Storage I/O and Data Management for Exascale Architectures
Multi-tiered Storage
Ephemeral Data Life Cycle Management
Advanced Instrumentation
Package of New API's
This project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 955811. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and France, the Czech Republic, Germany, Ireland, Sweden and the United Kingdom.
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IO-SEA Kick-off
The IO-SEA team welcomes you to our new website where we will keep you informed about our work, events and other HPC-related information. We also
About IO-SEA
IO-SEA aims to provide a novel data management and storage platform for exascale computing based on hierarchical storage management (HSM) and on-demand provisoning of storage services. The platform will efficiently make use of storage tiers spanning NVMe and NVRAM at the top all the way down to tape-based technologies. System requirements are driven by data intensive use-cases, in a very strict co-design approach. The concept of ephemeral data nodes and data accessors is introduced that allow users to flexibly operate the system.
IO-SEA Use Cases
IO-SEA aims to provide a novel data management and storage platform for exascale computing based on hierarchical storage management (HSM) and on-demand provisioning of storage services. The platform will efficiently make use of storage tiers spanning NVMe and NVRAM at the top all the way down to least active data stored with tape-based technologies. System requirements are driven by data intensive use-cases, in a very strict co-design approach. The concept of ephemeral data nodes and data accessors is introduced that allows users to flexibly operate the system, using various well-known data access paradigms, such as POSIX namespaces, S3/Swift Interfaces, MPI-IO and other middleware, data formats and protocols. These ephemeral resources eliminate the problem of treating storage resources as static and unchanging system components – which is not a tenable proposition for data intensive exascale environments. The methods and techniques are applicable to exascale class data intensive applications and workflows that need to be deployed in highly heterogeneous computing environments.
