Molecular docking with Raccoon2 on clouds: extending desktop applications with cloud computing

Molecular docking is a computer simulation that predicts the binding affinity between two molecules, a ligand and a receptor. Large-scale docking simulations, using one receptor and many ligands, are known as structure-based virtual screening. Often used in drug discovery, virtual screening can be very computationally demanding. This is why user-friendly domain-specific web or desktop applications that enable running simulations on powerful computing infrastructures have been created. Cloud computing provides on-demand availability, pay-per-use pricing, and great scalability which can improve the performance and efficiency of scientific applications. This paper investigates how domain-specific desktop applications can be extended to run scientific simulations on various clouds. A generic approach based on scientific workflows is proposed, and a proof of concept is implemented using the Raccoon2 desktop application for virtual screening, WS-PGRADE workflows, and gUSE services with the CloudBroker platform. The presented analysis illustrates that this approach of extending a domain-specific desktop application can run workflows on different types of clouds, and indeed makes use of the on-demand scalability provided by cloud computing. It also facilitates the execution of virtual screening simulations by life scientists without requiring them to abandon their favourite desktop environment and providing them resources without major capital investment

Supporting environmental modelling with Taverna workflows, web services and desktop grid technology

Ecosystem functioning, climate change, and multiple interactions among biogeochemical cycles, climate system, site conditions and land use options are leading-edge topics in recent environmental modelling. Terrestrial ecosystem models are widely used to support carbon sequestration and ecosystem studies under various ecological circumstances. Our team uses the Biome-BGC model (Numerical Terradynamic Simulation Group, University of Montana), and develops an improved model version of it, called Biome-BGC MuSo. Both the original and the improved model estimate the ecosystem scale storage and fluxes of energy, carbon, nitrogen and water, controlled by various physical and biological processes on a daily time-scale. Web services were also developed and integrated with parallel processing desktop grid technology. Taverna workflow management system was used to build up and carry out elaborated workflows like seamless data flow to model simulation, Monte Carlo experiment, model sensitivity analysis, model-data fusion, estimation of ecosystem service indicators or extensive spatial modelling. Straightforward management of complex data analysis tasks, organized into appropriately documented, shared and reusable scientific workflows enables researchers to carry out detailed and scientifically challenging ‘in silico’ experiments and applications that could open new directions in ecosystem research and in a broader sense it supports progress in environmental modelling. The workflow approach built upon these web services allows even the most complicated computations to be initiated without the need of programming skills and deep understanding of model structure and initialization. The developments enable a wider array of scientists to perform ecosystem scale simulations, and to perform analyses not previously possible due to high complexity and computational demand

From Quantity to Quality: Massive Molecular Dynamics Simulation of Nanostructures under Plastic Deformation in Desktop and Service Grid Distributed Computing Infrastructure

The distributed computing infrastructure (DCI) on the basis of BOINC and EDGeS-bridge technologies for high-performance distributed computing is used for porting the sequential molecular dynamics (MD) application to its parallel version for DCI with Desktop Grids (DGs) and Service Grids (SGs). The actual metrics of the working DG-SG DCI were measured, and the normal distribution of host performances, and signs of log-normal distributions of other characteristics (CPUs, RAM, and HDD per host) were found. The practical feasibility and high efficiency of the MD simulations on the basis of DG-SG DCI were demonstrated during the experiment with the massive MD simulations for the large quantity of aluminum nanocrystals ($\sim10^2$-$10^3$). Statistical analysis (Kolmogorov-Smirnov test, moment analysis, and bootstrapping analysis) of the defect density distribution over the ensemble of nanocrystals had shown that change of plastic deformation mode is followed by the qualitative change of defect density distribution type over ensemble of nanocrystals. Some limitations (fluctuating performance, unpredictable availability of resources, etc.) of the typical DG-SG DCI were outlined, and some advantages (high efficiency, high speedup, and low cost) were demonstrated. Deploying on DG DCI allows to get new scientific $\it{quality}$ from the simulated $\it{quantity}$ of numerous configurations by harnessing sufficient computational power to undertake MD simulations in a wider range of physical parameters (configurations) in a much shorter timeframe.Comment: 13 pages, 11 pages (http://journals.agh.edu.pl/csci/article/view/106

Cube-and-Conquer approach for SAT solving on grids

Our goal is to develop techniques for using distributed computing re- sources to efficiently solve instances of the propositional satisfiability problem (SAT). We claim that computational grids provide a distributed computing environment suitable for SAT solving. In this paper we apply the Cube and Conquer approach to SAT solving on grids and present our parallel SAT solver CCGrid (Cube and Conquer on Grid) on computational grid infrastructure. Our solver consists of two major components. The master application runs march_cc, which applies a lookahead SAT solver, in order to partition the input SAT instance into work units distributed on the grid. The client application executes an iLingeling instance, which is a multi-threaded CDCL SAT solver. We use BOINC middleware, which is part of the SZTAKI Desktop Grid package and supports the Distributed Computing Application Programming Interface (DC-API). Our preliminary results suggest that our approach can gain significant speedup and shows a potential for future investigation and development. Keywords: grid, SAT, parallel SAT solving, lookahead, march_cc, iLingeling, SZTAKI Desktop Grid, BOINC, DC-AP

Exploring the E-science Knowledge Base through co-citation analysis

E-Science is the “science of this age”; it is realized through collaborative scientific enquiry which requires utilization of non-trivial amounts of computing resources and massive data sets. In this paper we explore the e-Science knowledge base through co-citation analysis of extant literature. Our objective is to use the knowledge domain visualization software CiteSpace to identifying the turning point articles and authors. In other words, our analysis is not solely based on tabulating the frequency of co-cited articles and authors, but the identification of landmark articles and authors irrespective of their co-citation count. The dataset for this analysis is downloaded from the ISI Web of Science and includes approx. 1000 articles. It is expected that this paper will be an important source of reference for academics and researchers working in the area of e-Science and its three technology enablers - grid computing, desktop grids and cloud computing

Numerical computing of extremely large values of the Riemann-Siegel Z-function

A PhD értekezés egy olyan hatékony algoritmust mutat be, amely a Riemann-Siegel Z-függvény kiugró értékeinek meghatározására szolgál. A Riemann-féle zeta függvény nagyon fontos szerepet játszik a matematika és a fizika különböző területein. A zeta függvény kritikus egyenesen elhelyezkedő nagy értékeinek meghatározása hozzásegíthet minket a prímszámok eloszlásának sokkal jobb megértéséhez. A doktori értekezés első részében egy olyan algoritmust készítettünk, amelynek segítségével gyorsan és hatékonyan tudjuk a Riemann-Siegel-Z függvényben szereplő többváltozós függvényt közelíteni nagyon sok n egészre. Módszerünk többdimenziós szimultán Diofantikus egyenletek approximációján alapul, melynek megoldására hatékony algoritmust mutattunk be (MAFRA algoritmus). Ezt az algoritmust felhasználva kidolgoztunk egy új algoritmust (RS-PEAK), amelynek segítségével gyorsan és hatékonyan lehet meghatározni a Riemann-féle zeta függvény kritikus egyenesen elhelyezkedő kiugró értékeit. Az RS-PEAK algoritmus segítségével az MTA SZTAKI Desktop GRID hálózatát felhasználva sikerült nagyon nagy Z(t) értékeket publikálni, köztük a ma ismert legnagyobbat is, ahol t=310678833629083965667540576593682.05-ra a Z(t) =16874.202 értéket kapjuk. A disszertáció írásának időpontjában ez a legnagyobb publikált Z(t) érték. A doktori értekezésben több a Z(t) értékhez kapcsolódó számítási rekordot publikáltunk

Co-citation analysis of literature in e-science and e-infrastructures

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this recordAdvances in computer networking, storage technologies and high-performance computing are helping global communities of researchers to address increasingly ambitious problems in Science collaboratively. EScience is the “science of this age”; it is realized through collaborative scientific enquiry which requires the utilization of non-trivial amounts of computing resources and massive data sets. Core to this is the integrated set of technologies collectively known as e-Infrastructures. In this paper, we explore the e-Science and the eInfrastructure knowledge base through co-citation analysis of existing literature. The dataset for this analysis is downloaded from the ISI Web of Science and includes over 12,000 articles. We identify prominent articles, authors and articles with citation bursts. The detection of research clusters and the underlying seminal papers provide further insights. Our analysis is an important source of reference for academics, researchers and students starting research in this field