Finding the Business Case for Grids

Until recently, grid computing was an academic tool, used as a cost-effective means for tackling computationally intensive problems. In disciplines like particle physics and meteorology, when problems involved computations that could run in parallel, grids provided what were essentially cheap supercomputers.

Most enterprise applications, though, are designed for centralized processing, and couldn’t easily be diced up and distributed. This partially explains why grids have been slow to take off, despite the hype. Even so, Steve Tuecke, CEO of Univa, a grid software startup, believes grid computing is a case of Internet history repeating itself.

”As with the Internet, it was first adopted by academics. Then, it moved into large, leading-edge companies before finally gaining wider adoption,” he said.

Wider adoption happened after compelling applications, namely email, took off.

Thus far, Tueckes vision is panning out, with grids slowly making their way from academia into large enterprises. Financial institutions, energy companies, and insurance firms have found that they, like academic institutions, have large problems that can be broken down into smaller computation problems and distributed.

But, if grids stick to this supercomputer model, they will eventually hit an adoption wall. As adoption grows, however, and as the upstart grid industry prepares for the assault on the mid-tier, the concept of grid computing is changing, evolving to encompass more than just distributed computations.

This article was first published on GridComputingPlanet.com. To read the full article, click here.

RELATED NEWS AND ANALYSIS

• Huawei’s AI Update: Things Are Moving Faster Than We Think

  FEATURE |  By Rob Enderle,
  December 04, 2020

• Keeping Machine Learning Algorithms Honest in the ‘Ethics-First’ Era

  ARTIFICIAL INTELLIGENCE |  By Guest Author,
  November 18, 2020

• Key Trends in Chatbots and RPA

  FEATURE |  By Guest Author,
  November 10, 2020

• Top 10 AIOps Companies

  FEATURE |  By Samuel Greengard,
  November 05, 2020

• What is Text Analysis?

  ARTIFICIAL INTELLIGENCE |  By Guest Author,
  November 02, 2020

• How Intel’s Work With Autonomous Cars Could Redefine General Purpose AI

  ARTIFICIAL INTELLIGENCE |  By Rob Enderle,
  October 29, 2020

• Dell Technologies World: Weaving Together Human And Machine Interaction For AI And Robotics

  ARTIFICIAL INTELLIGENCE |  By Rob Enderle,
  October 23, 2020

• The Super Moderator, or How IBM Project Debater Could Save Social Media

  FEATURE |  By Rob Enderle,
  October 16, 2020

• Top 10 Chatbot Platforms

  FEATURE |  By Cynthia Harvey,
  October 07, 2020

• Finding a Career Path in AI

  ARTIFICIAL INTELLIGENCE |  By Guest Author,
  October 05, 2020

• CIOs Discuss the Promise of AI and Data Science

  FEATURE |  By Guest Author,
  September 25, 2020

• Microsoft Is Building An AI Product That Could Predict The Future

  FEATURE |  By Rob Enderle,
  September 25, 2020

• Top 10 Machine Learning Companies 2020

  FEATURE |  By Cynthia Harvey,
  September 22, 2020

• NVIDIA and ARM: Massively Changing The AI Landscape

  ARTIFICIAL INTELLIGENCE |  By Rob Enderle,
  September 18, 2020

• Continuous Intelligence: Expert Discussion [Video and Podcast]

  ARTIFICIAL INTELLIGENCE |  By James Maguire,
  September 14, 2020

• Artificial Intelligence: Governance and Ethics [Video]

  ARTIFICIAL INTELLIGENCE |  By James Maguire,
  September 13, 2020

• IBM Watson At The US Open: Showcasing The Power Of A Mature Enterprise-Class AI

  FEATURE |  By Rob Enderle,
  September 11, 2020

• Artificial Intelligence: Perception vs. Reality

  FEATURE |  By James Maguire,
  September 09, 2020

• Anticipating The Coming Wave Of AI Enhanced PCs

  FEATURE |  By Rob Enderle,
  September 05, 2020

• The Critical Nature Of IBM’s NLP (Natural Language Processing) Effort

  ARTIFICIAL INTELLIGENCE |  By Rob Enderle,
  August 14, 2020