In this special guest feature, Rob Farber from TechEnablement writes that the Intel Scalable Systems Framework is pushing the boundaries of Machine Learning performance. “machine learning and other data-intensive HPC workloads cannot scale unless the storage filesystem can scale to meet the increased demands for data.”
Intel Enterprise Edition for Lustre* Software has taken a leap toward greater enterprise capabilities and improved features for HPC with release of version 3.0. This latest version includes new security enhancements, dynamic LNET configuration support, ZFS snapshots, and other features asked for by the HPC community inside and outside the enterprise. Additionally, it adds the Intel Omni-Path Architecture drivers.
“Presto is a perfect fit with the Teradata Unified Data Architecture, an integrated analytical ecosystem for our enterprise customers. Presto enables companies to leverage standard ANSI SQL to execute interactive queries against Hadoop data. With Presto, utilizing Teradata’s Query Grid connector for Presto, customers can execute queries that originate in Teradata Integrated Data Warehouse that join data within the IDW and Hadoop leveraging Presto.”
In this slidecast, Marc Hamilton from Nvidia describes the latest updates to the company’s Deep Learning Platform. “Great hardware needs great software. To help data scientists and developers make the most of the vast opportunities in deep learning, we’re announcing today at the International Supercomputing show, ISC16, a trio of new capabilities for our deep learning software platform. The three — NVIDIA DIGITS 4, CUDA Deep Neural Network Library (cuDNN) 5.1 and the new GPU Inference Engine (GIE) — are powerful tools that make it even easier to create solutions on our platform.”
In this video from the HPC User Forum in Tucson, Prabhat from NERSC presents: Machine Learning. “Prabhat leads the Data and Analytics Services team at NERSC. His current research interests include scientific data management, parallel I/O, high performance computing and scientific visualization.”
“This talk will provide an overview of challenges in accelerating Hadoop, Spark and Memcached on modern HPC clusters. An overview of RDMA-based designs for multiple components of Hadoop (HDFS, MapReduce, RPC and HBase), Spark, and Memcached will be presented. Enhanced designs for these components to exploit in-memory technology and parallel file systems (such as Lustre) will be presented. Benefits of these designs on various cluster configurations using the publicly available RDMA-enabled packages from the OSU HiBD project (http://hibd.cse.ohio-state.edu) will be shown.”
Today Cornell University announced a five-year, $5 million project sponsored by the National Science Foundation to build a federated cloud comprised of data infrastructure building blocks (DIBBs) designed to support scientists and engineers requiring flexible workflows and analysis tools for large-scale data sets, known as the Aristotle Cloud Federation.
Today SGI, a global leader in high-performance solutions for compute, data analytics, and data management introduced the SGI UV 300RL for big data in-memory analytics. As a new model in the SGI UV server line certified and supported with Oracle Linux, the SGI UV 300RL provides up to 32 sockets and 24 terabytes of shared memory. The solution enables enterprises that have standardized on Intel-based servers to run Oracle Database In-Memory on a single system to help achieve real-time operations and accelerate data analytics at unprecedented scale.
Today SGI announced that global deployments of the SGI UV 300H single-node system provide in total over 200 Terabytes of in-memory computing capacity to organizations running the SAP HANA platform. Introduced just one year ago, more than 50 SGI UV 300H systems have been installed in organizations to run a variety of applications on SAP HANA, including the SAP ERP, SAP Supply Chain Management (SCM), SAP Bank Analyzer, and SAP Business Warehouse applications, as well as advanced analytics.
Joseph George from HP presented this talk at the recent HPC User Forum. “This paper describes the HP Big Data Reference Architecture (BDRA) solution and outlines how a modern architectural approach to Hadoop provides the basis for consolidating multiple big data projects while, at the same time, enhancing price/performance, density, and agility. HP BDRA is a modern, flexible architecture for the deployment of big data solutions; it is designed to improve access to big data, rapidly deploy big data solutions, and provide the flexibility needed to optimize the infrastructure in response to the ever-changing requirements in a Hadoop ecosystem.”