From e524860443bd334511dfd27533957d3655dd78a6 Mon Sep 17 00:00:00 2001 From: kronbichler Date: Thu, 10 Jan 2013 16:31:06 +0000 Subject: [PATCH] Improve a few comments. git-svn-id: https://svn.dealii.org/trunk@28005 0785d39b-7218-0410-832d-ea1e28bc413d --- deal.II/examples/step-37/step-37.cc | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/deal.II/examples/step-37/step-37.cc b/deal.II/examples/step-37/step-37.cc index fb8834e003..dd7af582d4 100644 --- a/deal.II/examples/step-37/step-37.cc +++ b/deal.II/examples/step-37/step-37.cc @@ -108,7 +108,7 @@ namespace Step37 // one CPU instruction. Newer processors with support for the so-called // advanced vector extensions (AVX) with 256 bit operands can use four // doubles and eight floats, respectively. Vectorization is a - // single-instruct/multiple-data (SIMD) concept, that is, one CPU + // single-instruction/multiple-data (SIMD) concept, that is, one CPU // instruction is used to process multiple data values at once. Often, // finite element programs do not use vectorization explicitly as the // benefits of this concept are only in arithmetic intensive operations. The @@ -125,7 +125,7 @@ namespace Step37 // vectorization, matrix-matrix products, fails on most compilers (as of // writing this tutorial in early 2012, neither gcc-4.6 nor the Intel // compiler v. 12 manage to produce useful vectorized code for the - // FullMatrix::mmult function, and not even on the more simpler case where + // FullMatrix::mmult function, and not even on the simpler case where // the matrix bounds are compile-time constants instead of run-time // constants as in FullMatrix::mmult). The main reason for this is that the // information to be processed at the innermost loop (that is where @@ -419,7 +419,7 @@ namespace Step37 LaplaceOperator:: evaluate_coefficient (const Coefficient &coefficient_function) { - const unsigned int n_cells = data.get_size_info().n_macro_cells; + const unsigned int n_cells = data.n_macro_cells(); FEEvaluation phi (data); coefficient.resize (n_cells * phi.n_q_points); for (unsigned int cell=0; cellNext comes the + // FEValues and costs $\mathcal O(d (p+1)^{2d})$.
  • Next comes the // application of the Jacobian transformation, the multiplication by the // variable coefficient and the quadrature weight. FEEvaluation has an // access function @p get_gradient that applies the Jacobian and returns the @@ -531,8 +531,7 @@ namespace Step37 const std::pair &cell_range) const { FEEvaluation phi (data); - AssertDimension (coefficient.size(), - data.get_size_info().n_macro_cells * phi.n_q_points); + AssertDimension (coefficient.size(), data.n_macro_cells() * phi.n_q_points); for (unsigned int cell=cell_range.first; cell