Percipient Storage for Exascale Data Centric Computing 2
A unified data storage system platform for AI, Deep learning, Big Data Analysis & High Performance Computing workloads
The landscape for extreme computing and big data analysis is changing with the proliferation of enormous volumes of data created by scientific instruments and sensors, in addition to data from simulations. This data needs to be stored, processed and analysed, and existing storage system technologies need to be adapted to achieve reasonable efficiencies in achieving higher scientific throughput at massive scales.
The SAGE consortium lead by Seagate started on the journey to address this problem within the SAGE project. The HPC & Big Data Analysis use cases and the technology ecosystem is now further evolving and there are new requirements and innovations that are brought to the forefront from AI/Deep learning. It is extremely critical to address them today without “reinventing the wheel” leveraging existing initiatives and know-how to build the pieces of the Exascale puzzle as quickly and efficiently as we can.
Sage2, follow on to the SAGE project, again led by Seagate, intends to validate a next generation storage system building on top of the already existing SAGE platform to address new use case requirements in the areas of extreme scale computing scientific workflows and AI/deep learning leveraging the latest developments in storage infrastructure software and storage technology ecosystem. Sage2 aims to provide significantly enhanced scientific throughput, improved scalability, and, time & energy to solution for these workloads. Sage2 will also dramatically increase the productivity of developers and users of these systems.
Sage2 will also provide global memory addressing capability to persistent storage resources for the new class of applications and workflows and include new arm based in-storage processing environments.
Stay Tuned! For more information contact: email@example.com
Duration: 36 months starting September 2018.
The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 800999