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During a race, overtaking a vehicle — for example — is difficult by nature. Drivers are trying to pass on a twisting track in close proximity to one other, where wind turbulence becomes highly unpredictable.

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To understand the aerodynamics between cars travelling at over miles per hour on a winding track, engineering firms have turned to data intensive computing in order to produce images like the one below:. Computational Fluid Dynamics simulation of a passing maneuver with unsteady flow, moving mesh and rotating tires.

Image courtesy of Swift Engineering , Inc. On the consumer side, this leads to the development of more fuel-efficient and safer vehicles.

On the Formula 1 side, modeling is key to producing safer and faster supercars. The promise of data intensive computing is that it can bring together the technologies of the newest data analytics technologies with traditional supercomputing, where scalability is king. This marriage of technologies empowers the development of platforms to solve the most complex problems in the world. Developed for supercomputing, globally addressable memory and low latency network technologies bring the ability to achieve new levels of scalability to analytics.

Achieving application scalability can only be done if the networking and memory features of the systems are large, efficient and scalable. Notably, two apex cloud virtues are feature richness and flexibility. To maximize these virtues, the cloud sacrifices user architectural control and consequently fails to meet the challenge of applications that require scale and complexity. Companies across all different verticals need to find the right balance of usage between the flexibility of cloud and the power of scalable systems.

Finding the proper balance results in the best ROI and ultimately segments leadership in a highly competitive business landscape. Just as the cloud is a delivery mechanism for generic computing, now data intensive, scalable system results can be delivered without necessarily purchasing a supercomputer.

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Most importantly this service is available through a subscription-based model as well as through system acquisition. It is designed to help companies discover, understand and take action against cyber attackers, and the US Department of Defense currently uses it to glean actionable insights on potential threat vectors.

In the end, the choice to implement a data intensive computing solution comes down to the amount of data an organization has, and how quickly analysis is required. Fast-moving datasets help spur innovation, inform strategy decisions, enhance customer relationships, inspire new products and more.

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So if an organization is struggling to maintain its framework productivity, data intensive computing may well provide the fastest, most cost-effective solution. Like this article? Subscribe to our weekly newsletter to never miss out! Follow DataconomyMedia. Bolding was appointed vice president in and was responsible for product management, corporate and product marketing for high performance computing solutions, and storage and data management.

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Enabling Practical Processing in and near Memory for Data-Intensive Computing

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