  This paper investigates co-scheduling algorithms for processing a set of
parallel applications. Instead of executing each application one by one, using
a maximum degree of parallelism for each of them, we aim at scheduling several
applications concurrently. We partition the original application set into a
series of packs, which are executed one by one. A pack comprises several
applications, each of them with an assigned number of processors, with the
constraint that the total number of processors assigned within a pack does not
exceed the maximum number of available processors. The objective is to
determine a partition into packs, and an assignment of processors to
applications, that minimize the sum of the execution times of the packs. We
thoroughly study the complexity of this optimization problem, and propose
several heuristics that exhibit very good performance on a variety of
workloads, whose application execution times model profiles of parallel
scientific codes. We show that co-scheduling leads to to faster workload
completion time and to faster response times on average (hence increasing
system throughput and saving energy), for significant benefits over traditional
scheduling from both the user and system perspectives.
