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Добірка наукової літератури з теми "Grammatical automatic parallel programming"

Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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Статті в журналах з теми "Grammatical automatic parallel programming"

1

Alfonseca, Manuel, and Alfonso Ortega. "Book Review: Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language." Genetic Programming and Evolvable Machines 5, no. 4 (December 2004): 393. http://dx.doi.org/10.1023/b:genp.0000036057.27304.5b.

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2

MORIARTY, K. J. M., and T. TRAPPENBERG. "PROGRAMMING TOOLS FOR PARALLEL COMPUTERS." International Journal of Modern Physics C 04, no. 06 (December 1993): 1285–94. http://dx.doi.org/10.1142/s0129183193001002.

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Although software tools already have a place on serial and vector computers they are becoming increasingly important for parallel computing. Message passing libraries, parallel operating systems and high level parallel languages are the basic software tools necessary to implement a parallel processing program. These tools up to now have been specific to each parallel computer system and a short survey will be given. The aim of another class of software tools for parallel computers is to help in writing or rewriting application programs. Because automatic parallelization tools are not very successful, an interactive component has to be incorporated. We will concentrate here on the discussion of SPEFY, a parallel program development facility.

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3

Park, Ho-Hyun, Alexandre Grings, Marcus Vinicius dos Santos, and Alexsandro Santos Soares. "Parallel hybrid evolutionary computation: Automatic tuning of parameters for parallel gene expression programming." Applied Mathematics and Computation 201, no. 1-2 (July 2008): 108–20. http://dx.doi.org/10.1016/j.amc.2007.12.002.

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4

García, Luis Germán, Emanuel Montoya, Sebastian Isaza, and Ricardo A. Velasquez. "An Open edX Extension for Parallel Programming Assignments with Automatic Configurable Grading." International Journal of Engineering Pedagogy (iJEP) 11, no. 4 (July 9, 2021): 7. http://dx.doi.org/10.3991/ijep.v11i4.20097.

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Computing devices of all types have almost converged to using central processing units featuring multiple processing cores. In order to develop efficient software for such devices, programmers need to learn how to write parallel programs. We present an infrastructure to support parallel programming assignments for online courses. We developed an extension to the Open edX platform with a backend that handles the execution of student codes on a cluster lab. The web user interface offers instructors a wide range of configuration options for the programming assignments as well as a flexible definition of criteria for automatic grading. We have successfully integrated the software with Open edX and tested it with a real parallel programming cluster lab.

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5

Magomedov, Sh G., and A. S. Lebedev. "A tool for automatic parallelization of affine programs for systems with shared and distributed memory." Russian Technological Journal 7, no. 5 (October 15, 2019): 7–19. http://dx.doi.org/10.32362/2500-316x-2019-7-5-7-19.

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Effective programming of parallel architectures has always been a challenge, and it is especially complicated with their modern diversity. The task of automatic parallelization of program code was formulated from the moment of the appearance of the first parallel computers made in Russia (for example, PS2000). To date, programming languages and technologies have been developed that simplify the work of a programmer (T-System, MC#, Erlang, Go, OpenCL), but do not make parallelization automatic. The current situation requires the development of effective programming tools for parallel computing systems. Such tools should support the development of parallel programs for systems with shared and distributed memory. The paper deals with the problem of automatic parallelization of affine programs for such systems. Methods for calculating space-time mappings that optimize the locality of the program are discussed. The implementation of developed methods is done in Haskell within the source-to-source translator performing automatic parallelization. A comparison of the performance of parallel variants of lu, atax, syr2k programs obtained using the developed tool and the modern Pluto tool is made. The experiments were performed on two x86_64 machines connected by the InfiniBand network. OpenMP and MPI were used as parallelization technologies. The performance of the resulting parallel program indicates the practical applicability of the developed tool for affine programs parallelization.

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6

Tsoulos, Ioannis G. "QFC: A Parallel Software Tool for Feature Construction, Based on Grammatical Evolution." Algorithms 15, no. 8 (August 21, 2022): 295. http://dx.doi.org/10.3390/a15080295.

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This paper presents and analyzes a programming tool that implements a method for classification and function regression problems. This method builds new features from existing ones with the assistance of a hybrid algorithm that makes use of artificial neural networks and grammatical evolution. The implemented software exploits modern multi-core computing units for faster execution. The method has been applied to a variety of classification and function regression problems, and an extensive comparison with other methods of computational intelligence is made.

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7

Szymczyk, M., and P. Szymczyk. "Automatic processing of Z-transform artificial neural networks using parallel programming." Neurocomputing 379 (February 2020): 74–88. http://dx.doi.org/10.1016/j.neucom.2019.10.078.

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8

van Reeuwijk, Kees, Arjan J. C. van Gemund, and Henk J. Sips. "Spar: A programming language for semi-automatic compilation of parallel programs." Concurrency: Practice and Experience 9, no. 11 (November 1997): 1193–205. http://dx.doi.org/10.1002/(sici)1096-9128(199711)9:11<1193::aid-cpe334>3.0.co;2-b.

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9

Xia, Tian, Peng Liu, Xiao Yang Jiang, and Ying Huang. "Parametric Design of Parallel Indexing CAM Based on VB." Advanced Materials Research 411 (November 2011): 50–53. http://dx.doi.org/10.4028/www.scientific.net/amr.411.50.

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Through the establishment of mathematical model of parallel indexing cam, a calculation method of the cam contour line is discovered. Then achieve automatic calculation about point in cam profile based on VB programming. Eventually output plots by SolidWorks.

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10

Ierotheou, C. S., S. P. Johnson, P. F. Leggett, M. Cross, E. W. Evans, H. Jin, M. Frumkin, and J. Yan. "The Semi-Automatic Parallelisation of Scientific Application Codes Using a Computer Aided Parallelisation Toolkit." Scientific Programming 9, no. 2-3 (2001): 163–73. http://dx.doi.org/10.1155/2001/327048.

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The shared-memory programming model can be an effective way to achieve parallelism on shared memory parallel computers. Historically however, the lack of a programming standard using directives and the limited scalability have affected its take-up. Recent advances in hardware and software technologies have resulted in improvements to both the performance of parallel programs with compiler directives and the issue of portability with the introduction of OpenMP. In this study, the Computer Aided Parallelisation Toolkit has been extended to automatically generate OpenMP-based parallel programs with nominal user assistance. We categorize the different loop types and show how efficient directives can be placed using the toolkit's in-depth interprocedural analysis. Examples are taken from the NAS parallel benchmarks and a number of real-world application codes. This demonstrates the great potential of using the toolkit to quickly parallelise serial programs as well as the good performance achievable on up to 300 processors for hybrid message passing-directive parallelisations.

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Дисертації з теми "Grammatical automatic parallel programming"

1

Tregidgo, R. W. S. "Parallel processing and automatic postal address recognition." Thesis, University of Essex, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.304946.

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2

Li, Li. "Model-based automatic performance diagnosis of parallel computations /." view abstract or download file of text, 2007. http://proquest.umi.com/pqdweb?did=1335366371&sid=1&Fmt=2&clientId=11238&RQT=309&VName=PQD.

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Thesis (Ph. D.)--University of Oregon, 2007.
Typescript. Includes vita and abstract. Includes bibliographical references (leaves 119-123). Also available for download via the World Wide Web; free to University of Oregon users.

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3

Goddard, Alan John. "An automatic approach to implementing DSP algorithms on parallel processors." Thesis, City University London, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.254871.

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4

Galparsoro, Miguel Angel Maiza. "Automatic scheduling and parallel code generation for high performance real-time systems." Thesis, University of York, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.288061.

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5

Doddapaneni, Srinivas P. "Automatic dynamic decomposition of programs on distributed memory machines." Diss., Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/8158.

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6

Saà-Garriga, Albert. "Automatic source code adaptation for heterogeneous platforms." Doctoral thesis, Universitat Autònoma de Barcelona, 2016. http://hdl.handle.net/10803/399986.

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7

Zarei, Behrouz. "Performance analysis of automatic lookahead generation in parallel discrete event simulation using control flow graphs." Thesis, Lancaster University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274230.

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8

Fjeld, Hans Erik. "Application of Parallel Programming in a Automatic Detector for a Pulsed MTD Radar system : Automatic Detection and Fast Ordered Selection Algorithms." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for elektronikk og telekommunikasjon, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-18490.

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Automatic CFAR Detection is to be implemented in a real time pulsed MTD radar system, used in a mar-itime application.The CFAR should be able to have good detection properties in bad weather conditions, where rough seastates, heavy downpour and high winds are expected.Sucient detection properties may be achieved using an Ordered Statistics based CFAR to generate detec-tion threshold for the MTD radar video signal.The MTD video is the coherent raw video of the signal ltered in a bandpass lter bank, separating theDoppler frequency space of the video into a number of individual Doppler channels.The Doppler frequency shift relates to a velocity, implying that every Doppler channel represents a velocityspace, so that targets and clutter may further resolved by their relative Doppler velocityCFAR algorithms are applied to all the test cells in the MTD video signal. These algorithms have to estimatea threshold that is used at discriminating real targets from clutter in all the velocity channels of the MTDvideo.A good threshold estimate is to have a low probability of false detections, and a high probability of declaringactual targets.This is to be valid in all clutter conditions, even when one or multiple targets are surrounded by non-stationary clutter and closely spaced.The Ordered Statistics algorithms involves using the k'th largest value of the test window as a mean clutterpower estimate for its corresponding test cell.The ordered statistics model makes a threshold selection based on the rank of the samples. A task withcomplexity increasing as a function of window length and k parameter.This task is to be performed on a large number of test cells in a system running real time. In a real timeradar system, all processing have to be done before the next scan becomes available.Radian AS works on developing a PC based MTD Radar system for a pulsed Doppler radar.The radar interfaces the PC through a PCI Express radar receiver card developed by Radian AS.This thesis investigates the application of parallel programming in C/C++ in order to achieve real time au-tomatic detection in a PC based MTD radar. Two means of parallel programming are considered, involvingexploitation of multi core CPU architecture as well as using a dedicated GPU as a co processor.OpenMP is an Open Source library with compiler instructions for running tasks in parallel over multiplecores in a CPU. It is easily incorporated into C/C++ code, and may be used with most multi core CPUs.nVidia has made GP-GPU computing available to the public through CUDA, selling CUDA enabled graphicscards and providing the tools as well as documentation needed for a programmer to be able to use the GPUas co processor.CUDA C integrates the SIMT abstractions of CUDA, and a programmer may write C code that is compiledand executed on the GPU.Dierent implementations of the OS-CFAR algorithm for threshold estimation are implemented using CUDAand OpenMP.The dierent implementations are evaluated and compared to each other in terms of the results gatheredfrom executing them on MTD video.The experiences drawn from this work is concluded with respect to the application of parallel programming,and further recommendations for the future of the project of making a PC based pulsed MTD Radar signalprocessor.This thesis introduces a CUDA algorithm for high throughput ordered selection using short window lengthson a large number of cells under test.An algorithm developed in C for the project assignment leading up to this thesis is enabled openMP, alongwith a C++ STL algorithm, for performing ordered statistics ranked selection on the CPU. In addition, theCUDA OS-CFAR algorithm is ported to C with openMP.The three implementations in C/C++ are compared to the CUDA C implementation.

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Gebremedhin, Mahder. "Automatic and Explicit Parallelization Approaches for Mathematical Simulation Models." Licentiate thesis, Linköpings universitet, Programvara och system, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-117346.

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The move from single core and processor systems to multi-core and many-processors systemscomes with the requirement of implementing computations in a way that can utilizethese multiple units eciently. This task of writing ecient multi-threaded algorithmswill not be possible with out improving programming languages and compilers to providethe mechanisms to do so. Computer aided mathematical modeling and simulationis one of the most computationally intensive areas of computer science. Even simpli-ed models of physical systems can impose a considerable amount of computational loadon the processors at hand. Being able to take advantage of the potential computationpower provided by multi-core systems is vital in this area of application. This thesis triesto address how we can take advantage of the potential computation power provided bythese modern processors to improve the performance of simulations. The work presentsimprovements for the Modelica modeling language and the OpenModelica compiler. Two approaches of utilizing the computational power provided by modern multi-corearchitectures are presented in this thesis: Automatic and Explicit parallelization. Therst approach presents the process of extracting and utilizing potential parallelism fromequation systems in an automatic way with out any need for extra eort from the modelers/programmers side. The thesis explains improvements made to the OpenModelicacompiler and presents the accompanying task systems library for ecient representation,clustering, scheduling proling and executing complex equation/task systems with heavydependencies. The Explicit parallelization approach explains the process of utilizing parallelismwith the help of the modeler or programmer. New programming constructs havebeen introduced to the Modelica language in order to enable modelers write parallelizedcode. the OpenModelica compiler has been improved accordingly to recognize and utilizethe information from this new algorithmic constructs and generate parallel code toimprove the performance of computations.

The series name Linköping Studies in Science and Technology Licentiate Thesis is incorrect. The correct series name is Linköping Studies in Science and Technology Thesis.

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10

Leao, Ruth Pastora Saraiva. "A study of automatic contingency selection algorithms for steady-state security assessment of power systems and the application of parallel processing." Thesis, Loughborough University, 1995. https://dspace.lboro.ac.uk/2134/32911.

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The performance of various Contingency Selection methods has been investigated within the framework of accuracy for application to steady-state power system security assessment and suitability for execution in a real-time environment. In the study the following requirements have been considered: (a) Effectiveness: in identifying contingencies which may cause limit violations and discarding all others; (b) Adaptability: to model both permanent and temporary changes in the system; (c) Flexibility: to model any number and type of contingencies; (d) Computational efficiency: in terms of speed in selecting the sub-set of contingencies as well as in terms of storage requirements; (e) Ability: to update and augment on-line the list of contingencies given the actual system operating data.

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Книги з теми "Grammatical automatic parallel programming"

1

Fahringer, Thomas. Automatic performance prediction of parallel programs. Boston: Kluwer Academic Publishers, 1996.

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2

Koelbel, Charles. Semi-automatic process partitioning for parallel computation. Hampton, Va: ICASE, 1988.

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3

Fahringer, Thomas. Automatic Performance Prediction of Parallel Programs. Boston, MA: Springer US, 1996.

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4

O'Neill, Michael. Grammatical evolution: Evolutionary automatic programming in an arbitrary language. Boston, MA: Kluwer Academic Publishers, 2004.

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5

1972-, Ryan Conor, ed. Grammatical evolution: Evolutionary automatic programming in an arbitrary language. Boston: Kluwer Academic Publishers, 2003.

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6

O'Neill, Michael. Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language. Boston, MA: Springer US, 2003.

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7

1938-, Robert Yves, and Vivien Frederic, eds. Scheduling and automatic parallelization. Boston: Birkhäuser, 2000.

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8

Inc, Parallax. Programming and customizing the multicore Propeller microcontroller: The official guide. New York: McGraw-Hill, 2010.

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9

Inc, Parallax. Programming and customizing the multicore propeller microcontroller: The official guide. New York: McGraw-Hill, 2010.

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10

Assmann, Ulrich. Parallele Modelle für Deduktionssysteme. Sankt Augustin: Infix, 1992.

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Частини книг з теми "Grammatical automatic parallel programming"

1

O’Neil, Michael, and Conor Ryan. "Survey Of Evolutionary Automatic Programming." In Grammatical Evolution, 5–21. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0447-4_2.

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2

Feautrier, Paul. "Automatic parallelization in the polytope model." In The Data Parallel Programming Model, 79–103. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61736-1_44.

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3

Hoffmann, Jan, and Zhong Shao. "Automatic Static Cost Analysis for Parallel Programs." In Programming Languages and Systems, 132–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46669-8_6.

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4

Howard, Daniel, and J. J. Collins. "Grammatical Genetic Programming: Application in Automatic Code Parallelization." In Convergence and Hybrid Information Technology, 217–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32645-5_28.

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5

Pan, Zhelong, Brian Armstrong, Hansang Bae, and Rudolf Eigenmann. "On the Interaction of Tiling and Automatic Parallelization." In OpenMP Shared Memory Parallel Programming, 24–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-68555-5_3.

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6

Bücker, H. Martin, Arno Rasch, and Andre Vehreschild. "Automatic Generation of Parallel Code for Hessian Computations." In OpenMP Shared Memory Parallel Programming, 372–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-68555-5_30.

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7

Qiu, Zhao, Qiaoqiao Chen, Xiangsheng Huang, and Xiaoquan Liang. "Electric Bicycle Violation Automatic Detection in Unconstrained Scenarios." In Parallel Architectures, Algorithms and Programming, 168–74. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2767-8_16.

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8

Barakhshan, Parinaz, and Rudolf Eigenmann. "iCetus: A Semi-automatic Parallel Programming Assistant." In Languages and Compilers for Parallel Computing, 18–32. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99372-6_2.

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Sura, Zehra, Chi-Leung Wong, Xing Fang, Jaejin Lee, Samuel P. Midkiff, and David Padua. "Automatic Implementation of Programming Language Consistency Models." In Languages and Compilers for Parallel Computing, 172–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11596110_12.

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Lewis, Andrew, David Abramson, and Tom Peachey. "An Evolutionary Programming Algorithm for Automatic Engineering Design." In Parallel Processing and Applied Mathematics, 586–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24669-5_77.

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Тези доповідей конференцій з теми "Grammatical automatic parallel programming"

1

Li, Liao, Jing Cuiping, Wang Wei, Zhang Aiqing, and Yang Zhang. "HiPro-CodeGen - Automatic Programming for Parallel Numerical Simulations." In 9th International Conference on Software Engineering and Applications. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0005089101250131.

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2

Sakhnini, Nina, Venkata N. Inukollu, and Joseph E. Urban. "Automatic parallel programming using the descartes specification language." In 2016 7th International Conference on Information and Communication Systems (ICICS). IEEE, 2016. http://dx.doi.org/10.1109/iacs.2016.7476068.

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3

Cheng, Doreen Y., and Douglas M. Pase. "An evaluation of automatic and interactive parallel programming tools." In the 1991 ACM/IEEE conference. New York, New York, USA: ACM Press, 1991. http://dx.doi.org/10.1145/125826.126052.

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4

Chennupati, Gopinath, R. Muhammad Atif Azad, and Conor Ryan. "Automatic lock-free parallel programming on multi-core processors." In 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2016. http://dx.doi.org/10.1109/cec.2016.7744316.

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Al-Mouhamed, Mayez, and Ayaz ul Hassan Khan. "Exploration of automatic optimization for CUDA programming." In 2012 2nd IEEE International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, 2012. http://dx.doi.org/10.1109/pdgc.2012.6449791.

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6

Hückelheim, Jan, and Laurent Hascoët. "Automatic differentiation of parallel loops with formal methods." In PPoPP '22: 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3503221.3508442.

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Cordes, Daniel, Andreas Heinig, Peter Marwedel, and Arindam Mallik. "Automatic Extraction of Pipeline Parallelism for Embedded Software Using Linear Programming." In 2011 IEEE 17th International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 2011. http://dx.doi.org/10.1109/icpads.2011.31.

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Shen, Jiasi, Martin Rinard, and Nikos Vasilakis. "Automatic synthesis of parallel unix commands and pipelines with KumQuat." In PPoPP '22: 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3503221.3508400.

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Golan-Gueta, Guy, G. Ramalingam, Mooly Sagiv, and Eran Yahav. "Automatic scalable atomicity via semantic locking." In PPoPP '15: 20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2688500.2688511.

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Chuong, Lieu My, Yan Lin Aung, Siew-Kei Lam, Thambipillai Srikanthan, and Lim Chai Soon. "Automatic Compilation of C Applications for FPGA-Based Hardware Acceleration." In 2011 Fourth International Symposium on Parallel Architectures, Algorithms and Programming (PAAP). IEEE, 2011. http://dx.doi.org/10.1109/paap.2011.70.

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