A Programming System for Parallel Execution of Fortran Subprograms in Distributed Environment

2005 ◽  
pp. 221-236
Author(s):  
Khaled M. Ben Hamed ◽  
Weichang Du
Keyword(s):  
Parallel Execution ◽  
Programming System ◽  
Distributed Environment

Cloud-BS: A MapReduce-based bisulfite sequencing aligner on cloud

2018 ◽  
Vol 16 (06) ◽  
pp. 1840028 ◽  
Author(s):  
Joungmin Choi ◽  
Yoonjae Park ◽  
Sun Kim ◽  
Heejoon Chae
Keyword(s):  
Large Scale ◽  
Bisulfite Sequencing ◽  
Programming Model ◽  
Parallel Execution ◽  
Distributed Environment ◽  
Absolute Level ◽  
Bisulfite Treatment ◽  
Genome Wide ◽  
A Genome ◽  
Nucleotide Resolution

In recent years, there have been many studies utilizing DNA methylome data to answer fundamental biological questions. Bisulfite sequencing (BS-seq) has enabled measurement of a genome-wide absolute level of DNA methylation at single-nucleotide resolution. However, due to the ambiguity introduced by bisulfite-treatment, the aligning process especially in large-scale epigenetic research is still considered a huge burden. We present Cloud-BS, an efficient BS-seq aligner designed for parallel execution on a distributed environment. Utilizing Apache Hadoop framework, Cloud-BS splits sequencing reads into multiple blocks and transfers them to distributed nodes. By designing each aligning procedure into separate map and reducing tasks while an internal key-value structure is optimized based on the MapReduce programming model, the algorithm significantly improves alignment performance without sacrificing mapping accuracy. In addition, Cloud-BS minimizes the innate burden of configuring a distributed environment by providing a pre-configured cloud image. Cloud-BS shows significantly improved bisulfite alignment performance compared to other existing BS-seq aligners. We believe our algorithm facilitates large-scale methylome data analysis. The algorithm is freely available at https://paryoja.github.io/Cloud-BS/ .

scholarly journals Towards Automatic Deductive Verification of C Programs with Sisal Loops Using the C-lightVer System

2021 ◽  
Vol 28 (4) ◽  
pp. 372-393
Author(s):  
Dmitry A. Kondratyev
Keyword(s):  
Automatic Parallelization ◽  
Parallel Execution ◽  
Programming System ◽  
Inference Rules ◽  
Deductive Verification ◽  
Recursive Functions ◽  
Axiomatic Semantics ◽  
Verification Conditions ◽  
Symbolic Method ◽  
Verification Condition

The C-lightVer system is developed in IIS SB RAS for C-program deductive verification. C-kernel is an intermediate verification language in this system. Cloud parallel programming system (CPPS) is also developed in IIS SB RAS. Cloud Sisal is an input language of CPPS. The main feature of CPPS is implicit parallel execution based on automatic parallelization of Cloud Sisal loops. Cloud-Sisal-kernel is an intermediate verification language in the CPPS system. Our goal is automatic parallelization of such a superset of C that allows implementing automatic verification. Our solution is such a superset of C-kernel as C-Sisal-kernel. The first result presented in this paper is an extension of C-kernel by Cloud-Sisal-kernel loops. We have obtained the C-Sisal-kernel language. The second result is an extension of C-kernel axiomatic semantics by inference rule for Cloud-Sisal-kernel loops. The paper also presents our approach to the problem of deductive verification automation in the case of finite iterations over data structures. This kind of loops is referred to as definite iterations. Our solution is a composition of symbolic method of verification of definite iterations, verification condition metageneration and mixed axiomatic semantics method. Symbolic method of verification of definite iterations allows defining inference rules for these loops without invariants. Symbolic replacement of definite iterations by recursive functions is the base of this method. Obtained verification conditions with applications of recursive functions correspond to logical base of ACL2 prover. We use ACL2 system based on computable recursive functions. Verification condition metageneration allows simplifying implementation of new inference rules in a verification system. The use of mixed axiomatic semantics results to simpler verification conditions in some cases.

FUNCTIONAL COORDINATION OF METHODS OF ORGANIZATION OF ADAPTIVE DATA COLLECTION PROCESSES AND METHODS OF SPATIAL SELF-ORGANIZATION OF MOBILE AGENTS

2017 ◽  
Vol 2 (1) ◽  
pp. 27-32
Author(s):  
Botchkaryov. A. ◽  
Keyword(s):  
Data Collection ◽  
Key Words ◽  
Motion Control ◽  
Mobile Agent ◽  
Mobile Agents ◽  
Parallel Execution ◽  
Self Organization ◽  
Data Collection Process ◽  
Speed Up ◽  
Adaptive Data Collection

The way of functional coordination of methods of organization adaptive data collection processes and methods of spatial self-organization of mobile agents by parallel execution of the corresponding data collection processes and the process of motion control of a mobile agent using the proposed protocol of their interaction and the algorithm of parallel execution planning is proposed. The method allows to speed up the calculations in the decision block of the mobile agent by an average of 40.6%. Key words: functional coordination, adaptive data collection process, spatial self-organization, mobile agents

Load Following Function of Fuel Cell Plant in Distributed Environment

2005 ◽  
Vol 1 (03) ◽  
pp. 285-290 ◽  
Author(s):  
F. González-Longatt ◽  
◽  
A. Hernandez ◽  
F. Guillen ◽  
C. Fortoul
Keyword(s):  
Fuel Cell ◽  
Distributed Environment ◽  
Load Following

The Design of General Off-Line Graphical Robot Programming System

2017 ◽  
Vol 9 (4) ◽  
pp. 343-352
Author(s):  
Zheng Zhang ◽  
Yonggang Peng ◽  
Yuhui Li
Keyword(s):  
Robot Programming ◽  
Programming System

Big Data Privacy Preservation Using Two Phase Top-Down Specialization Algorithm with Multidimensional Map Reduce Framework on Hadoop

2015 ◽  
Vol 3 (2) ◽  
Author(s):  
Shalin Eliabeth S. ◽  
Sarju S.
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Experimental Result ◽  
Map Reduce ◽  
Distributed Environment ◽  
Top Down ◽  
Two Phase ◽  
Data Anonymization ◽  
Big Data Privacy

Big data privacy preservation is one of the most disturbed issues in current industry. Sometimes the data privacy problems never identified when input data is published on cloud environment. Data privacy preservation in hadoop deals in hiding and publishing input dataset to the distributed environment. In this paper investigate the problem of big data anonymization for privacy preservation from the perspectives of scalability and time factor etc. At present, many cloud applications with big data anonymization faces the same kind of problems. For recovering this kind of problems, here introduced a data anonymization algorithm called Two Phase Top-Down Specialization (TPTDS) algorithm that is implemented in hadoop. For the data anonymization-45,222 records of adults information with 15 attribute values was taken as the input big data. With the help of multidimensional anonymization in map reduce framework, here implemented proposed Two-Phase Top-Down Specialization anonymization algorithm in hadoop and it will increases the efficiency on the big data processing system. By conducting experiment in both one dimensional and multidimensional map reduce framework with Two Phase Top-Down Specialization algorithm on hadoop, the better result shown in multidimensional anonymization on input adult dataset. Data sets is generalized in a top-down manner and the better result was shown in multidimensional map reduce framework by the better IGPL values generated by the algorithm. The anonymization was performed with specialization operation on taxonomy tree. The experiment shows that the solutions improves the IGPL values, anonymity parameter and decreases the execution time of big data privacy preservation by compared to the existing algorithm. This experimental result will leads to great application to the distributed environment.

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