  New developments in HPC technology in terms of increasing computing power on
multi/many core processors, high-bandwidth memory/IO subsystems and
communication interconnects, pose a direct impact on software and runtime
system development. These advancements have become useful in producing
high-performance collective communication interfaces that integrate efficiently
on a wide variety of platforms and environments. However, number of
optimization options that shows up with each new technology or software
framework has resulted in a \emph{combinatorial explosion} in feature space for
tuning collective parameters such that finding the optimal set has become a
nearly impossible task. Applicability of algorithmic choices available for
optimizing collective communication depends largely on the scalability
requirement for a particular usecase. This problem can be further exasperated
by any requirement to run collective problems at very large scales such as in
the case of exascale computing, at which impractical tuning by brute force may
require many months of resources. Therefore application of statistical, data
mining and artificial Intelligence or more general hybrid learning models seems
essential in many collectives parameter optimization problems. We hope to
explore current and the cutting edge of collective communication optimization
and tuning methods and culminate with possible future directions towards this
problem.
