GPU Computing

For computationally challenging problems in business and scientific research, GPGPU (General-purpose computing on graphics processing units) represent a technological breakthrough that can deliver significant benefits in performance. Highly parallel, many-core architectures proposed by vendors of low-cost graphics card such as AMD or NVIDIA provide means to parallelize scientific code to an extent that could enable faster financial trades, faster design of more aerodynamic cars or development of new drugs at lower cost.

Since Moore’s Lawe is nearly at the end, there is an inevitable shift towards many-core processors and massive parallelizm. Such an approach can be considered as one of the biggest advances in computing in recent history. All processors from all the major vendors will become many-core designs over the next few years. It is still unlcear which direction will be selected by the business and user community, e.g. many identical mainstream CPU cores or a heterogeneous mix of different kinds of core. Still, it is unavoidable that there will be lots of cores in each processor. A natural consequence of this change is that most software and algorithms will need redesigning.

Vratis is one the companies that are active in GPU computing since the early adoption of high-level languages such as CUDA or OpenCL. We have been working in the field of acceleration of scientific code for several years now and offer two products that accelerate sparse linear algebra and computational fluid dynamics:

  • SpeedIT is a library of iterative solvers and preconditioners that accelerate solving large system of linear equations. Official Page
  • Arael is a 3D FVM-based transient and steady-state solver for incompressible, laminar flows.

Our products are being develop with a group of post-docs from Polish universities and in collaboration with research institutions and companies such as NVIDIA, Engys or IconCFD. The products are well received by the market and are competitive in terms of performance and quality.

NVIDIA Tesla Video Success Stories

Art, Science & GPU Tokyo Institute of Technology Builds Tesla GPU Cluster University of Illinois at Urbana-Champaign Using GPUs for VMD Temple University GPUs and Surfactant Modeling Sandia National Lab GPUs for LAMMPS NVIDIA Tesla 20 Series Code Named "Fermi" University of Maryland GPUs Deliver 25x Speed Up Oxford University Observes 50X Faster Finance Calculations with GPUs University of Illinois UC Parallel Programing with CUDA FFA on Revolutionizing the Search for New Oil and Gas Fields Ansys FEA and CUDA Mathematica and CUDA