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Nvidia Corp. on Thursday announced Nvidia Cuda 6, the latest version its parallel computing platform and programming model. The Cuda 6 platform makes parallel programming easier than ever, enabling software developers to dramatically decrease the time and effort required to accelerate their scientific, engineering, enterprise and other applications with GPUs.

Nvidia Cuda 6 platform supports three key features – unified memory addressing, drop-in libraries and multi-GPU scaling capability – that further expose capabilities of currently-available Kepler architecture as well as next-generation Maxwell architectures; simplify development of applications that take advantage of massively parallel nature of graphics processing units and bring additional performance advantages.

Key features of Cuda 6 include:

  • Unified Memory – Simplifies programming by enabling applications to access CPU and GPU memory without the need to manually copy data from one to the other, and makes it easier to add support for GPU acceleration in a wide range of programming languages.
  • Drop-in Libraries – Automatically accelerates applications' BLAS and FFTW calculations by up to 8X by simply replacing the existing CPU libraries with the GPU-accelerated equivalents.
  • Multi-GPU Scaling – Re-designed BLAS and FFT GPU libraries automatically scale performance across up to eight GPUs in a single node, delivering over nine teraflops of double precision performance per node, and supporting larger workloads than ever before (up to 512GB). Multi-GPU scaling can also be used with the new BLAS drop-in library.

In addition to the new features, the Cuda 6 platform offers a full suite of programming tools, GPU-accelerated math libraries, documentation and programming guides. Version 6 of the Cuda Toolkit is expected to be available in early 2014.

"By automatically handling data management, unified memory enables us to quickly prototype kernels running on the GPU and reduces code complexity, cutting development time by up to 50 percent. Having this capability will be very useful as we determine future programming model choices and port more sophisticated, larger codes to GPUs," said Rob Hoekstra, manager of scalable algorithms department at Sandia National Laboratories.

"Our technologies have helped major studios, game developers and animators create visually stunning 3D animations and effects. They have been urging us to add support for acceleration on Nvidia GPUs, but memory management proved too difficult a challenge when dealing with the complex use cases in production. With Unified Memory, this is handled automatically, allowing the Fabric compiler to target Nvidia GPUs and enabling our customers to run their applications up to 10X faster," said Paul Doyle, chief executive of Fabric Engine.

Tags: Nvidia, CUDA, Kepler, Maxwell, Geforce, Quadro, GPGPU

Discussion

Comments currently: 5
Discussion started: 11/16/13 09:27:42 PM
Latest comment: 11/17/13 11:41:48 PM

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1. 
These are all pseudo unified memory triks. The real hardware level unfied memory implementation is in AMDs advanced HSA architecture. NVidia is sacred of AMDs advancement in HSA and APU technologies.
4 4 [Posted by: tks  | Date: 11/17/13 02:54:51 AM]
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