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High performance computing in multi-scale modeling, graph science and meta-heuristic optimization
aUniversity of Kragujevac, Faculty of Science
bUniversity of Kragujevac, Faculty of Economy
cDepartment of Chemistry and Chemical Biology, College of Science, Northeastern University, Boston, MA, SAD
Application of biomedical engineering for preclinical and clinical practice (MESTD - 41007)
Multiscale Methods and Their Applicatios in Nanomedicine (MESTD - 174028)

One of the main activities within the Group for Scientific Computing at the Faculty of Science are methods for efficiently utilizing real parallel architectures, typically clusters of SMP nodes, shared-memory systems, and GPUs. Focus is on the design, development and implementation of parallel algorithms and data structures for fundamental scientific and engineering problems. Message Passing Interface (MPI) is an important paradigm that still poses interesting design and implementation problems, especially combined with other programming models, like CUDA. In addition to standard HPC (High Performance Computing) technology stack, the Group also utilizes computing stacks like Hadoop and Spark. In this paper we present a short review of the recent research of the Group, focused on large-scale applications in various research fields with references to original articles. The first part considers multi-scale muscle modeling in mixed MPI-CUDA environment. In our approach, a finite element macro model is coupled with the microscopic Huxley kinetics model. The original approach in scheduling tasks within multi-scale simulation ensures good load balance, leading to speed-up of over two orders of magnitude and high scalability. The second part considers application of HPC in graph science for the task of establishing the basic structural features of the minimum-ABC index trees. In order to analyze large amounts of data (all trees of certain order) we utilize grid computing services like storage and computing in order to reduce analysis time up to three orders of magnitude. The last part presents WoBinGO framework for solving optimization problems on HPC resources. It overcomes the shortcomings of earlier static pilot-job frameworks by providing elastic resource provisioning using adaptive allocation of jobs with limited lifetime. The obtained results show that despite WoBinGO's adaptive and frugal allocation of computing resources, it provides significant speed-up when dealing with problems with computationally expensive evaluations, as found in hydro-informatics and market risk management.
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article language: English
document type: unclassified
DOI: 10.5937/jsscm1601050I
published in SCIndeks: 25/03/2017

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