Optimizing the performance of optimization in the cloud environment–An intelligent auto-scaling approach

The cloud computing paradigm has gained wide acceptance in the scientific community, taking a significant share from fields previously reserved exclusively for High Performance Computing (HPC). On-demand access to a large amount of computing resources provided by Cloud makes it ideal for executing large-scale optimizations using evolutionary algorithms without the need for owning any computing infrastructure. In this regard, we extended WoBinGO, an existing parallel software framework for genetic algorithm based optimization, to be used in Cloud. With these extensions, the framework is capable of elastically and frugally utilizing the underlying cloud computing infrastructure for performing computationally expensive fitness evaluations. We studied two issues that are pertinent when dealing with large-scale optimization in the elastic cloud environment: the computing instance launching overhead and the price of engaging Cloud for solving optimization problems, in terms of the instances’ cumulative uptime. To explain the usability limits of WoBinGO framework running in the IaaS environment, a comprehensive analysis of the framework’s performance was given. Optimization of both total optimization time and total cumulative uptime, leads to minimizing the cost of cloud resources utilization. In this way, we are proposing an intelligent decision support engine based on artificial neural networks and metaheuristics to provide the user with an assessment of the framework’s behavior on the underlying infrastructure in terms of optimization duration and the cost of resource consumption. According to a given assessment, the user can decide upon faster delivery of results or lower infrastructure costs. The proposed software framework has been used to solve a complex real-world optimization problem of a subsurface rock mass model calibration. The results obtained from the private OpenStack deployment show that by using the proposed decision support engine, significant savings can be achieved in both optimization time and optimization cost

Design and Comparison of Two Web Service Based Frameworks for Parallel Evaluation of the Population in Genetic Algorithms

Genetic algorithms are powerful techniques for optimization of complexsystems. These methods require a large number of evaluations of candidate solutionswhich take huge CPU time. This paper introduces two web service based frameworksfor parallel evaluation of the population in genetic algorithm using the master-slavemodel. Developed frameworks can be easily incorporated into any genetic algorithm,giving a universal mechanism for distribution of individuals and collection of the eval-uation results. This concept provides parallelization of genetic algorithms on variousdistributed architectures, including multiprocessors and computing clusters. Performedtests have shown that proposed frameworks achieve signicant speedup, especially whenevaluating large-scale problems. In addition, a case study from the eld of hydrologyis presented

Elastic grid resource provisioning with WoBinGO: A parallel framework for genetic algorithm based optimization

In this paper, we present the WoBinGO (Work Binder Genetic algorithm based Optimization) framework for solving optimization problems over a Grid. It overcomes the shortcomings of earlier static pilot-job frameworks, by: (1) providing elastic resource provisioning thus avoiding unnecessary occupation of Grid resources; (2) providing friendliness towards other batching queue users thanks to adaptive allocation of jobs with limited lifetime. It hides the complexity of the underlying Grid environment, allowing the users to concentrate on the optimization problems. Theoretical analysis of possible speed-up is presented. An empirical study using an artificial problem, as well as a real-world calibration problem of a leakage model at the Visegrad power plant were performed. The obtained results show that despite WoBinGO’s adaptive and frugal allocation of computing resources, it provides significant speed-up when dealing with problems that have computationally expensive evaluations. Moreover, the benchmarks were performed in order to estimate the influence of the limited job lifetime feature on the queuing time of other batching jobs, compared to a static pilot-job infrastructure.Author's versio

A 16-year-old Adolescent With Mediastinal Seminoma: A Case Report and Literature Review

Germ cell tumors (GCTs) are a heterogeneous group of neoplasms that arise from the primordial germ cells of the human embryo, which are normally destined to produce reproductive cells sperm, or ova. GCTs can be present in both gonadal GCTs and extragonadal GCT sites. Pediatric GCTs are relatively rare tumors with an incidence of 2%-3%. Primary mediastinal germ cell tumors GCTs are very rare extragonadal GCTs that arise in the anterior mediastinum. In this report, we present the case of a 16-year-old boy with primary seminoma arising in the anterior mediastinum. The patient presented with the symptoms of cough, fever, and chest tightness. CT finding was in favor of a large expansive process measuring 12.4x6.7x14.2 cm in the anterior mediastinum, accompanied by a conglomeration of hilar lymph nodes in the level of brachiocephalic veins juncture. Fine needle biopsy and core biopsy were performed transthoracically, under the control of MSCT. Based on histology and immunohistochemistry, the diagnosis of mediastinal germ cell tumor with immunophenotype of seminoma was made. The patient was treated with 4 cycles of chemotherapy by BEP protocol without significant side effects and toxicities. The patient remained disease-free for 16 months. The purpose of reporting this case is to confirm that chemotherapy with cisplatin-based regimens has markedly improved the outcome of adults and children with GCTs as well

High performance computing in multi-scale modeling, graph science and meta-heuristic optimization

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 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 utilize 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, 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

The Pharmacokinetics of Recombinant Human Erythropoietin in Balkan Endemic Nephropathy Patients

ABSTRACT Background: Balkan endemic nephropathy (BEN) hemodialysis patients require a higher dose of recombinant human erythropoietin for maintaining target hemoglobin level than patients with other kidney diseases. Objectives: Comparison of the pharmacokinetics of betaerythropoietin given subcutaneously to hemodialysis patients with BEN or other kidney diseases (non-BEN). Methods: Recombinant human erythropoietin (75U/kg) was administered subcutaneously to 10 BEN and 14 non-BEN hemodialysis patients. The predose plasma level of erythropoietin (Epo) was subtracted from all postdose levels. The relevant pharmacokinetic parameters were calculated after noncompartmental pharmacokinetic analysis using Kinetica software (Thermo Scientific, ver.5.0). Results: Although basal plasma Epo concentration was similar in BEN (20.1±10.3U/L) and non-BEN (15.1±8.1U/L; p=.1964) patients, there were significant differences between the groups for elimination rate constant (0.016±0.006 vs 0.026±0.011 hr -1 ; p=.020) and elimination half-life (50.24±19.12 vs 33.79±18.91 hr, p=.048). These differences remained significant after adjustment for patient characteristics (age, sex

Multi-modeling and multi-scale modeling as tools for solving complex realworld problems

In previous decades a number of computational methods for calculation of very complex physical phenomena with a satisfactory accuracy have been developed. Most of these methods usually model only a single physical phenomenon, while their performance regarding accuracy and efficiency are limited within narrow spatial and temporal domains. However, solving realworld problems often requires simultaneous analysis of several coupled physical phenomena that extend over few spatial and temporal scales. Thus, in the last decade, simultaneous modeling a number of physical phenomena (multi-modeling) and modeling across few scales (multi-scale modeling) have gained a huge importance. In this paper we give an overview of multi-modeling and multi-scale methods developed during the last decade within the Group for Scientific Computing at Faculty of Science, University of Kragujevac. In addition, we give a short review of accompanying problems that we had to solve in order to make the methods applicable in practice, such as parallelization of computations, parameters calibration, etc. In the first part of the paper we present methods for modeling various aspects of muscle behavior and their coupling into complex multi-models. The mechanical behavior of muscles is derived from the behavior of many individual components working together across spatial and temporal scales. Capturing the interplay between these components resulted in efficient multiscale model. The rest of the paper is reserved for the presentation of multi-models for solving real-world problems in the field of water resources management, as well as methods for calibration of complex models parameters. As most illustrative example, we present methodology for solving the problem of water leakage under Visegrad dam at Drina River in Republic of Srpska. With the aim to support decision making process during dam remediation, we have developed specialized multi-model that continuously uses acquired observations to estimate spatial distribution of main karst conductors, their characteristics, as well as hydraulic variables of the system