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Exploring the Convergence of AI, Data and HPC

The demand for performant and scalable AI solutions has stimulated a convergence of science, algorithm development, and affordable technologies to create a software ecosystem designed to support the data scientist. A special insideHPC report explores how HPC and the data driven AI communities are converging as they are arguably running the same types of data and compute intensive workloads on HPC hardware, be it on a leadership class supercomputer, small institutional cluster, or in the cloud.

Special Report: AI-HPC is Happening Now

HPC and the data driven AI communities are converging as they are arguably running the same types of data and compute intensive workloads on HPC hardware, be it on a leadership class supercomputer, small institutional cluster, or in the cloud. Download the insideHPC Special Report, brought to you by Intel, to learn more about AI-HPC and how today’s businesses are using this technology.

BlueData, Intel Compare Bare-Metal & Containers for Big Data Workloads

Has your business ever tried to decide between a bare-metal environment or a container-based environment for dealing with Big Data needs? BlueData and Intel collaborated to discuss this very issue in a benchmark study of the performance of Big Data workloads.

Bare-Metal Performance for Big Data Workloads on Docker Containers

In a benchmark study, Intel compared the performance of Big Data workloads running on a bare-metal deployment versus running in Docker containers with the BlueData EPIC software platform. The study found that it is possible to run Big Data workloads in a container-based environment without sacrificing performance. The benefits include agility, flexibility, and cost efficiency. Data science teams can get on-demand Hadoop and Spark clusters, while leveraging enterprise-grade security in a multi-tenant architecture. Get the white paper to learn about this breakthrough benchmark study.

InsideBIGDATA Guide to Data Analytics in Government

This guide provides an in-depth overview of the use of data analytics technology in the public sector. Focus is given to how data analytics is being used in the government setting with a number of high-profile use case examples. Download this InsideBIGDATA guide to data analytics in government.

FPGAs Speed Machine Learning at SC16 Intel Discovery Zone

In this video from SC16, Intel demonstrates how Altera FPGAs can accelerate Machine Learning applications with greater power efficiency. “The demo was put together using OpenCL design tools and then compiled to FPGA. From an end-user perspective, they tied it together using Intel MKL-DNN with CAFFE on top of that. This week, Intel announced the DLIA Deep Learning Inference Accelerator that brings the whole solution together in a box.”

Pikazo: Deep Neural Network Art on Intel Architecture

In this video from the Intel HPC Developer Conference, Noah Rosenberg and Karl Stiefvater from Pikazo describe the company’s innovative Pikazo App for smartphones. “Pikazo was developed in 2015 using neural style transfer algorithms. It is a collaboration between human, machine, and our concept of art. It is a universal art machine that paints any image in the style of any other, producing sometimes-beautiful, sometimes-disturbing, always-surprising artworks. Pikazo allows novice artists to create impressive imagery via a technique known as neural style transfer.”

Performance Optimization of Deep Learning Frameworks on Modern Intel Architectures

In this video from the Intel HPC Developer Conference, Elmoustapha Ould-ahmed-vall from Intel describes how the company is doubling down to optimize Machine Learning frameworks for Intel Platforms. Using open source frameworks as a starting point, surprising speedups are possible using Intel technologies.

New SGI Scale-out Solution for SAP HANA Does Real-Time Applications

Today SGI announced that enterprises can now leverage the Intel-based SGI UV 300H server in a multi-node cluster (scale out) to run SAP Business Warehouse (SAP BW) on SAP HANA or new SAP BW/4HANA. Unique to SGI, the cluster nodes can later be reconfigured as single-node systems with 1 to 32TB of shared memory (scale up) to run SAP S/4HANA and other real-time applications. “For large enterprises that plan to migrate to SAP S/4HANA but wish to begin their journey to SAP HANA with SAP BW, our new SGI cluster offering is unquestionably the optimal solution,” said Jorge Titinger, president and CEO, SGI. “The scalability of the SGI UV 300H architecture coupled with our expertise in mission-critical environments provides an ideal path to real-time business with SAP HANA.”

InsideBIGDATA Guide to Healthcare & Life Sciences

This technology guide provides an overview of the utilization of big data technologies as an emerging discipline in healthcare and life sciences. It explores the characteristics of this business strategy and the benefits of leveraging big data technologies within these sectors. It also touches on the challenges and future directions of big data and analytics in the healthcare and life sciences industries. To learn more download this insideBIGDATA guide.