LAMA 2.0 accelerates more than just numerical applications

www.libama.org Fraunhofer SCAI

By offering DSL-like C++-Syntax LAMA encourages writing of hardware-independent code. The framework allows the management of data on heterogeneous and distributed system architectures. These can range from embedded “System on a Chip” (aka SoC) devices to highly-parallel Supercomputers.

Consequently, LAMA offers full cluster support. Kernel management is provided with ready-to-use modules encapsulating interfaces to Intel® MKL and Nvidia® cuBLAS/cuSPARSE (targeting all multicore CPUs, Nvidia® GPUs, and Intel® Xeon® Phi™). For the purpose of extending LAMA, the framework supports the integration of custom modules.

Hence, it greatly facilitates the development of fast and scalable software for nearly every system on every scale. LAMA accelerates the time-to-market for new product developments significantly, reduces the time spent in maintaining existing code, and offers up-to-date hardware compatibility for even the latest architectures.

Typically, LAMA targets applications with needs in numerical mathematics (such as CFD and FEM simulation). Furthermore, LAMA 2.0 integrates methodology in the areas of optimization (e.g. for seismic imaging), computer vision, and deep learning.

It is available with an industry-friendly dual licensing model (AGPL for open source or individual agreements for other interests).

http://www.libama.org

Media Contact

Michael Krapp Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI

Alle Nachrichten aus der Kategorie: Information Technology

Here you can find a summary of innovations in the fields of information and data processing and up-to-date developments on IT equipment and hardware.

This area covers topics such as IT services, IT architectures, IT management and telecommunications.

Zurück zur Startseite

Kommentare (0)

Schreib Kommentar

Neueste Beiträge

Machine learning aids in simulating dynamics of interacting atoms

Automated approach transformative for computational materials science. A revolutionary machine-learning (ML) approach to simulate the motions of atoms in materials such as aluminum is described in this week’s Nature Communications…

“Intelligent” turbines for green energy from tidal water power

Fluid flow engineers and electrical engineers are jointly developing turbine blades with special integrated drives Tidal hydroelectric power plants of the future will be able to generate “green” electricity significantly…

‘Missing Ice Problem’ Finally Solved

During glacial periods, the sea level falls, because vast quantities of water are stored in the massive inland glaciers. To date, however, computer models have been unable to reconcile sea-level…

Partners & Sponsors