SPX: Collaborative Research: Memory Fabric: Data Management for Large-scale Hybrid Memory Systems

New large-scale high performance computing systems being developed for the national labs and by US industry, combine heterogeneous memory components, accelerators and accelerator-near memory, and programmable high-performance interconnects. These memory-rich designs are attractive as they provide the compute-near-data capacity needed for improving the time to scientific discovery, and for supporting new classes of latency-sensitive data-intensive applications.

However, existing software stacks are not equipped to deal with the heterogeneity and complexity of these machine designs, which impacts application performance and machine efficiency. The Memory Fabric (MF) solution developed in this project provides new abstractions and mechanisms that permit the systems software stacks to gain deeper insight into applications' data usage patterns and requirements, and to coordinate the decisions concerning how data should be distributed across different memories, or exchanged along different interconnection paths.

The Memory Fabric (MF) architecture introduces new data-centric abstractions, memory object and memory object flow, and accompanying memory and communications management methods. The higher-level information captured in the new abstractions empowers the MF runtime to better guide the underlying memory and interconnect management, and to mask the complexities of the underlying memory substrate.

Additional benefits are derived from use of near-memory-fabric computation, including via dynamically inserted application-specific codes, which further specialize and accelerate the operations carried out by MF. MF is evaluated using several important application domains, including big data learning and analytics, and traditional high-performance scientific simulations.

Its benefits include gains in application performance and resource efficiency, while shielding applications and application developers from the underlying machine details.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.