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New Study Details Importance of TCO for HPC Storage Buyers

Total cost of ownership (TCO) now rivals performance as a top criterion for purchasing high-performance computing (HPC) storage systems, according to an independent study published by Hyperion Research. The report, commissioned by our friends over at Panasas®, a leader in HPC data storage solutions, surveyed data center planners and managers, storage system managers, purchasing decision-makers and key influencers, as well as users of HPC storage systems.

New Study Details Importance of TCO for HPC Storage Buyers

Total cost of ownership (TCO) is often assumed to be an important consideration for buyers of HPC storage systems. Because TCO is defined differently by HPC users, it’s difficult to make comparisons based on a predefined set of attributes. With this fact in mind, our friends over at Panasas commissioned Hyperion Research to conduct a worldwide study that asked HPC storage buyers about the importance of TCO in general, and about specific TCO components that have been mentioned frequently in the past two years by HPC storage buyers.

Qumulo Offers Free Cloud Software to help Fight COVID-19 Outbreak

Today Qumulo announced it is offering its cloud-native file software, for free, to public and private sector medical and healthcare research organizations that are working to minimize the spread and impact of the COVID-19 virus.

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.

Pure Makes Customers “AI-First” Infrastructure a Reality

Pure Storage (NYSE: PSTG), a fast growing data storage company, announced a host of new and improved AI solutions that provide enterprise customers with the features and functionality needed to execute increasingly complex AI initiatives through any phase or scale. Built on Pure’s industry-leading file and object system, FlashBladeTM, and its joint AI-Ready Infrastructure (AIRITM) offering with NVIDIA, customers can develop and deploy AI rapidly to keep pace with modern business

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.

insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads

This new technology guide from DDN shows how optimized storage has a unique opportunity to become much more than a siloed repository for the deluge of data constantly generated in today’s hyper-connected world, but rather a platform that shares and delivers data to create competitive business value. The intended audience for this important new technology guide includes enterprise thought leaders (CIOs, director level IT, etc.), along with data scientists and data engineers who are a seeking guidance in terms of infrastructure for AI and DL in terms of specialized hardware. The emphasis of the guide is “real world” applications, workloads, and present day challenges.

Advanced Performance and Massive Scaling Driven by AI and DL

In this contributed article, Kurt Kuckein, Director of Marketing for DDN Storage, discusses how current enterprise and research data center IT infrastructures are woefully inadequate in handling the demanding needs of AI and DL. Designed to handle modest workloads, minimal scalability, limited performance needs and small data volumes, these platforms are highly bottlenecked and lack the fundamental capabilities needed for AI-enabled deployments.

Building a Data Catalog: A Guide to Planning & Implementing

Building and implementing a data catalog can help your enterprises’ data community discover and use the best data and analytics resources for their projects. A data catalog can help businesses achieve faster results, and make better decisions. As for the next steps to address the importance of data catalogs in your business, Data.world covers that, as well, in a new report.