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A Task parallel programming Framework Based on Heterogeneous Computing Platforms 1

5th International Symposium on Intelligent Systems Technologies and Applications, ISTA 2019

作者： Yuan, Liu Dong, Yinggang Li, Yangyang Zhang, Rui Xie, Haiyong China Academy of Electronics and Information Technology No. 11 Shuangyuan Road Shijingshan District Beijing100041 China No. 11 Shuangyuan Road Shijingshan District Beijing100041 China Institute of Network Technology Beijing University of Posts and Telecommunications No. 10 Xitucheng Road Haidian District Beijing100876 China Advanced Innovation Center for Human Brain Protection Capital Medical University No. 119 South Fourth Ring Road West Fengtai District Beijing100070 China School of Cyber Science University of Science and Technology of China No. 96 Jinzhai Road HefeiAnhui230027 China

ISBN: (数字)9789811539145

ISBN: (纸本)9789811539138

Along with the development of big data and artificial intelligence, high-performance heterogeneous parallel computing technology has received more and more attention from the industry. On the one hand, heterogeneous computing can significantly improve the computational efficiency. But on the other hand, it can also make the programming more difficult. Such bottlenecks make it harder to give full play to the advantages of heterogeneous hardware. There is currently no comprehensive solution to meet the efficient task scheduling requirements of heterogeneous computing systems. Therefore, this paper introduces a task parallel programming framework based on heterogeneous computing, including the design of programming model, adjusting task granularity, and task scheduling. A ST-HEFT-based static task scheduling method for heterogeneous computing system is proposed to improve computing efficiency. Simulation results show that the framework can obtain better average acceleration ratio. The difficulty of parallel programs for developers can be reduced, and the varying capabilities of heterogeneous components can be fully utilized. © 2020, Springer Nature Singapore Pte Ltd.

关键词： parallel programming

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Learning parallel programming through programming Challenges

Learning Parallel Programming through Programming Challenges

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2020 IEEE Frontiers in Education Conference, FIE 2020

作者： Martins, Guilherme Lopes De Souza, Paulo Sergio Jose Conte, Davi Bruschi, Sarita Mazzini University of São Paulo Institute of Mathematics and Computer Sciences São Carlos Brazil

ISBN: (纸本)9781728189611

This research full paper describes the use of challenges to teach parallel programming, regardless of teaching methodology (traditional, Problem-Based Learning and others) or programming-contest support systems. We verify how challenges contribute to the learning of parallel programming, considering technical and motivational aspects. Studies demonstrate the potential of using programming contests as educational resources at the end of courses, encouraging students to practice and learn more about parallel programming. However, there are no proposals exploring programming challenges as part of the learning process, since the beginning of the course. This paper presents four different experiments applying challenges to teach parallel programming for undergraduate and graduate students. We evaluated different teaching/learning methodologies, contexts, educational resources (including programming marathon systems), and groups of students. Considering all the experiments, almost 250 students participated and were evaluated. We developed and applied 58 challenges to analyze theoretical and practical knowledge about parallel programming. Different metrics were used to evaluate the students during the experiments: theoretical assessments, source-code quality, program output correctness, and students receptivity/motivation to learning from challenges. Results from experiments show scores of up to 96% of learning on technical aspects and up to 83% on student satisfaction. Challenges stimulated the development of high-quality parallel solutions and promoted a healthy environment among students, where the lightly-competitive context contributed to create a collaborative atmosphere. The use of programming marathon systems as an educational tool is not imperative. The use of challenges for the students' learning, not on their classification, showed that it is possible to teach parallel programming, maintaining students focused and motivated. © 2020 IEEE.

关键词： parallel programming

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Students’ Favorite parallel programming Practices 6th

Students’ Favorite Parallel Programming Practices

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6th Russian Supercomputing Days, RuSCDays 2020

作者： Konshin, Igor Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences Moscow Russia Dorodnicyn Computing Centre of FRC CSC RAS Moscow Russia DolgoprudnyMoscow Region Russia Sechenov University Moscow Russia

ISBN: (纸本)9783030646158

The paper describes the students’ most favorite parallel programming practices when studying a parallel computing course. Students’ learning activities are considered as the motivation to study the features of parallel programming. A presentation of parallel computing theory for shared and distributed memory is introduced in a sequence that allows to make students interested in the practical work. There are also derived estimates of the programs’ parallel efficiency which can be directly applied to the described learning activities. © 2020, Springer Nature Switzerland AG.

关键词： parallel programming

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JAMPI: A C++ parallel programming interface allowing the implementation of custom and generic scheduling mechanisms 32

JAMPI: A C++ parallel programming interface allowing the imp...

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32nd IEEE International Symposium on Computer Architecture and High Performance Computing, SBAC-PAD 2020

作者： Di Domenico, Daniel Cavalheiro, Gerson G. H. Federal University of Pelotas Pelotas-RS Brazil

ISBN: (纸本)9781728199245

The widespread of modern parallel architectures brought many challenges in terms of programming. In response, many parallel programming tools intend to aid the user in order to exploit hardware resources effectively. As a new alternative, this paper introduces the design and implementation of JAMPI, a generic parallel programming interface developed in C++ focused on code reuse, productivity and high-level abstraction to enable the construction of parallel applications. JAMPI is totally integrated with its host programming language and offers, as main feature, a fully disassociation of its programming interface from its scheduling mechanism. Aiming to manage and optimize the parallel execution, the proposed interface allows the programmer to implement a custom scheduling heuristic for each portion of the application code. Besides JAMPI model description, we proceeded some preliminary experiments using applications encoded with the proposed framework. Results showed that JAMPI can be used to reach performance on multicore platforms and does not add performance penalty over the sequential version of the benchmarks. © 2020 IEEE.

关键词： parallel programming

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SSAM: A Haskell parallel programming STM Based Simple Actor Model 4

SSAM: A Haskell Parallel Programming STM Based Simple Actor ...

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4th International Conference on Computing and Applied Informatics 2019, ICCAI 2019

作者： Kim, Y. Cheon, J. Hur, T. Byun, S. Woo, G. Dep. of Electrical and Computer Engineering Pusan National University Korea Republic of Dep. of Computer Science Kyungsung University Korea Republic of Smart Control Center of LG Electronics Korea Republic of

As computers with manycore architecture are being widely spread, parallel programming becomes a pending issue. While parallel programming has been a challenging issue, Haskell is known to be of the best available one. The immutability and declarative nature of Haskell suit well the need for parallelism. In our previous work, we implemented the SAM, a parallel programming model based on Haskell. In this paper, we propose the SSAM, an updated version of SAM, by solving the performance overhead of SAM socket. We discuss the architecture of SSAM and its major functions and show the 14% scalability upgrade in a performance test. © Published under licence by IOP Publishing Ltd.

关键词： parallel programming

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Pattern-driven design of a multiparadigm parallel programming framework 15

Pattern-driven design of a multiparadigm parallel programmin...

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15th International Conference on Evaluation of Novel Approaches to Software Engineering, ENASE 2020

作者： Niculescu, Virginia Loulergue, Frederic Bufnea, Darius Sterca, Adrian Faculty of Mathematics and Computer Science Babes-Bolyai University Cluj-Napoca Romania School of Informatics Computing and Cyber Systems Northern Arizona University United States Univ. Orleans INSA Centre Val de Loire LIFO EA 4022 Orléans France

ISBN: (纸本)9789897584213

parallel programming is more complex than sequential programming. It is therefore more difficult to achieve the same software quality in a parallel context. High-level parallel programming approaches are intermediate approaches where users are offered simplified APIs. There is a trade-off between expressivity and programming productivity, while still offering good performance. By being less error-prone, high-level approaches can improve application quality. From the API user point of view, such approaches should provide ease of programming without hindering performance. From the API implementor point of view, such approaches should be portable across parallel paradigms and extensible. JPLF is a framework for the Java language based on the theory of Powerlists, which are parallel recursive data structures. It is a high-level parallel programming approach that possesses the qualities mentioned above. This paper reflects on the design of JPLF: it explains the design choices and highlights the design patterns and design principles applied to build JPLF. © Copyright 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

关键词： parallel programming

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Designing a parallel programming Course for Lower-Division Students

Designing a Parallel Programming Course for Lower-Division S...

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2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020

作者： Chen, Xuguang Saint Martin's University Computer Science Department LaceyWA98503 United States

ISBN: (纸本)9781728176246

This paper covers the design of a course introducing parallel programming. The design is partly based on the feedback collected by the author when teaching other parallel computing courses offered for senior undergraduates and graduates, plus the conversations with lower-division students outside of class. It targets the students typically having completed CS1 and CS2 courses and puts more emphasis on gaining parallel programming skills through hands-on exercises. Its ideal lecture schedule is that each week, one meeting introduces the concepts and theories of parallel computing in a traditional classroom and the other discusses parallel programming skills in a computer lab. Unlike the courses designed for senior undergraduates or graduates, its instructional approaches are more inclined to student-centered and teamwork approaches. Its coursework will include term projects, the topics of which are selected from the instructor' research area and tailored for this class. The course design has been completed and will be implemented in the spring of 2021 as a pilot. © 2020 IEEE.

关键词： parallel programming

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pPython for parallel Python programming

pPython for Parallel Python Programming

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IEEE High Performance Extreme Computing Virtual Conference (HPEC)

作者： Byun, Chansup Arcand, William Bestor, David Bergeron, Bill Gadepally, Vijay Houle, Michael Hubbell, Matthew Jananthan, Hayden Jones, Michael Keville, Kurt Klein, Anna Michaleas, Peter Milechin, Lauren Morales, Guillermo Mullen, Julie Prout, Andrew Reuther, Albert Rosa, Antonio Samsi, Siddharth Yee, Charles Kepner, Jeremy MIT Cambridge MA 02139 USA

ISBN: (数字)9781665497862

ISBN: (纸本)9781665497862

pPython seeks to provide a parallel capability that provides good speed-up without sacrificing the ease of programming in Python by implementing partitioned global array semantics (PGAS) on top of a simple file-based messaging library (PythonMPI) in pure Python. The core data structure in pPython is a distributed numerical array whose distribution onto multiple processors is specified with a 'map' construct. Communication operations between distributed arrays are abstracted away from the user and pPython transparently supports redistribution between any block-cyclic-overlapped distributions in up to four dimensions. pPython follows a SPMD (single program multiple data) model of computation. pPython runs on any combination of heterogeneous systems that support Python, including Windows, Linux, and MacOS operating systems. In addition to running transparently on single-node (e.g., a laptop), pPython provides a scheduler interface, so that pPython can be executed in a massively parallel computing environment. The initial implementation uses the Slurm scheduler. Performance of pPython on the HPC Challenge benchmark suite demonstrates both ease of programming and scalability.

关键词： PGAS Python MPI parallel programming

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parallel programming for Classification Algorithms Using Logistic Regression and Artificial Neural Networks: Framework and Applications

Parallel Programming for Classification Algorithms Using Log...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Mohamed Almansoor Mohamed Alaradi Abdulla Alqaddoumi University of Bahrain Sakhir Bahrain College of Information Technology University of Bahrain Sakhir Bahrain

ISBN: (数字)9781728196756

ISBN: (纸本)9781728196763

parallel programming is an excellent way to speed up computation due to the simultaneous execution of the processes so that the operation is divided into the available threads. OpenMP, available for C, C++, and Fortran, is one of the popular frameworks for multiprocessing programming with shared memory. This work aims to utilize parallel programming on two classification cases using logistic regression and artificial neural networks. The main advantage of using parallel programming is the speed being higher. The first case study aims to predict the diabetic outcome from a dataset that contains health data of patients using logistic regression. It aims to explore a framework in parallelizing function execution to speed large sample processing. The case study featuring artificial neural networks will address a workflow of a train and execute deep learning models using parallelization to process large influx of timeseries data to diagnose presence of an anomaly. The case that used logistic regression achieved 62.5% reduction in execution time while the case that used neural network scored a 4.5 folds nominal reduction in training time and 71.1% reduction in execution time using 1-8 multithreading range.

关键词： Logistics Diabetes Training parallel programming Instruction sets Artificial neural networks C++ languages

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Performance evaluation in the reconstruction of 2D images of computed tomography using massively parallel programming CUDA

Research Square

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Research Square 2021年

作者： Cordeiro, Alexssandro Ferreira de Paula Filho, Pedro Luiz da Silva, Hamilton Pereira Junior, Arnaldo Candido Casanova, Edresson Spancerski, Jandrei Sartori Department of Computing The Federal Technological University of Parana Brazil Avenue 4232 PR Medianeira85884-000 Brazil

Purpose analysis of processing time and similarity of images generated between CPU and GPU architectures and sequential and parallel programming methodologies. Material and methods for image processing a computer with AMD FX-8350 processor and an Nvidia GTX 960 Maxwell GPU was used, along with the CUD-AFY library and the programming language C# with the IDE Visual studio. Results the results of the comparisons indicate that the form of sequential programming in a CPU generates reliable images at a high custom of time when compared to the forms of parallel programming in CPU and GPU. While parallel programming generates faster results, but with increased noise in the reconstructed image. For data types float a GPU obtained best result with average time equivalent to 31 of the processor, however the data is of type double the parallel CPU approach obtained the best performance. Conclusion for the float data type, the GPU had the best average time performance, while for the double data type the best average time performance was for the parallel approach CPU. Regarding image quality, the sequential approach obtained similar outputs, while the parallel approaches generated noise in their outputs. © 2021, CC BY.

关键词： parallel programming

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