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3rd International Conference on Modeling, Simulation, Optimization and Algorithm, ICMSOA 2021

作者： Wu, Linghong School of Computer and Information Engineering Nanchang Institute of Technology JIANGXI Nanchang330044 China

In recent years, with the continuous improvement of high performance computing hardware platform,the application of high performance for a wide range of high-performance, traditional parallel programming environment tools cannot meet the growing demand for high-performance applications and the face of parallel software development environment in a relatively backward state. The heterogeneous system of the new coprocessor with a high performance and power ratio can have high performance computing needs to provide the task to accelerate. As a result, heterogeneous systems are becoming a new trend in high performance computing. However, with the introduction of the new coprocessor and the expansion of the system scale, heterogeneous systems are difficult to program the problem is also increasingly prominent. In view of this problem, this paper adopts the abstract technology to enhance the granularity of parallel programming, and designs a structured parallel programming prototype system which can achieve different levels of implicit parallelism © Published under licence by IOP Publishing Ltd.

关键词： parallel programming

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A Systematic Review on Teaching parallel programming 22

A Systematic Review on Teaching Parallel Programming

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11th Euro-American Conference on Telematics and Information Systems, EATIS 2022

作者： Carneiro Neto, Jose Aprigio Alves Neto, Antonio Jose Moreno, Edward David Federal Institute of Sergipe Brazil Federal University of Sergipe Brazil

ISBN: (纸本)9781450397391

This work aimed to perform a systematic review of the literature on teaching parallel programming using low-cost clusters, identifying the main programming languages, hardware platforms and software tools used in teaching-learning this type of programming. The research results showed that the most used clusters in the teaching of parallel programming were assembled from multicore machines (Cluster Beowulf) and by single board computers (SBC), in addition to multicore machines with graphics acceleration cards (GPUs). Regarding the use of programming languages, software tools and parallelism libraries used in teaching parallel programming, it is observed that most of the researched works mentioned the use of C, C++, and JAVA programming languages, and as parallelism libraries the use of MPI, OpenMP, CUDA and Apache Hadoop. Furthermore, the tests on the clusters were carried out through the implementation of parallelized generic algorithms and, in some cases, using algorithms that involve matrix operations. © 2022 ACM.

关键词： parallel programming

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PyOMP: Multithreaded parallel programming in Python

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COMPUTING IN SCIENCE & ENGINEERING 2021年第6期23卷 77-80页

作者： Mattson, Timothy G. Anderson, Todd A. Georgakoudis, Giorgis Intel Corp Parallel Comp Lab Hillsboro OR 97124 USA Lawrence Livermore Natl Lab Ctr Appl Sci Comp Livermore CA 94550 USA

Python is a widely used language in scientific computing. When the goal is high performance, however, Python lags far behind low-level languages such as C and Fortran. To support applications that stress performance, Python needs to access the full capabilities of modern CPUs. That means support for parallel multithreading. In this article, we describe PyOMP, a system that enables OpenMP in Python. Programmers write code in Python with OpenMP, Numba generates code that compiles to LLVM, and the resulting programs run with performance that approaches that from code written with C and OpenMP. In this article, we provide an update on the PyOMP project and explain how to install it and use it to write parallel multithreaded code in Python.

关键词： Codes Program processors Multithreading Scientific computing parallel programming Memory management Benchmark testing

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WRATH: Workload Resilience Across Task Hierarchies in Task-based parallel programming Frameworks

arXiv

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arXiv 2025年

作者： Zhou, Sicheng Li, Zhuozhao Hayot-Sasson, Valérie Pan, Haochen Gonthier, Maxime Pauloski, J. Gregory Chard, Ryan Chard, Kyle Foster, Ian Department of Computer Science Southern University of Science and Technology Guangdong China Department of Computer Science University of Chicago ChicagoIL United States Data Science and Learning Division Argonne National Laboratory LemontIL United States

Failures in Task-based parallel programming (TBPP) can severely degrade performance and result in incomplete or incorrect outcomes. Existing failure-handling approaches, including reactive, proactive, and resilient methods such as retry and checkpointing mechanisms, often apply uniform retry mechanisms regardless of the root cause of failures, failing to account for the unique characteristics of TBPP frameworks such as heterogeneous resource availability and task-level failures. To address these limitations, we propose WRATH, a novel systematic approach that categorizes failures based on the unique layered structure of TBPP frameworks and defines specific responses to address failures at different layers. WRATH combines a distributed monitoring system and a resilient module to collaboratively address different types of failures in real time. The monitoring system captures execution and resource information, reports failures, and profiles tasks across different layers of TBPP frameworks. The resilient module then categorizes failures and responds with appropriate actions, such as hierarchically retrying failed tasks on suitable resources. Evaluations demonstrate that WRATH significantly improves TBPP robustness, tripling the task success rate and maintaining an application success rate of over 90% for resolvable failures. Additionally, WRATH can reduce the time to failure by 20%-50%, allowing tasks that are destined to fail to be identified and fail more quickly. © 2025, CC BY.

关键词： parallel programming

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parallel programming with Pictures - A Second Path (Work-in-progress)

Parallel Programming with Pictures - A Second Path (Work-in-...

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IEEE Integrated STEM Education Conference (ISEC)

作者： Liam Davis-Wallace Wu-Chun Feng Virginia Tech USA

ISBN: (纸本)9781665484305

Block based programming is a cornerstone of computer science education in elementary and middle schools across the country. By providing a visual representation of coding, users without prior knowledge can more easily begin writing their first program. Applications like Scratch and Snap! provide accessible block-based programming. The work outlined in this paper is a continuation of a recent study parallel programming with Pictures is a Snap! [1] that focused on educating students about the importance of parallel programming in the wake of plateauing computational horsepower in a single computer core. This addition makes the progression of modules more accessible for students of all *** parallelism in the Snap! block-based coding language, we can program with parallelism and utilize multiple cores of a machine at once. By teaching about multi-core technology, students will be more aptly prepared for the future of computer science and computing technology. Expanding on this with lessons to target different audiences can help broaden the appeal of introductory programming.

关键词： Computers Visualization parallel programming Multicore processing Education parallel processing Writing

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Evaluating Performance and Energy Efficiency of parallel programming Models in Heterogeneous Computing Systems

Evaluating Performance and Energy Efficiency of Parallel Pro...

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IEEE International Workshop/Symposium on Workload Characterization

作者： Demirhan Sevim Baturalp Bilgin Ismail Akturk Computer Science Department Ozyegin University Istanbul Turkey

ISBN: (数字)9798350356038

ISBN: (纸本)9798350356045

We provide a detailed evaluation of several parallel programming models, emphasizing both performance and energy efficiency in heterogeneous computing systems. The evaluation employs a diverse array of hardware, including Intel Xeon and AMD Epyc CPUs, along with NVIDIA GPUs featuring Pascal, Turing, and Ampere architectures, and an AMD GPU with Vega10 architecture. We utilize SYCL, OpenMP, CUDA, and HIP for implementing benchmarks in 11 varied application domains, offering a comprehensive perspective on the capabilities of these programming models in diverse computing environments.

关键词： Performance evaluation parallel programming Computational modeling Memory management Graphics processing units Market research Energy efficiency Hardware Heterogeneous networks Data models

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HPC-in-Containers: A Containerized parallel Environment for parallel programming Learning Using Docker

HPC-in-Containers: A Containerized Parallel Environment for ...

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International Conference on Information Technology Systems and Innovation (ICITSI)

作者： Andria Arisal Heru Suhartanto Research Center for Data and Information Sciences National Research and Innovation Agency Bandung Indonesia Faculty of Computer Science University of Indonesia Depok Indonesia

ISBN: (数字)9798331511470

ISBN: (纸本)9798331511487

This paper presents HPC-in-Containers, a novel containerized parallel computing environment using Docker. It is designed to facilitate learning parallel programming concepts, where users do not have to deploy a multicomputer infrastructure. Instead, they can use their desktop and laptop to easily have a multicomputer environment by utilizing containerization on the multi-core CPU. The environment offers a comprehensive platform for exploring various parallel programming paradigms, including distributed memory parallelism, shared memory parallelism, and hybrid approaches. We include source code samples for each paradigm to demonstrate its capabilities: parallel multicomputer implementations using MPI, parallel thread implementations for single computers using OpenMP, and hybrid parallelization techniques combining both MPI and OpenMP. Furthermore, to enhance user interaction and ease of use, the Docker image incorporates a Jupyter Notebook interface with a C interpreter, allowing users to write, execute, and visualize parallel code through a web browser. This feature makes HPC-in-Containers particularly suitable for educational settings, enabling students to experiment with parallel programming concepts in a controlled, reproducible environment. Our work contributes to the field of parallel computing education by providing a flexible, scalable, and easily deployable platform that bridges the gap between theoretical concepts and practical implementation. It offers a valuable resource for educators, students, and researchers seeking to explore and master the intricacies of parallel programming in a containerized setting.

关键词： Visualization Technological innovation Portable computers parallel programming Source coding Instruction sets Education parallel processing User experience Monitoring

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Integrating Interactive Performance Analysis in Jupyter Notebooks for parallel programming Education

Integrating Interactive Performance Analysis in Jupyter Note...

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IEEE International Symposium on parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Lena Oden Klaus Nölp Philipp Brauner Computer Engineering University of Hagen Hagen Germany Human-Computer Interaction Center RWTH Aachen University Aachen Germany

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Understanding the performance behavior of parallel applications is important in many ways, but doing so is not easy. Most open source analysis tools are written for the com mand line. We are building on these proven tools to provide an interactive performance analysis experience within Jupyter Notebooks when developing parallel code with MPI, OpenMP, or both. Our solution makes it possible to measure the execution time, perform profiling and tracing, and visualize the results within the notebooks. For ease of use, it provides both a graphical JupyterLab extension and a C++ Api. The JupyterLab extension shows a dialog where the user can select the type of analysis and its parameters. Internally, this tool uses Score-P,Scalasca, and Cube to generate profiling and tracing data. This tight integration gives students easy access to profiling tools and helps them better understand concepts such as benchmarking, scalability and performance bottlenecks. In addition to the technical development, the article presents hands-on exercises from our well-established parallel programming course. We conclude with a qualitative and quantitative evaluation with 19 students, which shows a positive effect of the tools on the students' perceived competence.

关键词： Training Codes parallel programming Documentation C++ languages Benchmark testing User interfaces

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parallel Optimization for Robotics: An Undergraduate Introduction to GPU parallel programming and Numerical Optimization Research

Parallel Optimization for Robotics: An Undergraduate Introdu...

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IEEE International Symposium on parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Brian Plancher Barnard College Columbia University New York NY

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

While parallel programming, particularly on graphics processing units (GPUs), and numerical optimization hold immense potential to tackle real-world computational challenges across disciplines, their inherent complexity and technical demands often act as daunting barriers to entry. This, unfortunately, limits accessibility and diversity within these crucial areas of computer science. To combat this challenge and ignite excitement among undergraduate learners, we developed an application-driven course, harnessing robotics as a lens to demystify the intricacies of these topics making them tangible and engaging. Our course's prerequisites are limited to the required undergraduate introductory core curriculum, opening doors for a wider range of students. Our course also features a large final-project component to connect theoretical learning to applied practice. In our first offering of the course we attracted 27 students without prior experience in these topics and found that an overwhelming majority of the students felt that they learned both technical and soft skills such that they felt prepared for future study in these fields.

关键词： Computer science Distributed processing parallel programming Educational robots Conferences Graphics processing units Complexity theory

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A Real-Time parallel programming Approach for Rust

Ada User Journal

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Ada User Journal 2023年第3期44卷 232-236页

作者： Carvalho, Tiago Silva, Hugo Pinho, Luís Miguel Instituto Superior de Engenharia do Porto Porto Portugal

The development of real-time systems is one of the areas with the highest relevance in computer science, and the number of critical systems has increased significantly. These systems considers several applications running concurrently, and inside each of those applications code might be parallelized to improve their performance and control the priority of each parallelizable task. Several efforts have been done in different programming languages to provide real-time systems with parallel programming models, whether by code extensions or annotations, or with specific features in the actual language core. Rust is a recent programming language that have quickly grown in potential and already with a large community, being continuously formed. The language is a good candidate in terms of both real-time systems and parallel programming. However, there is a lack of work that joins these two important concepts in an efficient and reliable way. In this work we aim to design and provide a framework for real-time parallel systems. We conduct a study over the existing work in other programming languages and aim to bring their advantages and useful programming models into the Rust programming language, in the format of a real-time parallel programming library. © 2023, Ada-Europe. All rights reserved.

关键词： parallel programming

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