Evaluate allowing "fast math" compiler options to enable auto-vectorization

Auto-vectorization can improve performance for some loops. Because auto-vectorization reorders operations it violates strict IEEE floating point compliance so compilers require "fast math" options be specified to enable auto-vectorization of floating point loops. Recently, effort has been going into making EnergyPlus more "vectorization-friendly". Auto-vectorization makes it practical to get a large number of loops to vectorize so validating use of "fast math" options with EnergyPlus is of interest.

With "fast math" precision changes are probably neutral, on average. What is of more concern may be that floating point error handling, such as NaN detection/propagation, may be affected. It may not be enough to get floating point error handling only in debug builds. If there are indeed scenarios where "fast math" interferes with important floating point handling a solution may be to have a separate "fast" build that uses "fast math" options and performance switches such as setting the architecture hardware type so that more advanced vectorization pipelines, such as AVX, can be exploited. If this is the case it is recommended that Intel C++ be used for the Windows "fast" build as it offers significant performance/vectorization benefits.

Start by reviewing EnergyPlus's build configuration and the compiler options used for existing builds, then identify how auto-vectorization and fast-math settings affect floating-point error handling. Done means establishing whether a fast-math build is safe and defining the required build configuration, validation, and platform choices.