Sign up for our newsletter and get the latest big data news and analysis.
Daily
Weekly

Atos HPC Software Suites

This whitepaper, “Atos HPC Software Suites,” from our friends over at Atos explains the main features  and functionalities of the Atos HPC Software Suites and shares the company’s vision for the significant evolutions coming next in the HPC software arena.

Atos HPC Software Suites

This whitepaper from our friends over at Atos explains the main features  and functionalities of the Atos HPC Software Suites and shares the company’s vision for the significant evolutions coming next in the HPC software arena.

Practical Hardware Design Strategies for Modern HPC Workloads – Part 3

This special research report sponsored by Tyan discusses practical hardware design strategies for modern HPC workloads. As hardware continued to develop, technologies like multi-core, GPU, NVMe, and others have allowed new application areas to become possible. These application areas include accelerator assisted HPC, GPU based Deep learning, and Big Data Analytics systems. Unfortunately, implementing a general purpose balanced system solution is not possible for these applications. To achieve the best price-to-performance in each of these application verticals, attention to hardware features and design is most important.

Practical Hardware Design Strategies for Modern HPC Workloads – Part 2

This special research report sponsored by Tyan discusses practical hardware design strategies for modern HPC workloads. As hardware continued to develop, technologies like multi-core, GPU, NVMe, and others have allowed new application areas to become possible. These application areas include accelerator assisted HPC, GPU based Deep learning, and Big Data Analytics systems. Unfortunately, implementing a general purpose balanced system solution is not possible for these applications. To achieve the best price-to-performance in each of these application verticals, attention to hardware features and design is most important.

Practical Hardware Design Strategies for Modern HPC Workloads

This special research report sponsored by Tyan discusses practical hardware design strategies for modern HPC workloads. As hardware continued to develop, technologies like multi-core, GPU, NVMe, and others have allowed new application areas to become possible. These application areas include accelerator assisted HPC, GPU based Deep learning, and Big Data Analytics systems. Unfortunately, implementing a general purpose balanced system solution is not possible for these applications. To achieve the best price-to-performance in each of these application verticals, attention to hardware features and design is most important.

Practical Hardware Design Strategies for Modern HPC Workloads

Many new technologies used in High Performance Computing (HPC) have allowed new application areas to  become possible. Advances like multi-core, GPU, NVMe, and others have created application verticals that  include accelerator assisted HPC, GPU based Deep Learning, Fast storage and parallel file systems, and Big  Data Analytics systems. In this special insideHPC technology guide sponsored by our friends over at Tyan, we look at practical hardware design strategies for modern HPC workloads.

Exascale for Everyone

The exascale hype has been gaining a lot of steam in the press lately, and for good reason. Ever since the petascale barrier was broken in 2008, technology users, companies and research institutions have set their ‘sites’ on the holy grail of computing milestones. In this guest article, Matt Ziegler, Director HPC & AI Product Management, HPC Product Marketing at Lenovo, explores the evolution of and potential of exascale computing.

AI Hardware to Support the Artificial Intelligence Software Ecosystem

Balance ratios are key to understanding the plethora of AI hardware solutions that are being developed or are soon to become available. This post from an insideHPC Special Report explores AI hardware options to support the growing artificial intelligence software ecosystem. 

Solving AI Hardware Challenges

For many deep learning startups out there, buying AI hardware and a large quantity of powerful GPUs is not feasible. So many of these startup companies are turning to cloud GPU computing to crunch their data and run their algorithms. Katie Rivera, of One Stop Systems, explores some of the AI hardware challenges that can arise, as well as the new tools designed to tackle these issues. 

Scaling Hardware for In-Memory Computing

The two methods of scaling processors are based on the method used to scale the memory architecture and are called scaling-out or scale-up. Beyond the basic processor/memory architecture, accelerators and parallel file systems are also used to provide scalable performance. “High performance scale-up designs for scaling hardware require that programs have concurrent sections that can be distributed over multiple processors. Unlike the distributed memory systems described below, there is no need to copy data from system to system because all the memory is globally usable by all processors.”