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

“Above the Trend Line” – Your Industry Rumor Central for 1/27/2020

Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as M&A activity, people movements, funding news, financial results, industry alignments, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

New Research from Accenture and Qlik Shows the Data Skills Gap is Costing Organizations Billions in Lost Productivity

A new report from Accenture (NYSE: ACN) and Qlik, titled “The Human Impact of Data Literacy” and conducted on behalf of The Data Literacy Project, found that while most organizations understand the incredible opportunity of data, a gap has emerged between organizations’ aspirations to be data-driven and their employees’ ability to create business value with data.

oneAPI: – A Unified Cross-Architecture, High Performance Programming Model Designed to Help Shape the Future of Application Development

In this article, we’ll dive into the newly announced oneAPI, a single, unified programming model that aims to simplify development across multiple architectures, such as CPUs, GPUs, FPGAs and other accelerators. The long-term journey is represented by two important first-steps – the industry initiative and the Intel beta product.

Heterogeneous Computing Programming: oneAPI and Data Parallel C++

Sponsored Post What you missed at the Intel Developer Conference, and how to catch-up today By James Reinders In the interests of full disclosure … I must admit that I became sold on DPC++ after Intel approached me (as a consultant – 3 years retired from Intel) asking if I’d help with a book on […]

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.