  Data movement between the CPU and main memory is a first-order obstacle
against improving performance, scalability, and energy efficiency in modern
systems. Computer systems employ a range of techniques to reduce overheads tied
to data movement, spanning from traditional mechanisms (e.g., deep multi-level
cache hierarchies, aggressive hardware prefetchers) to emerging techniques such
as Near-Data Processing (NDP), where some computation is moved close to memory.
Our goal is to methodically identify potential sources of data movement over a
broad set of applications and to comprehensively compare traditional
compute-centric data movement mitigation techniques to more memory-centric
techniques, thereby developing a rigorous understanding of the best techniques
to mitigate each source of data movement.
  With this goal in mind, we perform the first large-scale characterization of
a wide variety of applications, across a wide range of application domains, to
identify fundamental program properties that lead to data movement to/from main
memory. We develop the first systematic methodology to classify applications
based on the sources contributing to data movement bottlenecks. From our
large-scale characterization of 77K functions across 345 applications, we
select 144 functions to form the first open-source benchmark suite (DAMOV) for
main memory data movement studies. We select a diverse range of functions that
(1) represent different types of data movement bottlenecks, and (2) come from a
wide range of application domains. Using NDP as a case study, we identify new
insights about the different data movement bottlenecks and use these insights
to determine the most suitable data movement mitigation mechanism for a
particular application. We open-source DAMOV and the complete source code for
our new characterization methodology at https://github.com/CMU-SAFARI/DAMOV.
