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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.

Using Converged HPC Clusters to Combine HPC, AI, and HPDA Workloads

Many organizations follow an old trend to adopt AI and HPDA as distinct entities which leads to underutilization of their clusters. To avoid this, clusters can be converged to save (or potentially eliminate) capital expenditures and reduce OPEX costs. This sponsored post from Intel’s Esther Baldwin, AI Strategist, explores how organizations are using converged HPC to combine HPC, AI, and HPDA workloads.

Machine Learning Beyond Predefined Recipes

The next evolution in human intelligence is automating the creation of machine learning models to not follow predefined formulas, but rather adapt and evolve according to the problem’s data. While machine learning has enabled massive advancements across industries, it requires significant development and maintenance efforts from data science teams. Enter Darwin, a machine learning tool that automates the building and deployment of models at scale.

Building Fast Data Compression Code for Cloud and Edge Applications

Finding efficient ways to compress and decompress data is more important than ever. Compressed data takes up less space and requires less time and network bandwidth to transfer. In this article, we’ll discuss the data compression functions and the latest improvements in the Intel® Integrated Performance Primitives (Intel® IPP) library.

Solutions for Autonomous Driving – From Car to Cloud

From car to cloud―and the connectivity in between―there is a need for automated driving solutions that include high-performance platforms, software development tools, and robust technologies for the data center. With Intel GO automotive driving solutions, Intel brings its deep expertise in computing, connectivity, and the cloud to the automotive industry.

The Importance of Vectorization Resurfaces

Vectorization offers potential speedups in codes with significant array-based computations—speedups that amplify the improved performance obtained through higher-level, parallel computations using threads and distributed execution on clusters. Key features for vectorization include tunable array sizes to reflect various processor cache and instruction capabilities and stride-1 accesses within inner loops.