High-end supercomputing, as of Circa 2016,  is exemplified by  systems providing around a Million Billion calculations per second (10s of PetaFlop/s) compute performance. Though such machines have provided for significant innovation in basic sciences and technology, they will be insufficient to cater to the needs of science and industry in the coming years. Exascale machines with an order of a Billion Billions operations (1000 PFlop/s) will be needed.

Exascale is characterized not just by Exaflop computational capability, but also by massive volumes of data generated by both simulations running on such systems and increasingly by data generated through massive scientific experiments, crowdsourcing, and expanding sensor networks continually multiplying the volume of data. Such data must be analysed to derive valuable insights through which innovations and understanding are made possible in a vast spectrum of domains such as physics, computational biology, neuroscience, pharmaceutics, energy, and industrial manufacturing. The SAGE project, which incorporates research and innovation in hardware and enabling software, will significantly improve the performance of data I/O and enable computation and analysis to be performed more locally to data wherever it resides in the architecture, drastically minimising data movements between compute and data storage infrastructures. With a seamless view of data throughout the platform, incorporating multiple tiers of storage from memory to disk to long-term archive, it will enable API’s and programming models to easily use such a platform to efficiently utilize the most appropriate data analytics techniques suited to the problem space.

SAGE is a European Horizon 2020 funded research project, with 10 highly respected Partners led by Seagate. Indeed a multi-disciplinary collaborative approach is essential to understand and address the needs of storage systems for data intensive applications and use cases of the future.  The SAGE Project will re-define data storage for the next decade, with the depth of capabilities for the Exascale HPC compute era alongside a breadth of future ‘Big Data’ applications. For further project details please refer to the Research tab.