Sign up for our newsletter and get the latest HPC news and analysis.
Send me information from insideHPC:

insideHPC Guide to QCT Platform-on-Demand Designed for Converged Workloads

Not too long ago, building a converged HPC/AI environment – with two domains: High Performance Computing (HPC) and Artificial Intelligence (AI) – would require spending a lot of money on proprietary systems and software with the hope that it would scale as business demands changed.

In this insideHPC technology guide, “insideHPC Guide to QCT Platform-on-Demand Designed for Converged Workloads,”as we’ll see, by relying on open source software and the latest high performance/low cost system architectures, it is possible to build scalable hybrid on-premises solutions that satisfy the needs of converged HPC/AI workloads while being robust and easily manageable.


As businesses become more and more data driven, they quickly realize that to stay competitive they need a  solution that not only provides advanced capabilities for performing highly complex technical computations,  but can support deep data collection and predictive analysis at the same time.

Traditionally, these two domains—High Performance Computing (HPC) and Artificial Intelligence (AI)—existed as separate environments, each with their own unique hardware, software, storage, and networking requirements. HPC usually involves a significant amount of computing power employing state-of-the art parallel processing techniques. On the other hand, AI (including Machine Learning (ML), and Deep Learning (DL)), employs iterative algorithms to find insights hidden in oceans of data collected over time.

What converged HPC/AI workloads look like

Lately, we see growth in converged HPC/AI workloads in healthcare, finance, and automotive industries,  among others. For example:

  • In healthcare, pharmaceutical companies search for new medicines for treatment of tumors by applying  bioinformatics solutions that utilize HPC molecular dynamics modeling to identify potential compounds, and  deep learning to analyze gene expression profiles, and recognize images of human cancers.
  • In finance, data-rich HPC simulation techniques and predictive modeling with AI are now at the heart of all  strategic financial planning and analysis.
  • In the automotive industry, real-time AI object recognition training and response techniques for  autonomous vehicles, along with HPC simulations of vehicle crash testing, make up most critical workloads.

Building a comprehensive on-premises cluster to handle both a company’s HPC and AI workloads from  scratch is known to be complicated, costly, and hard to manage. Doing it right demands extensive knowledge of both kinds of workloads and the infrastructures they require.

Quanta Cloud Technology (QCT), a major cloud data center solution provider based in Taiwan, California, Singapore, and Germany, has created a converged, on-premises platform to satisfy HPC/AI customer needs while accelerating performance, reducing overall operating cost, and greatly simplifying system management.

Over the next few weeks we’ll explore QCT’s Platform-on-Demand designed for converged workloads:

  • Introduction, What converged HPC/AI workloads look like
  • The converged Platform-on-Demand solution from QCT
  • High Performance Computing with QCT POD
  • Deep Learning with QCT POD, Summary, About QCT

Download the complete insideHPC Guide to QCT Platform-on-Demand Designed for Converged Workloads courtesy of QCT.





Resource Links: