REMAP: Remote mEmory Manager for disAggregated Platforms

Abstract

Disaggregated computing is a new approach that promises to alleviate the problem of fixed resource proportionality in datacenter deployments. Two critical factors that affect the overall performance of disaggregated platforms are remote memory access latency and throughput. Previous works primarily expose remote data processing at the applcation level that (a) require code annotations and/or the use of custom user-level libraries, and (b) may hinder the overall system protection and functionality. In this paper, we are taking a different approach: we propose the Remote mEmory Manager for disAggregated Platforms (REMAP), a hardware architecture that enables the hotplug of remote memory resources to processing nodes, as normal paged memory at the OS-level, without requiring application-level code modifications. REMAP tightly couples processing nodes with remote memory controllers. Our architecture 'expands' system memory on demand, by dynamically attaching remote memory modules to unused Local Physical Address (LPA) ranges, where the memory access requests are tunneled over high-speed, low-latency serial links. To evaluate REMAP in terms of performance, we implemented a prototype using two zcul02 FPGA boards. REMAP provides a remote cache-line access latency of less than 750 nsec, and up to 1.3x overall system throughput, compared to a baseline CPU-memory configuration.