  The high cost of chemistry integration is a significant computational
bottleneck for realistic reactive-flow simulations using operator splitting.
Here we present a methodology to accelerate the solution of the chemical
kinetic ordinary differential equations using single-instruction, multiple-data
vector processing on CPUs using the OpenCL framework. First, we compared
several vectorized integration algorithms using chemical kinetic source terms
and analytical Jacobians from the pyJac software against a widely used
integration code, CVODEs. Next, we extended the OpenFOAM computational fluid
dynamics library to incorporate the vectorized solvers, and we compared the
accuracy of a fourth-order linearly implicit integrator -- both in vectorized
form and a corresponding method native to OpenFOAM -- with the community
standard chemical kinetics library Cantera. We then applied our methodology to
a variety of chemical kinetic models, turbulent intensities, and simulation
scales to examine a range of engineering and scientific scale problems,
including (pseudo) steady-state as well as time-dependent Reynolds-averaged
Navier--Stokes simulations of the Sandia flame D and the Volvo Flygmotor
bluff-body stabilized, premixed flame. Subsequently, we compared the
performance of the vectorized and native OpenFOAM integrators over the studied
models and simulations and found that our vectorized approach performs up to
33--35x faster than the native OpenFOAM solver with high accuracy.
