Thursday, July 15, 2010

EM Photonics Supports Fermi

EM Photonics, Inc. announced today the general availability of CULA 2.0, its GPU-accelerated linear algebra library used by thousands of developers and scientists worldwide. The new version provides support for NVIDIA GPUs based on the latest "Fermi" architecture, which offers HPC users unprecedented performance in double-precision mathematics, faster memory, and new usability features.

"The Tesla 20-series GPUs deliver a huge increase in double precision performance," said Andy Keane, General Manager for the Tesla high-performance computing group at NVIDIA. "The LAPACK functionality provided by CULA is critical to many applications ranging from computer-aided engineering and medical image reconstruction to climate change models, financial analysis and more. This new release is great news for developers who can easily accelerate their application with CULA 2.0.," he added.

"CULA 2.0 is the next step in the evolution of our product, where we can finally show strong double precision performance to complement our already impressive single precision speeds. Users of older GPUs will also see performance improvements as well as new routines and increased accuracy. As we continue tuning our CULA library for Fermi, users can expect to see even better performance as well as new features in the next few months," said Eric Kelmelis, CEO of EM Photonics.

Product Features

CULA contains a LAPACK interface comprised of over 150 mathematical routines from the industry standard for computational linear algebra, LAPACK. EM Photonics' CULA library includes many popular routines including system solvers, least squares solvers, orthogonal factorizations, eigenvalue routines, and singular value decompositions.

CULA offers performance up to a magnitude faster than highly optimized CPU-based linear algebra solvers. There is a variety of different interfaces available to integrate directly into your existing code. Programmers can easily call GPU-accelerated CULA from their C/C++, FORTRAN, MATLAB, or Python codes. This can all be done with no GPU programming experience. CULA is available for every system equipped with GPUs based on the NVIDIA CUDA architecture. This includes 32- and 64-bit versions of Linux, Windows, and OS X.

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