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An Innovative Control Approach for Cyber-Physical Transportation Systems: The Case of Monte-Carlo Workflow Computations

An Innovative Control Approach for Cyber-Physical Transporta...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Vladislav Kashansky Sara Agha Hossein Kashani Javier Garcia-Blas Fabrizio Marozzo Hai Zhuge Xiaoping Sun Research Laboratory Eteronix GmbH Vienna Austria Institute of Production and Logistics Management Business School Johannes Kepler Universität Linz Austria ARCOS Group Universidad Carlos III de Madrid Leganes Spain DIMES Department University of Calabria Rende Italy Key Laboratory of Intelligent Information Processing Institute of Computing Technology (ICTCAS) Beijing China

Contemporarily, in light of the intelligent transportation systems (ITS) sector, the tendency can be observed that the solution of the multi-objective cyber-physical optimization problems with imperfect information takes an increasingly weighted role. In the present scientific work, the authors want to take these developments into account by introducing an innovative cyber-physical architectural design and corresponding the two-stage heuristic computing approach. It is utilized in synergy with the MCSA 1 1 Multi-tier Cyber-physical System Architecture and DCEx architectural principles for the workflow scheduling of Monte-Carlo simulation, which is based on the intelligent and sustainable route-order dispatching process model. Factors such as emissions, transport costs, risks, and the individual weighting of orders are reflected in the model. In particular, the authors define a stochastic ILP-based 2 2 Integer Linear Programming monte-carlo workflow model. They further propose two-stage scheduling heuristic with 6-HEFT DAG relaxation as first stage and apply state-of-the-art techniques as a part of SCIP framework to solve 2nd 1–0 ILP-based stage; evaluate the performance of the scheduling approach. The authors obtain preliminary results of the second stage behavior using a realistic heterogeneous computing scenario and corresponding constraint structures within MACS simulator engine 3 3 Modular Architecture for Complex Computing Systems Analysis. The results from the experiments illustrate moderate complexity of the approach. Scalability of the model looks promising for the applicability in various industry-related scenarios and corresponding computing environments.

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SimplePIM: A Software Framework for Productive and Efficient processing-in-Memory

SimplePIM: A Software Framework for Productive and Efficient...

引用

international conference on parallel Architecture and Compilation techniques (PACT)

作者： Jinfan Chen Juan Gómez-Luna Izzat El Hajj Yuxin Guo Onur Mutlu ETH Zürich American University of Beirut

Data movement between memory and processors is a major bottleneck in modern computing systems. The processing-in-memory (PIM) paradigm aims to alleviate this bottleneck by performing computation inside memory chips. Real PIM hardware (e.g., the UPMEM system) is now available and has demonstrated potential in many applications. However, programming such real PIM hardware remains a challenge for many programmers. This paper presents a new software framework, SimplePIM, to aid programming real PIM systems. The framework processes arrays of arbitrary elements on a PIM device by calling iterator functions from the host and provides primitives for communication among PIM cores and between PIM and the host system. We implement SimplePIM for the UPMEM PIM system and evaluate it on six major applications. Our results show that SimplePIM enables 66.5% to 83.1% reduction in lines of code in PIM programs. The resulting code leads to higher performance (between 10% and 37% speedup) than hand-optimized code in three applications and provides comparable performance in three others. SimplePIM is fully and freely available at https://***/CMU-SAFARI/SimplePIM.

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parallel Computing techniques for Accelerating Machine Learning Algorithms on Big Data

Parallel Computing Techniques for Accelerating Machine Learn...

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international conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Rahul Mishra Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

In the era of Big Data, the computational demands of machine learning (ML) algorithms have grown exponentially, necessitating the development of efficient parallel computing techniques. This research paper delves into the exploration and evaluation of advanced parallel computing methodologies tailored for accelerating ML algorithms when applied to vast datasets. We commence by providing a comprehensive overview of the existing parallelization paradigms, highlighting their strengths and limitations in the context of ML. Subsequently, we introduce novel techniques that harness the combined power of distributed systems, multicore processors, and graphics processing units (GPUs) to achieve significant speed-ups. Experimental results, derived from processing multi-terabyte datasets, demonstrate that our proposed methods can achieve up to a tenfold increase in computational efficiency compared to traditional approaches. Furthermore, we present a detailed analysis of the scalability, fault-tolerance, and resource utilization of our techniques, providing insights for practitioners aiming to deploy ML algorithms on large-scale data infrastructures. This work not only paves the way for faster ML computations but also offers a blueprint for the next generation of scalable, parallel ML systems.

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A Performance Analysis of Autovectorization on RVV RISC-V Boards

A Performance Analysis of Autovectorization on RVV RISC-V Bo...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Lorenzo Carpentieri Mohammad VazirPanah Biagio Cosenza Department of Computer Science University of Salerno Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The RISC-V instruction set architecture has become increasingly popular due to its open source and extensible design, making it a competitive choice in high-performance computing and embedded systems. The RISC-V Vector extension (RVV) empowers RISC-V processors with length-agnostic vectorization capabilities, a critical feature for efficiently handling parallel processing demands across different hardware. Compiler support for autovectorization allows to generate vector instructions automatically without requiring any effort to programmers. Given the limited yet evolving compiler support for RVV, this paper offers an in-depth examination of autovectorization capabilities in GCC and LLVM, for RVV version 0.7 and 1.0. We evaluated the autovectorization performance of LLVM, LLVM-EPI and GCC compilers across 151 loops from the Test Suite for Vectorizing Compilers (TSVC) ans seven real-world applications on the AllWinner D1 and BananaPi-F3 boards, representing RISC-V vector hardware. Our study focuses on quantifying and comparing the level of vectorization each compiler achieves across a diverse range of vectorization patterns and workloads, providing insight into their strengths and limitations with respect to RISC-V RVV. Our findings highlight that the LLVM-19 compiler outperforms GCC-14 in 76 out of 151 loops, and its performance is more sensitive to the selection of vector length. Additionally, tuning the vector Length Multiplier (LMUL) parameter can lead to performance improvements of up to $3 x$, and leveraging knowledge of the vector length can further enhance LMUL optimization in compilers.

关键词： Embedded systems Instruction sets High performance computing parallel processing Vectors Hardware Registers Performance analysis Tuning Optimization

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Leveraging Ensemble Learning for Enhanced Sentiment Analysis

Leveraging Ensemble Learning for Enhanced Sentiment Analysis

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distributed Computing and Optimization techniques (ICDCOT), international conference on

作者： Adrish Bhowmic Souvik Parua Debayan Mandal Bishal Kumar Ghosh Raja Karmakar Computer Science and Engineering Techno International New Town Kolkata India Computer Science and Engineering (IOT & CS) Heritage Institute of Technology Kolkata India

Sentiment analysis assesses emotions in textual data, vital in applications like product reviews and social media. Ensemble learning enhances sentiment analysis by integrating diverse algorithms, improving predictive accuracy and robustness in interpretation. This work meets the growing demand for robust sentiment interpretation tools by combining ensemble learning with sentiment analysis. Ensemble methods integrate diverse learning algorithms, crucial in assessing textual emotions, particularly in areas like product reviews and social media within natural language processing (NLP). Our contribution involves developing tailored artificial neural network (ANN) sentiment classifiers proficient in capturing intricate language sequences for quality reviews. Leveraging the IMDB movie reviews dataset, our approach demonstrates effectiveness through various evaluation metrics. Utilizing k-nearest neighbour (KNN), Random Forest, and Naive Bayes algorithms, along with k-means clustering and regressive learning, we employ ensemble learning to analyze sentiments in online reviews. This effectively addresses the resource-intensive retraining process in conventional sentiment analysis methods, resulting in accurate outcomes for emotional AI.

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The Stream Reasoning System I-DLV-sr: Enhancements and applications in Smart Cities 6th

The Stream Reasoning System I-DLV-sr: Enhancements and Appli...

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6th international Joint conference on Rules and Reasoning (RuleML+RR)

作者： Calimeri, Francesco Mastria, Elena Perri, Simona Zangari, Jessica Univ Calabria Dept Math & Comp Sci Arcavacata Di Rende Italy DLVSyst Srl Arcavacata Di Rende Italy

ISBN: (纸本)9783031215407;9783031215414

I-DLV-sr is a recently proposed logic-based system for reasoning over data streams, which relies on a framework enabling a tight, fine-tuned interaction between Apache Flink and the ASP system I-2-DLV. Flink enables distributed stream processing, whereas I2-DLV acts as full-fledged reasoner capable of transparently performing incremental evaluations. In this paper, we present a new and optimized version of I-DLV-sr that features an improved management of parallel computations and communications between Flink and I-2-DLV, along with new linguistic extensions aiming at allowing its effective application in smart city scenarios.

关键词： Stream reasoning Stream processing Knowledge representation and reasoning Answer set programming Smart city applications

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An Open-Source Virtualization Layer for CUDA applications

An Open-Source Virtualization Layer for CUDA Applications

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26th international conference on parallel and distributed Computing (Euro-Par)

作者： Eiling, Niklas Lankes, Stefan Monti, Antonello Rhein Westfal TH Aachen E ON Energy Res Ctr Inst Automat Complex Power Syst Aachen Germany

ISBN: (纸本)9783030715939;9783030715922

GPUs have achieved widespread adoption for High-Performance Computing and Cloud applications. However, the closed-source nature of CUDA has hindered the development of otherwise commonly used virtualization techniques. In this paper, we evaluate the feasibility of building a GPU virtualization layer that isolates the GPU and CPU parts of CUDA applications to achieve better control of the interactions between applications and the CUDA libraries. We present our open-source tool that transparently intercepts CUDA library calls and executes them in a separate process using remote procedure calls. This allows the execution of CUDA applications on machines without a GPU and provides a basis for the development of tools that require fine-grained control of the GPU resources, such as checkpoint/restore and job schedulers.

关键词： GPU Virtualization CUDA Remote execution

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IoT Based Designing of Secure Data Storage System in distributed Cloud System with Big Data using Cryptography Algorithm 11

IoT Based Designing of Secure Data Storage System in Distrib...

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11th international conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Shukla, Ravi Shankar College of Computing and Informatics Saudi Electronic University Department of Computer Science Saudi Arabia

ISBN: (纸本)9781665487320

The cloud system plays a versatile role in the storage of vast data for future references. This includes the delivery of the computing sources that includes the servers with storage, various databases, networking system and software analysis. The web based services provides various usages in versatile forms. The cloud computing helps in numerous pathways for the web based service networks. The security of the data and the privacy constraints are the important aspects involved in the cloud computing techniques. This includes the safeguarding the sensitive data with higher precision. This is accompanied with the intelligent cryptographic algorithm to obtain secure data in the cloud system. This helps to save the data through several data processing applications accompanied with the cryptography algorithm. The cryptography algorithm are enabled to obtain encrypt data so that to have secure communication links in the networking system. This helps in the separation of data with the prescribed files in the distributed cloud networks. The operational time is secured by the classification of data in the form of data packets. © 2022 IEEE.

关键词： distributed cloud

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UpDown: Combining Scalable Address Translation with Locality Control

UpDown: Combining Scalable Address Translation with Locality...

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High Performance Computing, Networking, Storage and Analysis, SC-W: Workshops of the international conference for

作者： Yuqing Wang Swann Perarnau Andrew A. Chien Department of Computer Science University of Chicago Chicago USA Argonne National Laboratory Chicago USA

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

Global shared memories of petabytes are increasingly useful for applications, but traditional page-based techniques do not scale (limited reach), and scalable techniques such as segments fail to provide needed locality control. We propose a novel two-level translation scheme, UpDown, that provides compact, efficient access control to distributed segments of arbitrary size and data layout control, solving problems of reach and data locality. UpDown’s novel two-level structure separates access control from data layout, allowing applications to manage locality cheaply, without privileged *** evaluate UpDown against page-based systems, using big data computations. Our results show that UpDown is scalable and provides effective global data locality management. UpDown’s translation states are an average of ~620 billion times smaller total and ~130 billion times smaller local translation state. Simulations with synthetic traces show that the two-level translation scheme enables fast, user-level management of data locality in a scalable parallel machine.

关键词： Access control Translation High performance computing conferences Computational modeling Layout distributed databases parallel machines Big Data Data models

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Experience Report on Teaching parallel and distributed Programming Through Storytelling

Experience Report on Teaching Parallel and Distributed Progr...

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Software Analysis, Evolution and Reengineering - Companion (SANER-C), IEEE international conference on

作者： Bogdan Iudean Computer Science Department Babes-Bolyai University Cluj-Napoca Romania

ISBN: (数字)9798350351576

ISBN: (纸本)9798350351583

Multi-core processors, cloud computing, and distributed applications now define the landscape of modern software development and research. Efficiently teaching multithreading concepts is vital to equip students for real-world industry challenges. Navigating the complexities of multithreading proves challenging for students due to the high level of abstraction and difficulty associated with these concepts. The complexity of these technological fields raises the necessity for a comprehensive educational approach that facilitates students to understand the multithreading functionalities and usages with ease. This experience paper details our teaching approach, utilizing narrative-driven methods in parallel and distributed programming laboratories. We emphasize the relevance, novelty, and benefits of integrating interactive storytelling into the tangled learning process of parallel and distributed programming concepts. Concurrency, parallelization, and workload distribution are explained through live narrations, accompanied by animated presentations following a procedural storyline that spans laboratories. Complex ideas are transformed into meaningful character interactions, like stone-age people training octopuses to make ice-cream concurrently. This unique approach aims not only to enhance comprehension but also to make learning a vibrant and memorable experience for students, bridging the gap between theory and practical application. Our report extracts valuable lessons from student activities, emphasizing the motivational impact of engaging narratives. Students reflect on the relevance of concepts, shifting focus from implementation to understanding. Beyond just facilitating and easier access to knowledge, our approach aims to raise a deeper appreciation for the intricate world of parallel and distributed programming domain by encouraging students to see beyond the technical depth.

关键词： Training Technological innovation Multithreading Navigation Multicore processing Laboratories Software

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