  Providing architectural support is crucial for newly arising applications to
achieve high performance and high system efficiency. Currently there is a trend
in designing accelerators for special applications, while arguably a debate is
sparked whether we should customize architecture for each application. In this
study, we introduce what we refer to as Gene-Patterns, which are the base
patterns of diverse applications. We present a Recursive Reduce methodology to
identify the hotspots, and a HOtspot Trace Suite (HOTS) is provided for the
research community. We first extract the hotspot patterns, and then, remove the
redundancy to obtain the base patterns. We find that although the number of
applications is huge and ever-increasing, the amount of base patterns is
relatively small, due to the similarity among the patterns of diverse
applications. The similarity stems not only from the algorithms but also from
the data structures. We build the Periodic Table of Memory Access Patterns
(PT-MAP), where the indifference curves are analogous to the energy levels in
physics, and memory performance optimization is essentially an energy level
transition. We find that inefficiency results from the mismatch between some of
the base patterns and the micro-architecture of modern processors. We have
identified the key micro-architecture demands of the base patterns. The
Gene-Pattern concept, methodology, and toolkit will facilitate the design of
both hardware and software for the matching between architectures and
applications.
