libthermo

A fast C++ thermodynamical gas library.

libthermo targets high-performance computing using really simple/simplistic thermodynamical modelings instead of complex and more accurate approaches (such as https://cantera.org/).

It's well fitted for industrial simulation where the base blocks need to be efficient to not become bottlenecks.

Modelings

Properties

The library provides modelings for the following properties:

Short	Long
Cp	Specific heat pressure
Gamma	Specific heat ratio
r	Gas constant
H	Enthalpy
Phi	Entropy
PR	Pressure ratio
EffPoly	Polytropic efficiency

Properties

For now, only ideal gas is implemented in IdealGas class.

It has constant specific heat pressure, thus ratio.

Benchmarks

Benchmarks can be ran using:

• C++: thermo_benchs CMake target
• Python: ipython python_benchs.ipy

The metrics are computed over 1M elements for operation only (allocation is not timed), and are given in nanoseconds/element:

IdealGas

Case	Pure NumPy	C++ loop on vector	C++ xtensor	+ xsimd	+ tbb		pythermo*
Cp	0.27	0.12	0.32	0.32	0.31
Gamma	0.30	0.12	0.31	0.31	0.31
r	0.28	0.45
H	0.73	0.45	0.80	0.75	0.26
Phi	4.54	4.99	4.35	2.97	0.30		0.30
PR	11.5	14.41	13.65	8.28	0.55		0.53
EffPoly	17.1	16.18	14.87	9.87	2.59		2.68

*pythermo = Python bindings, incl. xsimd and tbb (vectorization and multithreading)

Conclusions are:

• Pure NumPy (vectorized), C++ loop over a 1D buffer allocated on the heap (std::vector) and xtensor are directly comparable
  • xtensor shows performance issues on very simples cases (Cp, gamma, H): to be investigated
• xsimd and tbb, turned on by simply adding 2 CMake flags give a huge benefit
  • up to x1.65 speed-up for xsimd
  • up to x15 speed-up for tbb, on a 12-cores/24-threads AMD Ryzen 3900XT
• using xtensor-python Python bindings to operate on NumPy buffers gives the same performance as pure C++ code