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basicbenchmark.h
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Thu, Feb 20, 23:31
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rDLMA Diffusion limited mixed aggregation
basicbenchmark.h
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#ifndef EIGEN_BENCH_BASICBENCH_H
#define EIGEN_BENCH_BASICBENCH_H
enum {LazyEval, EarlyEval, OmpEval};
template<int Mode, typename MatrixType>
void benchBasic_loop(const MatrixType& I, MatrixType& m, int iterations) __attribute__((noinline));
template<int Mode, typename MatrixType>
void benchBasic_loop(const MatrixType& I, MatrixType& m, int iterations)
{
for(int a = 0; a < iterations; a++)
{
if (Mode==LazyEval)
{
asm("#begin_bench_loop LazyEval");
if (MatrixType::SizeAtCompileTime!=Eigen::Dynamic) asm("#fixedsize");
m = (I + 0.00005 * (m + m.lazyProduct(m))).eval();
}
else if (Mode==OmpEval)
{
asm("#begin_bench_loop OmpEval");
if (MatrixType::SizeAtCompileTime!=Eigen::Dynamic) asm("#fixedsize");
m = (I + 0.00005 * (m + m.lazyProduct(m))).eval();
}
else
{
asm("#begin_bench_loop EarlyEval");
if (MatrixType::SizeAtCompileTime!=Eigen::Dynamic) asm("#fixedsize");
m = I + 0.00005 * (m + m * m);
}
asm("#end_bench_loop");
}
}
template<int Mode, typename MatrixType>
double benchBasic(const MatrixType& mat, int size, int tries) __attribute__((noinline));
template<int Mode, typename MatrixType>
double benchBasic(const MatrixType& mat, int iterations, int tries)
{
const int rows = mat.rows();
const int cols = mat.cols();
MatrixType I(rows,cols);
MatrixType m(rows,cols);
initMatrix_identity(I);
Eigen::BenchTimer timer;
for(uint t=0; t<tries; ++t)
{
initMatrix_random(m);
timer.start();
benchBasic_loop<Mode>(I, m, iterations);
timer.stop();
cerr << m;
}
return timer.value();
};
#endif // EIGEN_BENCH_BASICBENCH_H
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