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News, reviews and feature articles on companies that are changing how organizations leverage massive volumes of data

2nd Generation Intel® Xeon® Platinum 9200 Processors Offer Leadership Performance, and Advance AI

Simulation, modeling, data analytics, and other workloads commonly use high performance computing (HPC) to advance research and business in many ways. However, as converged workloads involving AI grow in adoption, HPC systems must keep pace with evolving needs. 2nd Generation Intel® Xeon® Platinum processors, with built-in AI acceleration technologies, offer leadership performance to speed the most demanding HPC workloads.

How Astera Labs is Revolutionizing Semiconductor Product Development—100% in the Cloud

For any established semiconductor product developer, designing a next-generation PCIe 5.0 chipset in less than a year is no small feat. For a brand-new startup with no compute infrastructure other than laptops, however, it is a huge ask. That’s why, with time being of the essence, Astera Labs decided to take a chance on the efficiencies it would gain from a 100% cloud-based approach.

Six Platform Investments from Intel to Facilitate Running AI and HPC Workloads Together on Existing Infrastructure

Because HPC technologies today offer substantially more power and speed than their legacy predecessors, enterprises and research institutions benefit from combining AI and HPC workloads on a single system. Six platform investments from Intel will help reduce obstacles and make HPC and AI deployment even more accessible and practical.

insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads – Part 3

Artificial Intelligence (AI) and Deep Learning (DL) represent some of the most demanding workloads in modern computing history as they present unique challenges to compute, storage and network resources. In this technology guide, insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads, we’ll see how traditional file storage technologies and protocols like NFS restrict AI workloads of data, thus reducing the performance of applications and impeding business innovation. A state-of-the-art AI-enabled data center should work to concurrently and efficiently service the entire spectrum of activities involved in DL workflows, including data ingest, data transformation, training, inference, and model evaluation.

insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads – Part 2

Artificial Intelligence (AI) and Deep Learning (DL) represent some of the most demanding workloads in modern computing history as they present unique challenges to compute, storage and network resources. In this technology guide, insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads, we’ll see how traditional file storage technologies and protocols like NFS restrict AI workloads of data, thus reducing the performance of applications and impeding business innovation. A state-of-the-art AI-enabled data center should work to concurrently and efficiently service the entire spectrum of activities involved in DL workflows, including data ingest, data transformation, training, inference, and model evaluation.

DAOS Delivers Exascale Performance Using HPC Storage So Fast It Requires New Units of Measurement

Forget what you previously knew about high-performance storage and file systems. New I/O models for HPC such as Distributed Asynchronous Object Storage (DAOS) have been architected from the ground up to make use of new NVM technologies such as Intel® Optane™ DC Persistent Memory Modules (Intel Optane DCPMMs). With latencies measured in nanoseconds and bandwidth measured in tens of GB/s, new storage devices such as Intel DCPMMs redefine the measures used to describe high-performance nonvolatile storage.

insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads

Artificial Intelligence (AI) and Deep Learning (DL) represent some of the most demanding workloads in modern computing history as they present unique challenges to compute, storage and network resources. In this technology guide, insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads, we’ll see how traditional file storage technologies and protocols like NFS restrict AI workloads of data, thus reducing the performance of applications and impeding business innovation. A state-of-the-art AI-enabled data center should work to concurrently and efficiently service the entire spectrum of activities involved in DL workflows, including data ingest, data transformation, training, inference, and model evaluation.

StreamSets Launches StreamSets Transformer

StreamSets, Inc., provider of the DataOps platform for modern data integration, released StreamSets® Transformer, a simple-to-use, drag-and-drop UI tool to create native Apache Spark applications. Designed for a wide range of users — even those without specialized skills — StreamSets Transformer enables the creation of pipelines for performing ETL, stream processing and machine-learning operations. Now, data engineers, scientists, architects and operators gain deep visibility into the execution of Apache Spark while broadening usage across the business.

Interview: Terry Deem and David Liu at Intel

I recently caught up with Terry Deem, Product Marketing Manager for Data Science, Machine Learning and Intel® Distribution for Python, and David Liu, Software Technical Consultant Engineer for the Intel® Distribution for Python*, both from Intel, to discuss the Intel® Distribution for Python (IDP): targeted classes of developers, use with commonly used Python packages for data science, benchmark comparisons, the solution’s use in scientific computing, and a look to the future with respect to IPD.

Develop Multiplatform Computer Vision Solutions with Intel® Distribution of OpenVINO™ Toolkit

Realize your computer vision deployment needs on Intel® platforms—from smart cameras and video surveillance to robotics, transportation, and much more. The Intel® Distribution of OpenVINO™ Toolkit (includes the Intel® Deep Learning Deployment Toolkit) allows for the development of deep learning inference solutions for multiple platforms.