  Simulating the flow of different fluids can be a highly computational
intensive process, which requires large amounts of resources. Recently there
has been a lot of research effort directed towards GPU processing, which can
greatly increase the performance of different applications, such as Smoothed
Particle Hydrodynamics (SPH), which is most commonly used for hydrodynamic
simulations. Smoothed particle hydrodynamics (SPH) is a numerical method
commonly used in Computational Fluid Dynamics (CFD). It is a method that can
simulate particle flow and interaction with structures and highly deformable
bodies. It replaces the fluid with a set of particles that carry properties
such as mass, speed and position that move according to the governing dynamics.
The dynamics of fluids are based on the Navier-Stokes equations. These describe
the physical properties of continuous fields in the fluid. SPH approximates
these equations using an integral interpolant that is then solved numerically.
This article addresses the current state of technologies available that can be
used to speed up the algorithm and proposes a set of optimizations that can be
achieved by using different frameworks. We also draw conclusions regarding the
equilibrium between performance and accuracy, using different numerical
algorithms, frameworks and hardware optimizations.
