HIGH PERFORMANCE COMPUTING
Tech focus: storage
ADVANCES IN STORAGE TECHNOLOGY ENABLE SCIENTISTS TO ACCELERATE THEIR RESEARCH
As the size and complexity of data sets grows, storage
technology is playing an increasingly important role in helping to accelerate scientific research. Increases in data requirements are driving users towards large-scale computing systems and increasing demand for larger and faster storage systems. Scientists and researchers
increasingly rely on storage to further their research – whether the focus is traditional high performance computing (HPC) workloads, artificial intelligence (AI) and deep learning (DL), or just to process and store huge volumes of sequencing data. Seagate is taking part in an
EU funded research project to develop a new storage architecture that can better suit scientists working in extreme scale HPC, AI and DL workloads or big data analysis. The innovatively named ‘Percipient Storage for Exascale Data Centric Computing 2’ (Sage 2) project is a follow-up from the initial work done to develop the architecture. Sage 2 now widens the scope to include both AI and DL workloads. In a recent SIG IO UK digital
event, Sai Narasimhamurthy, managing principal engineer at Seagate, gave a presentation on developments in the Sage
4 Scientific Computing World Summer 2020
2 project. Narasimhamurthy explained that the scope of the project was to address ‘classic extreme computing applications’ that have ‘continually changing IO requirements’ that mean current hardware cannot keep up: ‘We also looked at the overlap between extreme computing and big data analysis in the Sage project but in Sage 2 we are bringing AI and DL applications to the table.’
In the presentation
Narasimhamurthy commented that AI and DL applications have unique requirements for IO and storage that are still evolving and require deeper analysis. ‘We are still trying to find out if there are unique classes of AI and DL applications that have unique IO requirements,’ said Narasimhamurthy. The project is codesigned with particular applications and use cases by Seagate in the UK. The prototype system is housed at the Julich supercomputing center, based on four tiers of storage devices managed by an object file system rather than a traditional parallel file system. ‘We are trying to explore the use of object storage, not just as an archival tier, but also as a scratch for HPC and big data analysis. We already have that implemented fully.’
The full list of Participants in the Sage2 consortium are: Seagate (UK), Bull-ATOS (France), UK Atomic Energy Agency (UK), KTH Royal Institute of Technology (Sweden), Kitware (France), University of Edinburgh (UK), Jülich Supercomputing Centre (Germany), French Alternative Energies and Atomic Energy Commission/CEA (France). This group combines both
”We have finished the codesign with the applications and we are beginning to do the application porting now”
technology providers and application developers so that relevant codes for large scale HPC, AI and big data analysis. ‘We have finished the
codesign with the applications and we are beginning to do the application porting now, especially the AI and DL applications. For the global memory abstraction we have fully defined and implemented the special API for object mapping,’ added Narasimhamurthy. ‘There is a PKMD available on ARM and emulation of NVDIMM on the Arm platform. We are also actively contributing to the mainline Linux kernel.’ While there has been
significant progress, it should be noted that this is a three- year project and there are several features still being
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