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Use Eigen on CPUs. #1349

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Use Eigen on CPUs. #1349

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7 changes: 6 additions & 1 deletion src/solver/newton/newton.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -72,6 +72,9 @@ EIGEN_DEVICE_FUNC int newton_solver(Eigen::Matrix<double, N, 1>& X,
// we have converged: return iteration count
return iter;
}
Eigen::Matrix<double, N, 1> X_solve;

#if defined(CORENEURON_ENABLE_GPU)
// In Eigen the default storage order is ColMajor.
// Crout's implementation requires matrices stored in RowMajor order (C-style arrays).
// Therefore, the transposeInPlace is critical such that the data() method to give the rows
Expand All @@ -83,8 +86,10 @@ EIGEN_DEVICE_FUNC int newton_solver(Eigen::Matrix<double, N, 1>& X,
// Check if J is singular
if (nmodl::crout::Crout<double>(N, J.data(), pivot.data(), rowmax.data()) < 0)
return -1;
Eigen::Matrix<double, N, 1> X_solve;
nmodl::crout::solveCrout<double>(N, J.data(), F.data(), X_solve.data(), pivot.data());
#else
X_solve = J.partialPivLu().solve(F);
#endif
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Could you add a brief description/comment if this is done for performance reasons or something else?

I don't remember this well and hence asking for clarity: do we expect small differences in the results between the two? If there are two code paths, just think from CPU vs GPU debugging or results comparison perspectives. (obviously, not thinking fp differences as they exist on cpu vs gpu. But more from solver/accuracy perspective).

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Yes, without using NMODL is a 25% regression, compared to NOCMODL on the Masoli circuit we've been running recently.

That's a tricky question. The variation we implement ourselves is also "partial pivoting". There's two ways of doing LU decompositions: Dolittle and Crout. They result in almost the same matrices. Mostly I think they just iterate over the matrix differently. For a well-conditioned matrix, I doubt it makes any difference.

X -= X_solve;
}
// If we fail to converge after max_iter iterations, return -1
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