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Easy and efficient agent-based simulations with the OpenABL language and compiler

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2021年 116卷 61-75页

作者： Cosenza, Biagio Popov, Nikita Juurlink, Ben Richmond, Paul Chimeh, Mozhgan Kabiri Spagnuolo, Carmine Cordasco, Gennaro Scarano, Vittorio Univ Salerno Dipartimento Informat Fisciano Italy Tech Univ Berlin Embedded Syst Architecture Berlin Germany Univ Campania Dipartimento Psicol Caserta Italy Univ Sheffield Res Software Engn Grp Sheffield S Yorkshire England JetBrains PhpStorm Team Prague Czech Republic NVIDIA Santa Clara CA USA

Agent-based simulations represent an effective scientific tool, with numerous applications from social sciences to biology, which aims to emulate or predict complex phenomena through a set of simple rules performed by multiple agents. To simulate a large number of agents with complex models, practitioners have developed high-performance parallel implementations, often specialized for particular scenarios and target hardware. It is, however, difficult to obtain portable simulations, which achieve high performance and at the same time are easy to write and to reproduce on different hardware. This article gives a complete presentation of OpenABL, a domain-specific language and a compiler for agent-based simulations that enable users to achieve high-performance parallel and distributed agent simulations with a simple and portable programming environment. OpenABL is comprised of (1) an easy-to-program language, which relies on domain abstractions and explicitly exposes agent parallelism, synchronization and locality, (2) a source-to-source compiler, and (3) a set of pluggable compiler backends, which generate target code for multi-core CPUs, GPUs, and cloud-based systems. We evaluate OpenABL on simulations from different fields. In particular, our analysis includes predator-prey and keratinocyte, two complex simulations with multiple step functions, heterogeneous agent types, and dynamic creation and removal of agents. The results show that OpenABL-generated codes are portable to different platforms, perform similarly to manual target-specific implementations, and require significantly fewer lines of codes. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Agent-based simulation Domain specific language GPU parallel and distributed computing Compilers

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Implementation of a scalable platform for real-time monitoring of machine tools

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COMPUTERS IN INDUSTRY 2024年 155卷

作者： Tapia, Endika Lopez-Novoa, Unai Sastoque-Pinilla, Leonardo Lopez-de-Lacalle, Luis Norberto Univ Basque Country Adv Mfg Ctr Aeronaut Biscay Sci & Technol Pk 202 Zamudio 48170 Spain Univ Basque Country Dept Comp Languages & Syst Rafael Moreno pasealekua 2-3 Bilbao 48013 Spain Univ Basque Country Dept Mech Engn Torres Quevedo enparantza S-N Bilbao 48013 Spain

In the new hyper connected factories, data gathering, and prediction models are key to keeping both productivity and piece quality. This paper presents a software platform that monitors and detects outliers in an industrial manufacturing process using scalable software tools. The platform collects data from a machine, processes it, and displays visualizations in a dashboard along with the results. A statistical method is used to detect outliers in the manufacturing process. The performance of the platform is assessed in two ways: firstly by monitoring a five-axis milling machine and secondly, using simulated tests. Former tests prove the suitability of the platform and reveal the issues that arise in a real environment, and latter tests prove the scalability of the platform with higher data processing needs than the previous ones.

关键词： Big data Aeronautical manufacturing Machine tool Scalable data processing parallel and distributed computing

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ABET Accreditation: A Way Forward for PDC Education

ABET Accreditation: A Way Forward for PDC Education

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35th IEEE International parallel and distributed Processing Symposium (IPDPS)

作者： Aly, Sherif G. Harmanani, Haidar Raj, Rajendra K. Sharafeddine, Sanaa Amer Univ Cairo Dept Comp Sci & Engn Cairo Egypt Lebanese Amer Univ Dept Comp Sci & Math Beirut Lebanon Rochester Inst Technol Dept Comp Sci Rochester NY 14623 USA

ISBN: (纸本)9781665435772

With parallel and distributed computing (PDC) now wide-spread, modern computing programs must incorporate PDC within the curriculum. ACM and IEEE Computer Society's Computer Science curricular guidelines have recommended exposure to PDC concepts since 2013. More recently, a variety of initiatives have made PDC curricular content, lectures, and labs freely available for undergraduate computer science programs. Despite these efforts, progress in ensuring computer science students graduate with sufficient PDC exposure has been uneven. This paper discusses the impact of ABET's revised criteria that have required exposure to PDC to achieve accreditation for computer science programs since 2018. The authors reviewed 20 top ABET-accredited computer science programs and analyzed how they covered the required PDC components in their curricula. Using their own institutions as case studies, the authors examine in detail how three different ABET-accredited computer science programs covered PDC using different approaches, yet meeting the PDC requirements of these ABET criteria. The paper also shows how ACM/IEEE Computer Society curricular guidelines for computer engineering and software engineering programs, along with ABET accreditation criteria, can cover PDC.

关键词： parallel and distributed computing computing programs curricular guidelines ABET accreditation

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Load balancing for distributed nonlocal models within asynchronous many-task systems

Load balancing for distributed nonlocal models within asynch...

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35th IEEE International parallel and distributed Processing Symposium (IPDPS)

作者： Gadikar, Pranav Diehl, Patrick Jha, Prashant K. Indian Inst Technol Madras Comp Sci Dept Chennai Tamil Nadu India Louisiana State Univ Ctr Computat & Technol Baton Rouge LA 70803 USA Univ Texas Austin Oden Inst Computat Engn & Sci Austin TX 78712 USA

ISBN: (纸本)9781665435772

In this work, we consider the challenges of developing a distributed solver for models based on nonlocal interactions. In nonlocal models, in contrast to the local model, such as the wave and heat partial differential equations, the material interacts with neighboring points on a larger-length scale compared to the mesh discretization. In developing a fully distributed solver, the interaction over a length scale greater than mesh size introduces additional data dependencies among the compute nodes and communication bottleneck. In this work, we carefully look at these challenges in the context of nonlocal models;to keep the presentation specific to the computational issues, we consider a nonlocal heat equation in a 2d setting. In particular, the distributed framework we propose pays greater attention to the bottleneck of data communication and the dynamic balancing of loads among nodes with varying compute capacity. For load balancing, we propose a novel framework that assesses the compute capacity of nodes and dynamically balances the load so that the idle time among nodes is minimal. Our framework relies heavily on HPX library, an asynchronous many-task run time system. We present several results demonstrating the effectiveness of the proposed framework.

关键词： Nonlocal Computational Models Load Balancing AMT HPX parallel and distributed computing

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Product collaborative filtering based recommendation systems for large-scale E-commerce

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International Journal of Information Management Data Insights 2025年第1期5卷

作者： Trinh, Trang Nguyen, Van-Ho Nguyen, Nghia Nguyen, Duy-Nghia University of Economics and Law Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

The rapid growth in e-commerce and the increasing diversity of customer preferences necessitates the development of an effective recommender system for a business offering a wide range of products. This paper introduces a product-based collaborative filtering approach utilizing Apache Spark, a powerful parallel processing framework to address the scalability issues of recommender systems in the cloud computing environment. Using Spark's distributed computing ability, our model attains a surprising 7.6 times speedup on the training time compared to traditional single-machine methods while preserving accuracy with a Root Mean Square Error (RMSE) 0.9. These results demonstrate the effectiveness of parallel and distributed techniques in developing efficient and accurate recommender systems for large-scale e-commerce applications. Future work will focus on applying multi-model to enhance the accuracy of prediction and configuration to optimize the cost of cluster operations. © 2025 The Author(s)

关键词： Apache spark Collaborative filtering E-commerce Large-scale parallel and distributed computing Recommendation systems

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Lessons Learned after the Introduction of parallel and distributed computing Concepts into ECE Undergraduate Curricula at UTN-Bahia Blanca Argentina

Lessons Learned after the Introduction of Parallel and Distr...

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26th IEEE International parallel and distributed Processing Symposium (IPDPS) / Workshop on High Performance Data Intensive computing

作者： Iparraguirre, Javier Friedrich, Guillermo R. Coppo, Ricardo J. Univ Tecnol Nacl Dept Elect Engn Fac Reg Bahia Blanca Bahia Blanca Buenos Aires Argentina

ISBN: (纸本)9780769546766

In 2011 we introduced an elective course on parallel Processing into the ECE undergraduate curricula. UTN Bahia Blanca was one of the first Universities in Argentina that decided to teach OpenCL. During the same year, we also began participation in the NSF/IEEE TCCP 2011 Early Adopters Program. This work summarizes the lessons we learned in our endeavor of teaching parallel and distributed computing concepts. Additionally, it discusses future improvements to our teaching methods and proposes modifications to our initial curricula.

关键词： parallel and distributed computing Higher Education ECE undergraduate curricula

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A Survey of Teaching PDC Content in Undergraduate Curriculum 11

A Survey of Teaching PDC Content in Undergraduate Curriculum

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IEEE 11th Annual computing and Communication Workshop and Conference (CCWC)

作者： Suo, Xiaoyuan Glebova, Olga Liu, David Lazar, Alina Bein, Doina Webster Univ Dept Math & Comp Sci Webster Groves MO 63119 USA Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Purdue Univ Ft Wayne Dept Comp Sci Ft Wayne IN USA Youngstown State Univ Sch Comp Sci Informat & Engn & Technol Youngstown OH 44555 USA Calif State Univ Fullerton Dept Comp Sci Fullerton CA 92634 USA

ISBN: (纸本)9781665414906

parallel and distributed computing (PDC) is included in the Computer Science Curriculum 2013 from ACM and IEEE Computer Science and hence required for ABET Computer Science programs, 2019 to 2020. It is a challenge to incorporate PDC into an existing full Computer Science program. In this paper, we examine the feasibility and challenges of introducing PDC thinking at the lower level or upper level and evaluate the effectiveness of various PDC learning and teaching methodologies. Further, we propose possible teaching strategies and techniques for introducing PDC topics for both lower and upper-level undergraduate students.

关键词： parallelism Teaching PDC CS curriculum parallel and distributed computing Active Learning Accreditation

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Scaling sparse matrix-matrix multiplication in the accumulo database

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distributed AND parallel DATABASES 2020年第1期38卷 31-62页

作者： Demirci, Gunduz Vehbi Aykanat, Cevdet Bilkent Univ Dept Comp Engn Ankara Turkey

We propose and implement a sparse matrix-matrix multiplication (SpGEMM) algorithm running on top of Accumulo's iterator framework which enables high performance distributed parallelism. The proposed algorithm provides write-locality while ingesting the output matrix back to database via utilizing row-by-row parallel SpGEMM. The proposed solution also alleviates scanning of input matrices multiple times by making use of Accumulo's batch scanning capability which is used for accessing multiple ranges of key-value pairs in parallel. Even though the use of batch-scanning introduces some latency overheads, these overheads are alleviated by the proposed solution and by using node-level parallelism structures. We also propose a matrix partitioning scheme which reduces the total communication volume and provides a balance of workload among servers. The results of extensive experiments performed on both real-world and synthetic sparse matrices show that the proposed algorithm scales significantly better than the outer-product parallel SpGEMM algorithm available in the Graphulo library. By applying the proposed matrix partitioning, the performance of the proposed algorithm is further improved considerably.

关键词： Databases NoSQL Accumulo Graphulo parallel and distributed computing Sparse matrices Sparse matrix-matrix multiplication SpGEMM Matrix partitioning Graph partitioning Data locality

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The state of the art and taxonomy of big data analytics: view from new big data framework

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ARTIFICIAL INTELLIGENCE REVIEW 2020年第2期53卷 989-1037页

作者： Mohamed, Azlinah Najafabadi, Maryam Khanian Wah, Yap Bee Zaman, Ezzatul Akmal Kamaru Maskat, Ruhaila Univ Teknol MARA Adv Analyt Engn Ctr Fac Comp & Math Sci Shah Alam Selangor Malaysia INTI Int Univ & Coll Fac Informat Technol Nilai Negeri Sembilan Malaysia

Big data has become a significant research area due to the birth of enormous data generated from various sources like social media, internet of things and multimedia applications. Big data has played critical role in many decision makings and forecasting domains such as recommendation systems, business analysis, healthcare, web display advertising, clinicians, transportation, fraud detection and tourism marketing. The rapid development of various big data tools such as Hadoop, Storm, Spark, Flink, Kafka and Pig in research and industrial communities has allowed the huge number of data to be distributed, communicated and processed. Big data applications use big data analytics techniques to efficiently analyze large amounts of data. However, choosing the suitable big data tools based on batch and stream data processing and analytics techniques for development a big data system are difficult due to the challenges in processing and applying big data. Practitioners and researchers who are developing big data systems have inadequate information about the current technology and requirement concerning the big data platform. Hence, the strengths and weaknesses of big data technologies and effective solutions for Big Data challenges are needed to be discussed. Hence, due to that, this paper presents a review of the literature that analyzes the use of big data tools and big data analytics techniques in areas like health and medical care, social networking and internet, government and public sector, natural resource management, economic and business sector. The goals of this paper are to (1) understand the trend of big data-related research and current frames of big data technologies;(2) identify trends in the use or research of big data tools based on batch and stream processing and big data analytics techniques;(3) assist and provide new researchers and practitioners to place new research activity in this domain appropriately. The findings of this study will provide insights

关键词： parallel and distributed computing Big data tools Big data analytics techniques Domain area

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Optimizing FHEW With Heterogeneous High-Performance computing

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第8期16卷 5335-5344页

作者： Lei, Xinya Guo, Ruixin Zhang, Feng Wang, Lizhe Xu, Rui Qu, Guangzhi China Univ Geosci Sch Comp Sci Wuhan 430078 Peoples R China China Univ Geosci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430078 Peoples R China Oakland Univ Sch Engn & Comp Sci Rochester MI 48309 USA

The latest implementation of the fully homomorphic encryption algorithm (FHEW), FHEW-V2, takes about 0.12 s for a bootstrapping on a single-node computer. It seems much faster than the previous implementations. However, the 30-bit homomorphic addition requires 270 times of bootstrapping;plus those spent on key generation, the total elapsed time climbs to 55 seconds, which is unacceptable. In this article, we reveal how to further optimize FHEW-V2 by focusing on efficiently constructing homomorphic full adders. We tackle inefficiency in FHEW-V2 by massive efforts: First, we explore FHEW-V2 and locate hotspots;second, we leverage the heterogeneous parallel computing model of multicore CPU and GPUs to remove the hotspots to improve performance. The empirical results show that a 30-bit homomorphic addition is completed in 23.8753 s after optimization, gaining an overall speedup of 2.2845;and a 6-bit homomorphic multiplication costs 25.8438, gaining an overall speedup of 2.2435. The 2.2845 speedup is a rough integration of a 13.248 speedup for the key generation and a 1.672 speedup for the bootstrapping;the 2.2435 speedup is a rough integration of the same key generation and a 1.675 speedup for the bootstrapping. We also reveal the strengths and weaknesses of FHEW-V2 by comparing it with a state-of-the-art somewhat homomorphic encryption algorithm, microsoft's simple encrypted arithmetic library (SEAL).

关键词： Encryption Multicore processing Computational modeling Informatics Optimization Graphics processing units FHEW homomorphic encryption high-performance computing (HPC) parallel and distributed computing

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