  The development of an LHC physics analysis involves numerous investigations
that require the repeated processing of terabytes of data. Thus, a rapid
completion of each of these analysis cycles is central to mastering the science
project. We present a solution to efficiently handle and accelerate physics
analyses on small-size institute clusters. Our solution is based on three key
concepts: Vectorized processing of collision events, the "MapReduce" paradigm
for scaling out on computing clusters, and efficiently utilized SSD caching to
reduce latencies in IO operations. Using simulations from a Higgs pair
production physics analysis as an example, we achieve an improvement factor of
$6.3$ in runtime after one cycle and even an overall speedup of a factor of
$14.9$ after $10$ cycles.
