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The Essential Guide: Machine Scheduling for AI Workloads on GPUs

This white paper by Run:AI (virtualization and acceleration layer for deep learning) addresses the challenges of expensive and limited compute resources and identifies solutions for optimization of resources, applying concepts from the world of virtualization, High-Performance Computing (HPC), and distributed computing to deep learning.

Intel® Parallel Studio XE 2020: Transform Enterprise, Cloud, HPC & Artificial Intelligence with Faster Parallel Code

In this article we’ll drill down into the capabilities of Intel® Parallel Studio XE 2020, the latest release of a comprehensive, parallel programming tool suite that simplifies the creation and modernization of code. Using this newest release, software developers and architects can speed AI inferencing with support for Intel® Deep Learning Boost and Vector Neural Network Instructions (VNNI), designed to accelerate inner convolutional neural network (CNN) loops.

Understanding Intention: Using Content, Context, and the Crowd to Build Better Search Applications

This white paper by enterprise search specialists Lucidworks, points out that unlike consumer search, which has become a seamless part of our everyday lives, the enterprise side might as well still be running Windows 95. Imagine if Amazon, Google, or Facebook treated every user the same, regardless of who they are, where they are, what they’re searching for, and what they’ve clicked. Your users expect that same sophistication in their enterprise apps.

Machine Learning for All: the Democratizing of a Technology

Our friends over at H2O.ai have produced a short new eBook “Machine learning for all: the democratizing of a technology” which covers machine learning features and automatic AI solutions, and how organizations can benefit from using them.

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.

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.

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.

Models for Thinking: An Example of Why Data Sciences Increasingly Need the Humanities

Parsing such large-scale data sets – classifying genomic sequences, mapping forms of advertisement, observing online discussions, etc. – is a matter of organization: How do you make sense of, and classify, these clusters of information? The answer, often, is to configure them into abstract but coherent topics.

Why You Need a Modern Infrastructure to Accelerate AI and ML Workloads

Recent years have seen a boom in the generation of data from a variety of sources: connected devices, IoT, analytics, healthcare, smartphones, and much more. This data management problem is particularly acute in the areas of Artificial Intelligence (AI) and Machine Learning (ML) workloads. This guest article from WekaIO highlights why focusing on optimizing infrastructure can spur machine learning workloads and AI success.