Implications of Heterogeneous Memories in Next Generation Server Systems

Abstract

Next generation datacenter and exascale machines will include significantly larger amounts of memory, greater heterogeneity in the performance, persistence or sharing properties of the memory components they encompass, and increase in the relative cost and complexity of the data paths in the resulting memory topology. This poses several challenges to the systems software stacks managing these memory-centric platform designs. First, technology advances in novel memory technologies shift the data access bottlenecks into the software stack. Second, current systems software lacks capabilities to bridge the multi-dimensional non-uniformity in the memory subsystem to the dynamic nature of the workloads it must support. In addition, current memory management solutions have limited ability to explicitly reason about the costs and tradeoffs associated with data movement operations, leading to limited efficiency of their interconnect use. To address these problems, next generation systems software stacks require new data structures, abstractions and mechanisms in order to enable new levels of efficiency in the data placement, movement, and transformation decisions that govern the underlying memory use. In this talk, I will present our approach to rearchitecting systems software and services in response to both node-level and system-wide memory heterogeneity and scale, particularly concerning the presence of non-volatile memories, and will demonstrate the resulting performance and efficiency gains using several scientific and data-intensive workloads.