Special issue on high performance computing in modeling and simulation

In the pure technological era we are living, the need for appropriate tools, methods, and approaches that could boost and skyrocket real world various applications is of paramount importance even for daily life. Toward this direction, in the up-to-date literature, several computational tools are offered, new advanced nearly real-time performing techniques are introduced, almost every day, and powerful computing approaches are promising to tackle the issues of performance, energy efficiency, and computational burden, with many different fruitful ways. Nevertheless, most of these demands, trends, and perspectives would have never met the expected outcome without the help of modern high performance computing systems able to model and simulate computationally intensive scientific applications in the most efficient and appropriate way. Consequently, numerous and various high performance computing approaches like multi-/manycore systems, accelerators, compute clusters, and massively parallel machines, when combined with efficient numerical methods for differential equation systems and native computational paradigms, enable scientists and researchers worldwide to significantly advance the application of computing methodologies in research and industry applications, both in qualitative but mainly in quantitative way. In this aspect, this Special Issue aimed to offer both scientists and engineers in academy and industry an opportunity to express and discuss their views on current trends, challenges, and state-of-the art solutions to various problems in High Performance Computing for Modeling and Simulation. Moreover, it was highly related to the corresponding Special Session on High Performance Computing in Modeling and Simulation (HPCMS), within the 23rd Euromicro International Conference on Parallel, Distributed and network-based Processing (PDP), held in Turku, Finland on March 4-6, 2015, and its relevant topics. Eventually, a major part of these topics is covered by the content of the fore-coming Special Issue of Concurrency and Computation: Practice and Experience through eight (8) finally selected papers, all thoroughly reviewed and revised properly as a detailed major extension of their conference papers earlier published in the PDP 2015 proceedings. More specifically, in this Special Issue, both theoretical aspects of high performance computing systems, like libraries for the reduction of the programming burden of numerical models on heterogeneous parallel architectures, hybrid programming model MPI/OpenMP for tackling the communication load imbalance issues, and applications starting with parallel shared-memory version of the Space Saving algorithm for mining items, approximate and semi-asynchronous parallel model for supporting Parallel and Discrete Event Simulation, parallel execution pipeline of an existing description algorithm capable of characterizing both color and texture information of a given feature point for robotic visual place recognition, and parallel and hardware acceleration of detection of ambiguous objects for surveillance reasons, as well as optimization techniques to be parallelized such as Imperialist Competitive Algorithm, are fully considered in a fruitful and plausible way. In more details, the article by Chakroun et al1 described ExaShark,2 an open source library with the aim of reducing the programming burden of numerical models on heterogeneous parallel architectures. The presented library offers a global-array-like interface, whereas its run-time can be configured to use shared memory threading techniques, inter-node distribution techniques, or combinations of both. ExaShark takes advantage of the latest HPC technologies, helping to scale to future generation systems. The article demonstrates the usefulness of the ExaShark library through several experiments, including stencil codes, solvers, and matrix factorization algorithms. Utrera and co-authors3 analyzed a significant problem in the field of HPC applied to modeling and simulation, that is, the communication load imbalance generated by irregular-data applications running in a multi-node cluster. The study targets, in particular, a hybrid programming model MPI+OpenMP, where several approaches to diminish communication load imbalance are adopted, like computation-communication overlap, issuing communications in parallel, and a new approach based on message fragmentation in order to take advantage of the eager-protocol. The article includes a number of interesting results of experiments, in which the performance of overlapped and non-overlapped approaches are quantified, including the impact due to network latency. The article by Majd et al4 concerned another relevant aspect often involved in modeling and simulation, that is, optimization. In particular, the authors focus their work on parallelizing a relatively new evolutionary optimization approach, namely, the Imperialist Competitive Algorithm.5 The proposed parallelizations include a master-slave version and a more sophisticated multi-population strategy, both exploiting the well-known Message Passing Interface. The article describes a variety of experiments and comparisons, based on two different computing platforms, and proved that the developed parallel algorithms can achieve significant performances in both optimization and speed of execution. Rousset et al6 presented nested graphs as an approach to model parallel and distributed multi-agent simulations aiming at facilitating the dynamic distribution of computations among parallel machines. The aforementioned task is successfully achieved due to finer granularity on multiple levels of abstraction. In the proposed approach, a common and generic framework, which represents the agent models, as well as their distribution, is efficiently presented. In addition, the proposed PDMAS framework includes a more graphical method to model parallel and distributed multi-agent