forked from M-Labs/nalgebra
Improve CsMatrix multiplaction performances.
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@ -7,7 +7,7 @@ use std::slice;
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use allocator::Allocator;
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use constraint::{AreMultipliable, DimEq, SameNumberOfRows, ShapeConstraint};
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use sparse::{CsMatrix, CsStorage, CsVector};
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use sparse::{CsMatrix, CsStorage, CsStorageMut, CsVector};
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use storage::{Storage, StorageMut};
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use {DefaultAllocator, Dim, Matrix, MatrixMN, Real, Scalar, Vector, VectorN, U1};
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@ -150,8 +150,7 @@ where
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);
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let mut res = CsMatrix::new_uninitialized_generic(nrows1, ncols2, self.len() + rhs.len());
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let mut timestamps = VectorN::zeros_generic(nrows1, U1);
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let mut workspace = unsafe { VectorN::new_uninitialized_generic(nrows1, U1) };
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let mut workspace = VectorN::<N, R1>::zeros_generic(nrows1, U1);
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let mut nz = 0;
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for j in 0..ncols2.value() {
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@ -160,24 +159,19 @@ where
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res.data.i.resize(new_size_bound, 0);
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res.data.vals.resize(new_size_bound, N::zero());
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for (i, val) in rhs.data.column_entries(j) {
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nz = self.scatter(
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i,
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val,
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timestamps.as_mut_slice(),
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j + 1,
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workspace.as_mut_slice(),
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nz,
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&mut res,
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);
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for (i, beta) in rhs.data.column_entries(j) {
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for (k, val) in self.data.column_entries(i) {
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workspace[k] += val * beta;
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}
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}
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// Keep the output sorted.
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let range = res.data.p[j]..nz;
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res.data.i[range.clone()].sort();
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for p in range {
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res.data.vals[p] = workspace[res.data.i[p]]
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for (i, val) in workspace.as_mut_slice().iter_mut().enumerate() {
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if !val.is_zero() {
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res.data.i[nz] = i;
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res.data.vals[nz] = *val;
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*val = N::zero();
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nz += 1;
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}
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}
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}
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@ -257,3 +251,21 @@ where
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res
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}
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}
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impl<'a, 'b, N, R, C, S> Mul<N> for CsMatrix<N, R, C, S>
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where
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N: Scalar + ClosedAdd + ClosedMul + Zero,
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R: Dim,
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C: Dim,
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S: CsStorageMut<N, R, C>,
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{
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type Output = Self;
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fn mul(mut self, rhs: N) -> Self {
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for e in self.values_mut() {
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*e *= rhs
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}
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self
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}
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}
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