Accelerate OpenFOAM® with Culises

April 13th, 2015

Culises significantly accelerates your OpenFOAM® application by using GPUs for the computationally most intensive tasks.

Its main features are

  • Library for GPU-based acceleration of OpenFOAM®
  • Multi-GPU support, significantly reduced computing times
  • Highly efficient state-of-the-art iterative solvers like AMG
  • Quick and easy installation, no validation necessary
  • Flexible interfaces to customer-specific software/engineering applications available

The acceleration of the linear solver by Culises is greater than 2x. The overall speedup depends on the type of application and the time spent in the linear solver. Culises my be tested on FluiDyna’s purpose-built workstation to determine the acceleration potential for your individual OpenFOAM® application. Find out more on:

SpeedIT FLOW: RANS single-phase fluid flow solver on GPU

September 4th, 2014

SpeedIT FLOW is a RANS single-phase fluid flow solver that runs fully on GPU. Benchmark results on external aero flow and other industry-relevant OpenFOAM cases on a GPU card indicate approximately 3x faster time to solution vs. Intel Xeon E5649 running 12 cores. This is about two times faster than competing solutions that offer only partial acceleration on GPU. More details are available on this blog.

Acceleration of iterative Navier-Stokes solvers on graphics processing units

July 14th, 2013


While new power-efficient computer architectures exhibit spectacular theoretical peak performance, they require specific conditions to operate efficiently, which makes porting complex algorithms a challenge. Here, we report results of the semi-implicit method for pressure linked equations (SIMPLE) and the pressure implicit with operator splitting (PISO) methods implemented on the graphics processing unit (GPU). We examine the advantages and disadvantages of the full porting over a partial acceleration of these algorithms run on unstructured meshes. We found that the full-port strategy requires adjusting the internal data structures to the new hardware and proposed a convenient format for storing internal data structures on GPUs. Our implementation is validated on standard steady and unsteady problems and its computational efficiency is checked by comparing its results and run times with those of some standard software (OpenFOAM) run on central processing unit (CPU). The results show that a server-class GPU outperforms a server-class dual-socket multi-core CPU system running essentially the same algorithm by up to a factor of 4.

See also supplementary materials and the follow up at

(Tadeusz Tomczak, Katarzyna Zadarnowska, Zbigniew Koza, Maciej Matyka and Łukasz Mirosław: “Acceleration of iterative Navier-Stokes solvers on graphics processing units”, International Journal of Computational Fluid Dynamics, accepted, July 2013. [DOI])