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377 lines
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377 lines
16 KiB
Plaintext
[template perf[name value] [value]]
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[template para[text] '''<para>'''[text]'''</para>''']
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[mathpart perf Performance]
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[section:perf_over2 Performance Overview]
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[performance_overview]
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[endsect]
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[section:interp Interpreting these Results]
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In all of the following tables, the best performing
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result in each row, is assigned a relative value of "1" and shown
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in bold, so a score of "2" means ['"twice as slow as the best
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performing result".] Actual timings in nano-seconds per function call
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are also shown in parenthesis. To make the results easier to read, they
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are color-coded as follows: the best result and everything within 20% of
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it is green, anything that's more than twice as slow as the best result is red,
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and results in between are blue.
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Result were obtained on a system
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with an Intel core i7 4710MQ with 16Gb RAM and running
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either Windows 8.1 or Xubuntu Linux.
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[caution As usual with performance results these should be taken with a large pinch
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of salt: relative performance is known to shift quite a bit depending
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upon the architecture of the particular test system used. Further
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more, our performance results were obtained using our own test data:
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these test values are designed to provide good coverage of our code and test
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all the appropriate corner cases. They do not necessarily represent
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"typical" usage: whatever that may be!
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]
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[endsect]
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[section:getting_best Getting the Best Performance from this Library: Compiler and Compiler Options]
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By far the most important thing you can do when using this library
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is turn on your compiler's optimisation options. As the following
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table shows the penalty for using the library in debug mode can be
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quite large. In addition switching to 64-bit code has a small but noticeable
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improvement in performance, as does switching to a different compiler
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(Intel C++ 15 in this example).
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[table_Compiler_Option_Comparison_on_Windows_x64]
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[endsect] [/section:getting_best Getting the Best Performance from this Library: Compiler and Compiler Options]
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[section:tradoffs Trading Accuracy for Performance]
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There are a number of [link policy Policies] that can be used to trade accuracy for performance:
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* Internal promotion: by default functions with `float` arguments are evaluated at `double` precision
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internally to ensure full precision in the result. Similarly `double` precision functions are
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evaluated at `long double` precision internally by default. Changing these defaults can have a significant
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speed advantage at the expense of accuracy, note also that evaluating using `float` internally may result in
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numerical instability for some of the more complex algorithms, we suggest you use this option with care.
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* Target accuracy: just because you choose to evaluate at `double` precision doesn't mean you necessarily want
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to target full 16-digit accuracy, if you wish you can change the default (full machine precision) to whatever
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is "good enough" for your particular use case.
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For example, suppose you want to evaluate `double` precision functions at `double` precision internally, you
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can change the global default by passing `-DBOOST_MATH_PROMOTE_DOUBLE_POLICY=false` on the command line, or
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at the point of call via something like this:
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double val = boost::math::erf(my_argument, boost::math::policies::make_policy(boost::math::policies::promote_double<false>()));
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However, an easier option might be:
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#include <boost/math/special_functions.hpp> // Or any individual special function header
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namespace math{
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namespace precise{
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//
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// Define a Policy for accurate evaluation - this is the same as the default, unless
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// someone has changed the global defaults.
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//
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typedef boost::math::policies::policy<> accurate_policy;
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//
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// Invoke BOOST_MATH_DECLARE_SPECIAL_FUNCTIONS to declare
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// functions that use the above policy. Note no trailing
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// ";" required on the macro call:
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//
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BOOST_MATH_DECLARE_SPECIAL_FUNCTIONS(accurate_policy)
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}
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namespace fast{
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//
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// Define a Policy for fast evaluation:
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//
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using namespace boost::math::polcies;
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typedef policy<promote_double<false> > fast_policy;
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//
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// Invoke BOOST_MATH_DECLARE_SPECIAL_FUNCTIONS:
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//
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BOOST_MATH_DECLARE_SPECIAL_FUNCTIONS(fast_policy)
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}
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}
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And now one can call:
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math::accurate::tgamma(x);
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For the "accurate" version of tgamma, and:
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math::fast::tgamma(x);
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For the faster version.
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Had we wished to change the target precision (to 9 decimal places) as well as the evaluation type used, we might have done:
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namespace math{
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namespace fast{
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//
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// Define a Policy for fast evaluation:
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//
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using namespace boost::math::polcies;
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typedef policy<promote_double<false>, digits10<9> > fast_policy;
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//
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// Invoke BOOST_MATH_DECLARE_SPECIAL_FUNCTIONS:
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//
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BOOST_MATH_DECLARE_SPECIAL_FUNCTIONS(fast_policy)
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}
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}
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One can do a similar thing with the distribution classes:
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#include <boost/math/distributions.hpp> // or any individual distribution header
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namespace math{ namespace fast{
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//
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// Define a policy for fastest possible evaluation:
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//
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using namespace boost::math::polcies;
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typedef policy<promote_float<false> > fast_float_policy;
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//
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// Invoke BOOST_MATH_DECLARE_DISTRIBUTIONS
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//
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BOOST_MATH_DECLARE_DISTRIBUTIONS(float, fast_float_policy)
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}} // namespaces
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//
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// And use:
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//
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float p_val = cdf(math::fast::normal(1.0f, 3.0f), 0.25f);
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Here's how these options change the relative performance of the distributions on Linux:
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[table_Distribution_performance_comparison_for_different_performance_options_with_GNU_C_version_5_1_0_on_linux]
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[endsect] [/section:tradoffs Trading Accuracy for Performance]
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[section:multiprecision Cost of High-Precision Non-built-in Floating-point]
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Using user-defined floating-point like __multiprecision has a very high run-time cost.
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To give some flavour of this:
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[table:linpack_time Linpack Benchmark
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[[floating-point type] [speed Mflops]]
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[[double] [2727]]
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[[__float128] [35]]
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[[multiprecision::float128] [35]]
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[[multiprecision::cpp_bin_float_quad] [6]]
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]
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[endsect] [/section:multiprecision Cost of High-Precision Non-built-in Floating-point]
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[section:tuning Performance Tuning Macros]
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There are a small number of performance tuning options
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that are determined by configuration macros. These should be set
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in boost/math/tools/user.hpp; or else reported to the Boost-development
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mailing list so that the appropriate option for a given compiler and
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OS platform can be set automatically in our configuration setup.
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[table
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[[Macro][Meaning]]
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[[BOOST_MATH_POLY_METHOD]
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[Determines how polynomials and most rational functions
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are evaluated. Define to one
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of the values 0, 1, 2 or 3: see below for the meaning of these values.]]
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[[BOOST_MATH_RATIONAL_METHOD]
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[Determines how symmetrical rational functions are evaluated: mostly
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this only effects how the Lanczos approximation is evaluated, and how
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the `evaluate_rational` function behaves. Define to one
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of the values 0, 1, 2 or 3: see below for the meaning of these values.
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]]
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[[BOOST_MATH_MAX_POLY_ORDER]
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[The maximum order of polynomial or rational function that will
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be evaluated by a method other than 0 (a simple "for" loop).
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]]
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[[BOOST_MATH_INT_TABLE_TYPE(RT, IT)]
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[Many of the coefficients to the polynomials and rational functions
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used by this library are integers. Normally these are stored as tables
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as integers, but if mixed integer / floating point arithmetic is much
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slower than regular floating point arithmetic then they can be stored
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as tables of floating point values instead. If mixed arithmetic is slow
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then add:
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#define BOOST_MATH_INT_TABLE_TYPE(RT, IT) RT
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to boost/math/tools/user.hpp, otherwise the default of:
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#define BOOST_MATH_INT_TABLE_TYPE(RT, IT) IT
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Set in boost/math/config.hpp is fine, and may well result in smaller
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code.
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]]
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]
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The values to which `BOOST_MATH_POLY_METHOD` and `BOOST_MATH_RATIONAL_METHOD`
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may be set are as follows:
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[table
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[[Value][Effect]]
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[[0][The polynomial or rational function is evaluated using Horner's
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method, and a simple for-loop.
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Note that if the order of the polynomial
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or rational function is a runtime parameter, or the order is
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greater than the value of `BOOST_MATH_MAX_POLY_ORDER`, then
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this method is always used, irrespective of the value
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of `BOOST_MATH_POLY_METHOD` or `BOOST_MATH_RATIONAL_METHOD`.]]
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[[1][The polynomial or rational function is evaluated without
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the use of a loop, and using Horner's method. This only occurs
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if the order of the polynomial is known at compile time and is less
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than or equal to `BOOST_MATH_MAX_POLY_ORDER`. ]]
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[[2][The polynomial or rational function is evaluated without
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the use of a loop, and using a second order Horner's method.
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In theory this permits two operations to occur in parallel
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for polynomials, and four in parallel for rational functions.
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This only occurs
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if the order of the polynomial is known at compile time and is less
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than or equal to `BOOST_MATH_MAX_POLY_ORDER`.]]
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[[3][The polynomial or rational function is evaluated without
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the use of a loop, and using a second order Horner's method.
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In theory this permits two operations to occur in parallel
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for polynomials, and four in parallel for rational functions.
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This differs from method "2" in that the code is carefully ordered
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to make the parallelisation more obvious to the compiler: rather than
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relying on the compiler's optimiser to spot the parallelisation
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opportunities.
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This only occurs
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if the order of the polynomial is known at compile time and is less
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than or equal to `BOOST_MATH_MAX_POLY_ORDER`.]]
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]
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The performance test suite generates a report for your particular compiler showing which method is likely to work best,
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the following tables show the results for MSVC-14.0 and GCC-5.1.0 (Linux). There's not much to choose between
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the various methods, but generally loop-unrolled methods perform better. Interestingly, ordering the code
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to try and "second guess" possible optimizations seems not to be such a good idea (method 3 below).
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[table_Polynomial_Method_Comparison_with_Microsoft_Visual_C_version_14_0_on_Windows_x64]
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[table_Rational_Method_Comparison_with_Microsoft_Visual_C_version_14_0_on_Windows_x64]
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[table_Polynomial_Method_Comparison_with_GNU_C_version_5_1_0_on_linux]
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[table_Rational_Method_Comparison_with_GNU_C_version_5_1_0_on_linux]
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[endsect] [/section:tuning Performance Tuning Macros]
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[section:comp_compilers Comparing Different Compilers]
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By running our performance test suite multiple times, we can compare the effect of different compilers: as
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might be expected, the differences are generally small compared to say disabling internal use of `long double`.
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However, there are still gains to be main, particularly from some of the commercial offerings:
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[table_Compiler_Comparison_on_Windows_x64]
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[table_Compiler_Comparison_on_linux]
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[endsect] [/section:comp_compilers Comparing Different Compilers]
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[section:comparisons Comparisons to Other Open Source Libraries]
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We've run our performance tests both for our own code, and against other
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open source implementations of the same functions. The results are
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presented below to give you a rough idea of how they all compare.
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In order to give a more-or-less level playing field our test data
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was screened against all the libraries being tested, and any
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unsupported domains removed, likewise for any test cases that gave large errors
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or unexpected non-finite values.
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[caution
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You should exercise extreme caution when interpreting
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these results, relative performance may vary by platform, the tests use
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data that gives good code coverage of /our/ code, but which may skew the
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results towards the corner cases. Finally, remember that different
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libraries make different choices with regard to performance verses
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numerical stability.
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]
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The first results compare standard library functions to Boost equivalents with MSVC-14.0:
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[table_Library_Comparison_with_Microsoft_Visual_C_version_14_0_on_Windows_x64]
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On Linux with GCC, we can also compare to the TR1 functions, and to GSL and RMath:
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[table_Library_Comparison_with_GNU_C_version_5_1_0_on_linux]
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And finally we can compare the statistical distributions to GSL, RMath and DCDFLIB:
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[table_Distribution_performance_comparison_with_GNU_C_version_5_1_0_on_linux]
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[endsect] [/section:comparisons Comparisons to Other Open Source Libraries]
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[section:perf_test_app The Performance Test Applications]
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Under ['boost-path]\/libs\/math\/reporting\/performance you will find
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some reasonable comprehensive performance test applications for this library.
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In order to generate the tables you will have seen in this documentation (or others
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for your specific compiler) you need to invoke `bjam` in this directory, using a C++11
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capable compiler. Note that
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results extend/overwrite whatever is already present in
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['boost-path]\/libs\/math\/reporting\/performance\/doc\/performance_tables.qbk,
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you may want to delete this file before you begin so as to make a fresh start for
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your particular system.
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The programs produce results in Boost's Quickbook format which is not terribly
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human readable. If you configure your user-config.jam to be able to build Docbook
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documentation, then you will also get a full summary of all the data in HTML format
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in ['boost-path]\/libs\/math\/reporting\/performance\/html\/index.html. Assuming
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you're on a 'nix-like platform the procedure to do this is to first install the
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`xsltproc`, `Docbook DTD`, and `Bookbook XSL` packages. Then:
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* Copy ['boost-path]\/tools\/build\/example\/user-config.jam to your home directory.
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* Add `using xsltproc ;` to the end of the file (note the space surrounding each token, including the final ";", this is important!)
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This assumes that `xsltproc` is in your path.
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* Add `using boostbook : path-to-xsl-stylesheets : path-to-dtd ;` to the end of the file. The `path-to-dtd` should point
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to version 4.2.x of the Docbook DTD, while `path-to-xsl-stylesheets` should point to the folder containing the latest XSLT stylesheets.
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Both paths should use all forward slashes even on Windows.
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At this point you should be able to run the tests and generate the HTML summary, if GSL, RMath or libstdc++ are
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present in the compilers path they will be automatically tested. For DCDFLIB you will need to place the C
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source in ['boost-path]\/libs\/math\/reporting\/performance\/third_party\/dcdflib.
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If you want to compare multiple compilers, or multiple options for one compiler, then you will
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need to invoke `bjam` multiple times, once for each compiler. Note that in order to test
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multiple configurations of the same compiler, each has to be given a unique name in the test
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program, otherwise they all edit the same table cells. Suppose you want to test GCC with
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and without the -ffast-math option, in this case bjam would be invoked first as:
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bjam toolset=gcc -a cxxflags=-std=gnu++11
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Which would run the tests using default optimization options (-O3), we can then run again
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using -ffast-math:
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bjam toolset=gcc -a cxxflags='-std=gnu++11 -ffast-math' define=COMPILER_NAME='"GCC with -ffast-math"'
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In the command line above, the -a flag forces a full rebuild, and the preprocessor define COMPILER_NAME needs to be set
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to a string literal describing the compiler configuration, hence the double quotes - one for the command line, one for the
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compiler.
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[endsect] [/section:perf_test_app The Performance Test Applications]
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[endmathpart]
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[/
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Copyright 2006 John Maddock and Paul A. Bristow.
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Distributed under the Boost Software License, Version 1.0.
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(See accompanying file LICENSE_1_0.txt or copy at
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http://www.boost.org/LICENSE_1_0.txt).
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]
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