Nvidia has announced that it plans to integrate a high-speed interconnect, called NVLink into its future GPUs, enabling GPUs and CPUs to share data five to 12 times faster than they can today. This will eliminate a longstanding bottleneck and help pave the way for a new generation of exascale supercomputers that are 50-100 times faster than today's most powerful systems, the vendor said. Nvidia will add NVLink technology into its Pascal GPU architecture - expected to be introduced in 2016 - following Nvidia's new Maxwell compute architecture for 2013. The new interconnect was co-developed with IBM, which is incorporating it in future versions of its Power CPUs. With NVLink technology tightly coupling IBM Power CPUs with Nvidia Tesla GPUs, the Power data center ecosystem will be able to fully leverage GPU acceleration for a diverse set of applications, such as high performance computing, data analytics and machine learning. Today's GPUs are connected to x86-based CPUs through the PCI Express (PCIe) interface, which limits the GPU's ability to access the CPU memory system and is four- to five-times slower than typical CPU memory systems. PCIe is an even greater bottleneck between the GPU and IBM Power CPUs, which have more bandwidth than x86 CPUs. As the NVLink interface will match the bandwidth of typical CPU memory systems, it will enable GPUs to access CPU memory at its full bandwidth.

Nvidia has unveiled the Tesla K40 GPU accelerator. Providing double the memory and up to 40% higher performance than its predecessor, the Tesla K20X GPU accelerator, and 10 times higher performance than today's fastest CPU, the Tesla K40 GPU is optimized for big data analytics and large-scale scientific workloads, the company claimed. GPU accelerators have gone mainstream in the HPC and supercomputing industries, enabling engineers and researchers to consistently drive innovation and scientific discovery, said Sumit Gupta, general manager of Tesla Accelerated Computing products at Nvidia. With the breakthrough performance and higher memory capacity of the Tesla K40 GPU, enterprise customers can quickly crunch through massive volumes of data generated by their big data analytics applications. Key features of the Tesla K40 GPU accelerator include: 12GB of ultra-fast GDDR5 memory; 2,880 CUDA parallel processing cores; dynamic parallelism; and the PCIe Gen-3 interconnect support. In a related announcement, the Texas Advanced Computing Center (TACC) at the University of Texas at Austin plans to deploy "Maverick," a new interactive, remote visualization and data analysis system powered by Nvidia's Tesla K40 GPU accelerators. Maverick is expected to be fully operational in January 2014.

Nvidia has unveiled a new family of Tesla GPUs based on Nvidia Kepler GPU computing architecture, which makes GPU-accelerated computing easier and more accessible for a broader range of high performance computing (HPC) scientific and technical applications. The new Nvidia Tesla K10 and K20 GPUs are computing accelerators built to handle the most complex HPC problems in the world. Designed with an intense focus on high performance and extreme power efficiency, Kepler is three times as efficient as its predecessor, the Nvidia Fermi architecture, which itself established a new standard for parallel computing when introduced two years ago, the vendor said. Nvidia developed a set of innovative architectural technologies that make the Kepler GPUs high performing and highly energy efficient, as well as more applicable to a wider set of developers and applications including SMX Streaming Multiprocessor, Dynamic Parallelism and Hyper-Q, it said.