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chore: Move to the Ingonyama MSM #374
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Original file line number | Diff line number | Diff line change |
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@@ -8,7 +8,7 @@ use digest::{ExtendableOutput, Update}; | |
use ff::{FromUniformBytes, PrimeField}; | ||
use group::{cofactor::CofactorCurveAffine, Curve, Group as AnotherGroup}; | ||
#[cfg(any(target_arch = "x86_64", target_arch = "aarch64"))] | ||
use grumpkin_msm::{bn256 as bn256_msm, grumpkin as grumpkin_msm}; | ||
use grumpkin_msm::grumpkin as grumpkin_msm; | ||
// Remove this when https://github.com/zcash/pasta_curves/issues/41 resolves | ||
use halo2curves::{bn256::G2Affine, CurveAffine, CurveExt}; | ||
use num_bigint::BigInt; | ||
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@@ -35,6 +35,19 @@ pub mod grumpkin { | |
}; | ||
} | ||
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#[cfg(any(target_arch = "x86_64", target_arch = "aarch64"))] | ||
fn bn256_msm(points: &[bn256::Affine], scalars: &[bn256::Scalar]) -> bn256::Point { | ||
cfg_if::cfg_if! { | ||
if #[cfg(feature = "cuda")] { | ||
let stream = ingonyama_grumpkin_msm::Config::new(); | ||
let cfg = ingonyama_grumpkin_msm::default_config(&stream.stream); | ||
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There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Could we make this a There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. I tried this, but ran into issues like
Dmytro mentioned that the first stream creation will have some delay, but each time after that, the Icicle/CUDA runtime will be able to reuse them with little overhead.
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ingonyama_grumpkin_msm::bn256_msm(&points, &scalars, &cfg) | ||
} else { | ||
grumpkin_msm::bn256(points, scalars) | ||
} | ||
} | ||
} | ||
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#[cfg(any(target_arch = "x86_64", target_arch = "aarch64"))] | ||
impl_traits!( | ||
bn256, | ||
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Why is it that the Grumpkin msm should still be on sppark? Doesn't ingonyama support grumpkin as well?