Keith Packard, HPE | LinuxCon 2015
Channel: SiliconANGLE theCUBE
Date: 20/08/2015

Inside “The Machine” with distinguished HP engineer | #LinuxCon
by Elizabeth Kays | Nov 5, 2015


https://siliconangle.com/2015/11/05/inside-the-machine-with-distinguished-hp-engineer-linuxcon/


 Hewlett-Packard Enterprise (HP) has bet big on the Machine, a piece of technology large enough to handle the biggest of Big Data—and anything else the industry can throw at it. Keith Packard, Distinguished Engineer at HP, joined Jeff Frick on theCUBE, from the SiliconANGLE Media team, at LinuxCon 2015 to talk about the Machine’s innovation and use of open-source technology.

“The machine is actually a collection of technologies,” Packard explained. “We have three ideas. We have silicon doing computation, our traditional CPUs. We have photonics, which is silicon photonics, an optical interconnect directly between integrated circuits. And then we have the new HP memristor which is a high density, low power storage technology. And we’re combining those together into an enormous machine with a large amount of compute and a large amount of memory.”

And by large, he means enormous. “The initial version of the machine we’re building right now will have 80 nodes with four terabytes of memory on every node for a total of 320 terabytes [of memory] and 80 CPUs …. in a single rack,” Packard said.

Challenges and Opportunities

Such an undertaking isn’t without its challenges, according to Packard, especially when it comes to software development. “One of the big opportunities that we have is that we have a phenomenal amount of memory,” he said. “The Linux kernel’s not quite ready for that, and so we have a lot of work to do in the operating system itself to get it to support that amount of memory. We also are building a massively parallel machine with up to 80 CPUs …. You have to do a lot of work in software to make sure the data is actually transmitted between the CPUs correctly and there aren’t any data collisions or data corruption.”

This will allow for applications ranging from really big data analytics endeavors to graphing problems and the sorting of huge datasets—and it’s all going to be open-source. “The only operating system we’re doing on the machine is all going to be under GPL, so everything is free software.”


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