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.

AccelerEyes Supports Fermi

AccelerEyes today announced that its Jacket software platform for MATLAB, including its new 1.4 release, will support the latest NVIDIA graphics processing units (GPU) based on the Fermi architecture (Tesla 20-series and GeForce GTX 4xx-series). NVIDIA's release of the Fermi architecture brings with it 448 computational cores, increased IEEE-754 floating-point arithmetic precision, error-correcting memory for reliable computation, and enhanced memory caching mechanisms.

AccelerEyes develops Jacket, a software platform that delivers GPU computing power to desktop users of MATLAB and other very high level languages. It enables faster prototyping and problem solving across a range of government, manufacturing, energy, media, biomedical, financial, and scientific research applications. The Jacket software platform enables accelerated double-precision performance for common arithmetic and linear algebra functionality on the new NVIDIA hardware based on the FERMI architecture.

"With this release of Jacket and with the familiar MATLAB environment, domain experts can create highly optimized heterogeneous applications for the latest CPUs from Intel and AMD while also leveraging the latest generation of GPUs from NVIDIA," said John Melonakos, CEO of AccelerEyes. "Efficiently using all available host cores for certain parts of an application while accelerating other portions on GPUs is the key to squeezing maximum performance out of today's GPU-enabled workstations, servers, clusters, and cloud services. With Fermi's improvement in double-precision performance, we expect a big increase in the number and type of applications that benefit from GPU acceleration."