v_w^2)b_w + (u * v_w^3)d_w)
+            let mut E_scalar: BlsScalar = E_evals
+                .iter()
+                .zip(v_coeffs_E.iter())
+                .map(|(eval, coeff)| eval * coeff)
+                .sum();
+            E_scalar += -r_0_eval + (u_challenge * self.evaluations.z_eval);
 
-            // Commitments to compute [F]_1
-            let F_comms = vec![
+            // We group all the remaining scalar multiplications in the
+            // verification process, with the purpose of
+            // parallelizing them
+            let scalarmuls_points = vec![
                 self.a_comm.0,
                 self.b_comm.0,
                 self.c_comm.0,
                 self.d_comm.0,
                 verifier_key.permutation.s_sigma_1.0,
                 verifier_key.permutation.s_sigma_2.0,
                 verifier_key.permutation.s_sigma_3.0,
+                opening_key.g,
+                self.w_z_chall_w_comm.0,
+                self.w_z_chall_comm.0,
+                self.w_z_chall_w_comm.0,
             ];
 
-            // Compute '[F]_1' in single-core
+            let mut scalarmuls_scalars = v_coeffs_E[..V_MAX_DEGREE].to_vec();
+
+            // As we include the shifted coefficients when computing [F]_1,
+            // we group them to save scalar multiplications when multiplying
+            // by [a]_1, [b]_1, and [d]_1
+            scalarmuls_scalars[0] += v_coeffs_E[V_MAX_DEGREE];
+            scalarmuls_scalars[1] += v_coeffs_E[V_MAX_DEGREE + 1];
+            scalarmuls_scalars[3] += v_coeffs_E[V_MAX_DEGREE + 2];
+
+            scalarmuls_scalars.push(E_scalar);
+            scalarmuls_scalars.push(u_challenge);
+            scalarmuls_scalars.push(z_challenge);
+            scalarmuls_scalars
+                .push(u_challenge * z_challenge * domain.group_gen);
+
+            // Compute the scalar multiplications in single-core
             #[cfg(not(feature = "std"))]
-            let mut F: G1Projective = F_comms
+            let scalarmuls: Vec<G1Projective> = scalarmuls_points
                 .iter()
-                .zip(v_coeffs_F.iter())
-                .map(|(poly, coeff)| poly * coeff)
-                .sum();
+                .zip(scalarmuls_scalars.iter())
+                .map(|(point, scalar)| point * scalar)
+                .collect();
 
-            // Compute '[F]_1' in multi-core
+            // Compute the scalar multiplications in multi-core
             #[cfg(feature = "std")]
-            let mut F: G1Projective = F_comms
+            let scalarmuls: Vec<G1Projective> = scalarmuls_points
                 .par_iter()
-                .zip(v_coeffs_F.par_iter())
-                .map(|(poly, coeff)| poly * coeff)
-                .sum();
+                .zip(scalarmuls_scalars.par_iter())
+                .map(|(point, scalar)| point * scalar)
+                .collect();
 
             // [F]_1 = [D]_1 + (v)[a]_1 + (v^2)[b]_1 + (v^3)[c]_1 + (v^4)[d]_1 +
             // + (v^5)[s_sigma_1]_1 + (v^6)[s_sigma_2]_1 + (v^7)[s_sigma_3]_1 +
             // + (u * v_w)[a]_1 + (u * v_w^2)[b]_1 + (u * v_w^3)[d]_1
+            let mut F: G1Projective = scalarmuls[..V_MAX_DEGREE].iter().sum();
             F += D;
 
-            // Evaluations to compute [E]_1
-            let E_evals = vec![
-                self.evaluations.a_eval,
-                self.evaluations.b_eval,
-                self.evaluations.c_eval,
-                self.evaluations.d_eval,
-                self.evaluations.s_sigma_1_eval,
-                self.evaluations.s_sigma_2_eval,
-                self.evaluations.s_sigma_3_eval,
-                self.evaluations.a_w_eval,
-                self.evaluations.b_w_eval,
-                self.evaluations.d_w_eval,
-            ];
-
-            // Compute '[E]_1' = (-r_0 + (v)a + (v^2)b + (v^3)c + (v^4)d +
-            // + (v^5)s_sigma_1 + (v^6)s_sigma_2 + (v^7)s_sigma_3 +
-            // + (u)z_w + (u * v_w)a_w + (u * v_w^2)b_w + (u * v_w^3)d_w)
-            let mut E: BlsScalar = E_evals
-                .iter()
-                .zip(v_coeffs_E.iter())
-                .map(|(eval, coeff)| eval * coeff)
-                .sum();
-            E += -r_0_eval + (u_challenge * self.evaluations.z_eval);
-
-            let E = E * opening_key.g;
+            // [E]_1 = E * G
+            let E = scalarmuls[V_MAX_DEGREE];
 
             // Compute the G_1 element of the first pairing:
             // [W_z]_1 + u * [W_zw]_1
             //
             // Note that we negate this value to be able to subtract
             // the pairings later on, using the multi Miller loop
             let left = G1Affine::from(
-                -(self.w_z_chall_comm.0
-                    + u_challenge * self.w_z_chall_w_comm.0),
+                -(self.w_z_chall_comm.0 + scalarmuls[V_MAX_DEGREE + 1]),
             );
 
             // Compute the G_1 element of the second pairing:
             // z * [W_z]_1 + (u * z * w) * [W_zw]_1 + [F]_1 - [E]_1
             let right = G1Affine::from(
-                z_challenge * self.w_z_chall_comm.0
-                    + (u_challenge * z_challenge * domain.group_gen)
-                        * self.w_z_chall_w_comm.0
-                    + F
+                scalarmuls[V_MAX_DEGREE + 2] + scalarmuls[V_MAX_DEGREE + 3] + F
                     - E,
             );