How HPE's memory driven computing is tackling Alzheimer's
German researchers can now process large data sets in 13 seconds
Researchers in Germany have been using HPE's high performance servers to speed up their research into brain diseases like Alzheimer's.
Joachim Schultze, a researcher at the German Center for Neurodegenerative Diseases (DZNE) and the University of Bonn, needed a way to collect all the data involved in his research and access it easily.
There were several difficulties Schultze faced, not least that the DZNE has nine research sites in Germany and that each nurse has a device for collecting data, giving it a vastly distributed edge. Additionally, the organisation has data from historical studies that are still in use.
"We have big data from large studies 30,000 people over 30 years, and we want to be looking at the data over time going back and back and back again," said Schultze.
The researchers got their hands on two HPE machines this year: the Superdome X and the Memory Fabric Testbed; Schultze said "it is enormously exciting".
The researchers saw immediate changes to the amount of time it took to process the data sets they were using, Schultze said, dropping from 22 minutes to 13 seconds. The new technology also consumes 60% less energy than the high-performance computing (HPC) set up the DZNE was using previously.
Schultze is excited about applying this to future projects too and wants to upgrade to HPE's new Superdome Flex.
"We have encouraged people at our institute now to think 'What could you do if this was possible?' They see it's possible and now they start to think about it again," he said.
In the future he would like to use the machines in the sequencing centre of genomes centres. He could then share the metadata with colleagues in different centres which would help the DNZE enormously.
He wants to have the nine sites equipped with these machines in the future. "I think that integration could not be envisioned if you didn't have somebody on the other side", he said, pointing to HPE.
It's not only genomic data this applies to, it can also be used for clinical, laboratory and imaging data. Schultze would like to integrate the data "to really search for bigger patterns to really understand what the diseases are".
Image source: Bigstock
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