Nvidia’s Tesla K80 dual-GPU offers 24 GB graphics memory
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"NVIDIA GPUs have become the de facto computing platform for the deep learning community," said Yann LeCun, director of AI Research at Facebook, and Silver Professor of Computer Science & Neural Science at New York University. "Because the accuracy of deep learning systems improves as the models and datasets get larger, we always look for the fastest hardware we can find. The Tesla K80 accelerator, with its dual-GPU architecture and large memory, gives us more teraflops and more GB than ever before from a single server, allowing us to make faster progress in deep learning."
The Tesla K80 delivers up to 8.74 teraflops single-precision and up to 2.91 teraflops double-precision peak floating point performance, and10 times higher performance than today's fastest CPUs on leading science and engineering applications, such as AMBER, GROMACS, Quantum Espresso and LSMS.
"The Tesla K80 dual-GPU accelerators are up to 10 times faster than CPUs when enabling scientific breakthroughs in some of our key applications, and provide a low energy footprint," said Wolfgang Nagel, director of the Center for Information Services and HPC at Technische Universität Dresden in Germany. "Our researchers use the available GPU resources on the Taurus supercomputer extensively to enable a more refined cancer therapy, understand cells by watching them live, and study asteroids as part of ESA's Rosetta mission."
Key features of the Tesla K80 dual-GPU accelerator include:
- Two GPUs per board - Doubles throughput of applications designed to take advantage of multiple GPUs.
- 24GB of ultra-fast GDDR5 memory - 12GB of memory per GPU, 2x more memory than Tesla K40 GPU, allows users to process 2x larger datasets.
- 480GB/s memory bandwidth - Increased data throughput allows data scientists to crunch though petabytes of information in half the time compared to the Tesla K10 accelerator. Optimized for energy exploration, video and image processing, and data analytics applications.
- 4,992 CUDA® parallel processing cores - Accelerates applications by up to 10x compared to using a CPU alone.
- Dynamic NVIDIA GPU Boost Technology - Dynamically scales GPU clocks based on the characteristics of individual applications for maximum performance.
- Dynamic Parallelism - Enables GPU threads to dynamically spawn new threads, enabling users to quickly and easily crunch through adaptive and dynamic data structures.
Source: http://nvidianews.nvidia.com/News/NV...uting-c15.aspx
