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November 2018

Dear ComputingEdge reader:

Advances in High-Performance Computing

This issue of ComputingEdge showcases two articles on HPC, both originally published in Computing in Science & Engineering. “A Dream of Simplicity: Scientific Computing on Turing Machines” discusses how computational scientists can best prepare their data and software for high-performance computations. In “Enhancing Product Innovation with Computational Engineering,” the authors describe how product developers in diverse industries such as automotive and sporting goods are harnessing the power of HPC to design and analyze virtual prototypes of new products.

Enjoy this issue »

 
Breaking Out
 
Get out of your familiar environment, gentle colleagues. Look beyond your accepted understanding of technology. We sometimes get so used to the concepts, tools, and procedures used in our field that we falsely believe them to be universal. We are surprised when we find people who don’t understand our systems the way that we do.
 
Let’s start with Ernst’s article on autonomous cars. He asks us to look at these devices as cyber-physical systems. Look beyond the performance of the system. Consider its dynamics, safety, and potential for communication. Similarly, look at how Post and Goldfarb try to expand our view of product engineering. And how Schneier suggests that artificial intelligence could defend cyberspace. All these are new views of familiar landscapes.
 
Want to consider a chilling idea? Read the piece by Kshetri and Voas and then ask whether that cute little stuffed bunny beneath your kid’s bed is as harmless as it looks. The Internet of Things is everywhere, and with it comes cyber risks. That sweet toy might easily be reporting your child’s vital signs, capturing data on the home environment, and recording the time it takes you to respond to the baby’s cry.
 
If you want to push your intellect, let Hinsen remind you that all our fancy computing environments are layered upon the theoretically simple concept of a Turing Machine. He’ll also needle you to concede that all that computing theory you learned as an undergrad might help you a bit with your engineering computations.
 
If you can read but one article from this issue of ComputingEdge, look at the piece by Choo and Liu about interpreting deep-learning systems. It’s a problem on the cutting edge of research. We know how to make these systems work, but we don’t know what the internal parameters mean. And deep-learning models don’t merely have five or ten parameters. They have millions.
 
So you can see how this issue of ComputingEdge can help you break out of the familiar view of technology. If you need more, we have Lenz and Heinrichs telling you what you can learn from guidance apps; Almeida, Doneda, and Moreira da Costa asking you how the technology of smart cities interacts with governance; and, finally, Patel, Ali, and Sheth giving you tools that will give you a new view—a truly graphical view—of cloud and fog computing.
 
—David Alan Grier for ComputingEdge
 

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