  The collective operations are considered critical for improving the
performance of exascale-ready and high-performance computing applications. On
this paper we focus on the Message-Passing Interface (MPI) Allgather many to
many collective, which is amongst the most called and time-consuming
operations. Each MPI algorithm for this call suffers from different operational
and performance limitations, that might include only working for restricted
cases, requiring linear amounts of communication steps with the growth in
number of processes, memory copies and shifts to assure correct data
organization, and non-local data exchange patterns, most of which negatively
contribute to the total operation time. All these characteristics create an
environment where there is no algorithm which is the best for all cases and
this consequently implies that careful choices of alternatives must be made to
execute the call. Considering such aspects, we propose the Stripe Parallel
Binomial Trees (Sparbit) algorithm, which has optimal latency and bandwidth
time costs with no usage restrictions. It also maintains a much more local
communication pattern that minimizes the delays due to long range exchanges,
allowing the extraction of more performance from current systems when compared
with asymptotically equivalent alternatives. On its best scenario, Sparbit
surpassed the traditional MPI algorithms on 46.43% of test cases with mean
(median) improvements of 34.7% (26.16%) and highest reaching 84.16%.
