Refactor verification method and optimize numerical kernels with oneMKL BLAS
This commit transitions the verification approach from post-Newtonian theory
comparison to regression testing against baseline simulations, and optimizes
critical numerical kernels using Intel oneMKL BLAS routines.
Verification Changes:
- Replace PN theory-based RMS calculation with trajectory-based comparison
- Compare optimized results against baseline (GW150914-origin) on XY plane
- Compute RMS independently for BH1 and BH2, report maximum as final metric
- Update documentation to reflect new regression test methodology
Performance Optimizations:
- Replace manual vector operations with oneMKL BLAS routines:
* norm2() and scalarproduct() now use cblas_dnrm2/cblas_ddot (C++)
* L2 norm calculations use DDOT for dot products (Fortran)
* Interpolation weighted sums use DDOT (Fortran)
- Disable OpenMP threading (switch to sequential MKL) for better performance
Build Configuration:
- Switch from lmkl_intel_thread to lmkl_sequential
- Remove -qopenmp flags from compiler options
- Maintain aggressive optimization flags (-O3, -xHost, -fp-model fast=2, -fma)
Other Changes:
- Update .gitignore for GW150914-origin, docs, and temporary files
This commit is contained in:
@@ -27,6 +27,7 @@ using namespace std;
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#endif
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#include "TwoPunctures.h"
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#include <mkl_cblas.h>
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TwoPunctures::TwoPunctures(double mp, double mm, double b,
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double P_plusx, double P_plusy, double P_plusz,
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@@ -891,25 +892,17 @@ double TwoPunctures::norm1(double *v, int n)
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/* -------------------------------------------------------------------------*/
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double TwoPunctures::norm2(double *v, int n)
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{
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int i;
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double result = 0;
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for (i = 0; i < n; i++)
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result += v[i] * v[i];
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return sqrt(result);
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// Optimized with oneMKL BLAS DNRM2
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// Computes: sqrt(sum(v[i]^2))
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return cblas_dnrm2(n, v, 1);
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}
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/* -------------------------------------------------------------------------*/
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double TwoPunctures::scalarproduct(double *v, double *w, int n)
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{
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int i;
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double result = 0;
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for (i = 0; i < n; i++)
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result += v[i] * w[i];
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return result;
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// Optimized with oneMKL BLAS DDOT
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// Computes: sum(v[i] * w[i])
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return cblas_ddot(n, v, 1, w, 1);
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}
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/* -------------------------------------------------------------------------*/
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