Title: CODA: Enabling Co-location of Computation and Data for Near-Data Processing,
Abstract: Recent studies have demonstrated that near-data processing (NDP) is an
effective technique for improving performance and energy efficiency of
data-intensive workloads. However, leveraging NDP in realistic systems with
multiple memory modules introduces a new challenge. In today's systems, where
no computation occurs in memory modules, the physical address space is
interleaved at a fine granularity among all memory modules to help improve the
utilization of processor-memory interfaces by distributing the memory traffic.
However, this is at odds with efficient use of NDP, which requires careful
placement of data in memory modules such that near-data computations and their
exclusively used data can be localized in individual memory modules, while
distributing shared data among memory modules to reduce hotspots. In order to
address this new challenge, we propose a set of techniques that (1) enable
collections of OS pages to either be fine-grain interleaved among memory
modules (as is done today) or to be placed contiguously on individual memory
modules (as is desirable for NDP private data), and (2) decide whether to
localize or distribute each memory object based on its anticipated access
pattern and steer computations to the memory where the data they access is
located. Our evaluations across a wide range of workloads show that the
proposed mechanism improves performance by 31% and reduces 38% remote data
accesses over a baseline system that cannot exploit computate-data affinity
characteristics.