Bull's contribution has been essential to the project’s success, leading the architecture and several key technical deliveries, including the concept of ephemeral data nodes and data accessors introduced to flexibly operate the system, using various well-known data access paradigms, such as POSIX namespaces, S3/Swift Interfaces, MPI-IO and other 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 work-flows that need to be deployed in highly heterogeneous computing environments.

Critical aspects of intelligent data placement are considered for extreme volumes of data. This ensures that the right resources among the storage tiers are used and accessed by data nodes as close as possible to compute nodes – optimising performance, cost, and energy at extreme scale. Advanced IO instrumentation and monitoring features will be developed to that effect leveraging the latest advancements in AI and machine learning to systematically analyse the telemetry records to make smart decisions on data placement. These ideas coupled with in-storage-computation remove unnecessary data movements within the system.