In this chapter, we provide an overview of the current state of the art with respect to solution
of mixed integer linear optimization problems (MILPs) in parallel. Sequential algorithms for …

Communication between tasks and load imbalance have been identified as a major
challenge for the performance and energy efficiency of parallel applications. A common way …

Distributed Exascale computing systems are considered as a specific type of system that is
capable of running dynamic and interactive applications. The existence of a dynamic and …

Process mapping (or process placement) is a useful algorithmic technique to optimize the
way applications are launched and executed onto a parallel machine. By taking into account …

The dynamic and interactive nature of Distributed Exascale Computing System leads to a
situation where the load balancer lacks the proper pattern for the solution. In addition to …

New generation application problems in big data and high‐performance computing (HPC)
areas claim very diverse operational properties. The convergence requires the dynamic …

Effectively mapping tasks of High Performance Computing (HPC) applications on parallel
systems is crucial to assure substantial performance gains. As platforms and applications …

In this article, we introduce parallel mixed integer linear programming (MILP) solvers. MILP
solving algorithms have been improved tremendously in the last two decades. Currently …

In distributed Exascale systems, the occurrence of a dynamic and interactive nature changes
the workload of the system's computing elements. Because of this, the load balancer needs …

(English) The dominant programming approach for scientific and industrial computing on
clusters is MPI+ X. While there are a variety of approaches within the node, denoted by …