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Adaptive Recursive Query Optimization

Adaptive Recursive Query Optimization

引用

international conference on Data Engineering

作者： Anna Herlihy Guillaume Martres Anastasia Ailamaki Martin Odersky EPFL Switzerland

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Performance-critical industrial applications, including large-scale program, network, and distributed system analyses, are increasingly reliant on recursive queries for data analysis. Yet traditional relational algebra-based query optimization techniques do not scale well to recursive query processing due to the iterative nature of query evaluation, where relation cardinalities can change unpredictably during the course of a single query execution. To avoid error-prone cardinality estimation, adaptive query processing techniques use runtime information to inform query optimization, but these systems are not optimized for the specific needs of recursive query processing. In this paper, we introduce Adaptive Metaprogramming, an innovative technique that shifts recursive query optimization and code generation from compile-time to runtime using principled metaprogramming, enabling dynamic optimization and re-optimization before and after query execution has begun. We present a custom join-ordering optimization applicable at multiple stages during query compilation and execution. Through Carac, a custom Datalog engine, we evaluate the optimization potential of Adaptive Metaprogramming and show unoptimized recursive query execution time can be improved by three orders of magnitude and hand-optimized queries by 6x.

关键词： Runtime Adaptive systems Codes Query processing Estimation Real-time systems Iterative methods

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Improving Space and Time Efficiency in Hadoop Architecture Using Machine Learning Algorithms

Improving Space and Time Efficiency in Hadoop Architecture U...

引用

IoT Based Control Networks and Intelligent Systems (ICICNIS), international conference on

作者： Murali Vivekanandam Kiran Karasala Aptimized

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

This research study explores the application of Hadoop distributed File System (HDFS), MapReduce, and Machine Learning algorithms, along with Social Spider Optimization (SSO), to effectively analyze and utilize large volumes of unstructured data generated by social media platforms. The study focuses on analyzing user behavior and preferences within the context of online shopping, considering individual and business perspectives. The research proposes novel approaches, including the derby model, virtual local file system, a refined recommender system, and parallelized SSO, to efficiently handle various data formats and improve the performance of data processing tasks. The proposed methods demonstrate improved efficiency in terms of space and time requirements compared to existing approaches. This research contributes to the advancement of big data analytics techniques and has the potential to revolutionize how businesses understand and leverage user behavior in the digital age.

关键词： Training Machine learning algorithms File systems Computational modeling Predictive models parallel processing Boosting Data models Load modeling Business

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A Mathematical Framework for Optimizing Lexical Analysis on Multi-Core Architectures

A Mathematical Framework for Optimizing Lexical Analysis on ...

引用

Sustainable Expert Systems (ICSES), international conference on

作者： Vaikunta Pai T Priyadarshini P B Sunil Kamath Shreyas Vivek Manjunatha Nethravathi P S Department of ISE NITTE (Deemed to be University) NMAM Institute of Technology (NMAMIT) Nitte India Department of Computer Applications St Joseph Engineering College Mangaluru India School of Information Technology Deakin University Waurn Ponds VIC Australia Department of CSE Manipal Institute of Technology Manipal Academy of Higher Education (MAHE) Manipal India Department of CSE Shree Devi Institute of Technology Mangaluru India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

In the field of multi-core computing systems, optimizing the distribution of tasks across multiple cores is essential to harness maximum performance potential. Traditional static task allocation methods, often employed in lexical analysis-the initial and vital phase of compiling and interpreting source code-frequently result in imbalanced workloads and suboptimal resource utilization. This paper introduces an innovative and sophisticated mathematical model for dynamic task allocation specifically tailored to lexical analysis, aiming to revolutionize how tasks are distributed across multiple cores. By employing advanced dynamic allocation techniques, our proposed model not only ensures balanced workload distribution but also scales efficiently with varying computational demands. This novel approach significantly enhances the efficiency of lexical analysis in multi-core environments, leading to substantial improvements in processing times and resource utilization. Extensive experimental results validate the superiority of our method, showcasing dramatic reductions in processing latency and a more effective exploitation of multi-core capabilities. This dynamic model not only advances the field of lexical analysis but also sets a new benchmark for task allocation strategies in multi-core computing systems, promising broader applicability and future innovations in compiler design and resource management.

关键词： Adaptation models Analytical models Multicore processing Computational modeling Scalability parallel processing Dynamic scheduling Mathematical models Computational efficiency Resource management

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Design and Implementation of a Real-Time distributed Precise Point Positioning Platform 16th

Design and Implementation of a Real-Time Distributed Precise...

引用

16th international conference on Wireless Algorithms, Systems, and applications (WASA)

作者： Ding, Jian Wu, Dingcheng Xu, Xueyong Xu, Hongli Huang, Liusheng Univ Sci & Technol China Dept Comp Sci & Technol Hefei Peoples R China North Informat Control Res Acad Grp CO LTD Nanjing Peoples R China

ISBN: (纸本)9783030861377;9783030861360

For Global Navigation Satellite System (GNSS) data processing, voluminous real-time Continuously Operating Reference Stations (CORS) data processing is a challenging problem. There are many methods have been proposed for regional network processing, such as parallel computing. However, they are mainly used for post-processing or near real-time processing. Due to the magnitude-increased and epoch-related of large geographic area CORS data processing, it brings huge challenges for real-time reception and efficient processing. Therefore, a real-time distributed Precise Point Positioning (PPP) platform is designed based on the idea of distributed computing and message queue to solve voluminous real-time CORS data processing, and it decomposed the real-time data processing into three processes: Input/output (I/O) multiplexing for real-time stream data acquisition, parallel PPP computing, and Weight Round Robin task scheduling. The real-time data of 5 international GNSS Service (IGS) stations is processed, the results show that it generally takes 30 min to achieve accuracy within centimeter. When the platform is applied for 1414 CORS real-time data processing in China, it can perform PPP calculations with stability and high precision. Application of the real-time Precipitable Water Vapor (PWV) monitoring is also provided.

关键词： GNSS Precise Point Positioning distributed computing PWV

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Evaluating the Soft Error Resilience of Graph applications on GPGPUs

Evaluating the Soft Error Resilience of Graph Applications o...

引用

IEEE international conference on Big Data Security on Cloud (BigDataSecurity), High Performance and Smart Computing (HPSC) and Intelligent Data and Security (IDS)

作者： Xiaohui Wei Mengting Zhou Nan Jiang Hengshan Yue College of Computer Science and Technology Jilin University Changchun China

ISBN: (数字)9798350389524

ISBN: (纸本)9798350389531

General-Purpose Graphics processing Units (GPGPUs) are widely utilized for graph processing thanks to their high throughput, massive parallelism and powerful computing capacity. However, due to the increasing integration, GPGPUs are susceptible to soft errors, which can undermine the reliability of graph applications accelerated using GPGPUs. Typically, fault tolerance strategies such as thread replication and checkpoint mechanism are applied to ensure the reliability of program execution. However, these techniques require appropriate trade-off between reliability improvement and overhead. To this end, it is imperative to understand the error resilience profile of graph applications. In this paper, we construct a multidimensional analysis framework, which analyzes the error resilience of graph applications on GPGPUs from application, kernel function, and thread perspective. Based on the exhaustive statistical fault injection (FI) results for four graph algorithms, we propose heuristic suggestions as guidance for efficient fault tolerance strategies.

关键词： Fault tolerance Instruction sets Heuristic algorithms Fault tolerant systems Graphics processing units parallel processing Throughput

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Leveraging CMAESAO for Optimal distributed Generation Placement and Sizing in Modern Power Systems

Leveraging CMAESAO for Optimal Distributed Generation Placem...

引用

international conference on Signal processing, Communication, Power and Embedded System (SCOPES)

作者： Smrutirekha Mahanta Manoj Kumar Maharana School of Electrical Engineering KIIT Deemed to be University Bhubaneswar Odisha India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

This study presents a comprehensive analysis of optimal placement and sizing methods for distributed generation (DG) sources, essential for addressing rising power demand and transmission constraints in modern power systems. The effective integration of DGs into the grid enhances reliability, reduces losses, and improves overall performance. This paper focuses on leveraging the Covariance Matrix Adaptation Evolution Strategy with Aquila Optimization (CMAESAO) technique, a powerful optimization approach for determining the most effective DG placement and sizing. A systematic review of various methodologies, including symbiotic search algorithms, particle swarm optimization, and stochastic fractal search algorithms is provided to contextualize the CMAESAO technique. Each optimization method is evaluated based on its strengths, practical applications, and effectiveness in optimizing DG deployment. By contrasting these techniques, the research aims to elucidate the advantages of the CMAESAO method in enhancing DG integration scenarios. The findings contribute valuable insights into the optimization of distributed generation systems, paving the way for more efficient and reliable energy distribution in future power networks. This study ultimately underscores the importance of advanced optimization strategies in improving the performance and sustainability of modern electrical systems.

关键词： Symbiosis Accuracy Stochastic processes distributed power generation Power system reliability Reliability Covariance matrices Optimization Standards Systematic literature review

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GPU Acceleration for Data processing and Analytics

GPU Acceleration for Data Processing and Analytics

引用

IEEE international conference on Big Data

作者： Qiong Luo HKUST and HKUST Guangzhou China

Graphics processing units, or GPUs, are widely employed as hardware accelerators in various applications, such as algorithmic trading, computer vision, and large language model training. In particular, NVIDIA’s GPUs, together with its Compute-Unified Device Architecture (CUDA) interface, provide a massively parallel platform for general-purpose computing. However, it is often challenging to accelerate data processing and analytical tasks on the GPU when they are irregular and do not match well the GPU architecture or programming paradigm. In this talk, I will discuss general methodologies as well as specific design and implementation techniques on using the GPU to accelerate such tasks, and compare them with CPU-based solutions. With the prevalence of GPU-equipped computing resources and big data applications of increasing complexity and scale, more opportunities and challenges will arise in this space.

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ASimulated Logistic Algorithm for distributed Flexible Assembly Flowshop Scheduling with Post-processing

ASimulated Logistic Algorithm for Distributed Flexible Assem...

引用

international Symposium on Industrial Electronics (INDEL)

作者： Haiqiang Hao Haiping Zhu School of Mechanical Science & Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350352320

ISBN: (纸本)9798350352337

An optimized mathematical model has been developed to streamline gas turbine production by targeting the minimization of the overall completion time, known as makespan. The production process is broken down into a series of stages: jobs are processed across multiple parallel factories, assembled into semi-finished goods with flexibility in their assembly order, and then transformed into final products through various stages of additional processing. To initiate solutions, a three-tier composite heuristic is employed, supplemented by six specialized neighborhood structures devised according to the different stages of the problem. Drawing inspiration from natural population growth mechanisms, we introduce a Simulated Logistic Algorithm (SLA) designed for robust solution enhancement. Comparative experiments on both small and extensive scales demonstrate the SLA's superior performance over established optimization techniques such as Particle Swarm Optimization, Tabu Search, and Genetic Algorithms, especially notable in large-scale applications. Thorough mathematical and statistical evaluations reinforce the efficacy of the SLA and validate the proposed approach.

关键词： Job shop scheduling Heuristic algorithms Particle swarm optimization Assembly Optimization Turbines Tuning Logistics Genetic algorithms Testing

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PaC-Trees: Supporting parallel and Compressed Purely-Functional Collections 2022

PaC-Trees: Supporting Parallel and Compressed Purely-Functio...

引用

43rd ACM SIGPLAN international conference on Programming Language Design and Implementation (PLDI)

作者： Dhulipala, Laxman Blelloch, Guy E. Gu, Yan Sun, Yihan Univ Maryland College Pk MD 20742 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA UC Riverside Riverside CA USA

ISBN: (纸本)9781450392655

Many modern programming languages are shifting toward a functional style for collection interfaces such as sets, maps, and sequences. Functional interfaces offer many advantages, including being safe for parallelism and providing simple and lightweight snapshots. However, existing high-performance functional interfaces such as PAM, which are based on balanced purely-functional trees, incur large space overheads for large-scale data analysis due to storing every element in a separate node in a tree. This paper presents PaC-trees, a purely-functional data structure supporting functional interfaces for sets, maps, and sequences that provides a significant reduction in space over existing approaches. A PaC-tree is a balanced binary search tree which blocks the leaves and compresses the blocks using arrays. We provide novel techniques for compressing and uncompressing the blocks which yield practical parallel functional algorithms for a broad set of operations on PaC-trees such as union, intersection, filter, reduction, and range queries which are both theoretically and practically efficient. Using PaC-trees we designed CPAM, a C++ library that implements the full functionality of PAM, while offering significant extra functionality for compression. CPAM consistently matches or outperforms PAM on a set of microbenchmarks on sets, maps, and sequences while using about a quarter of the space. On applications including inverted indices, 2D range queries, and 1D interval queries, CPAM is competitive with or faster than PAM, while using 2.1-7.8x less space. For static and streaming graph processing, CPAM offers 1.6x faster batch updates while using 1.3-2.6x less space than the state-of-the-art graph processing system Aspen.

关键词： purely-functional data structures parallel data structures space-efficient data structures

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Developing a new modified Algorithms for Digital Image Sampling and Quantization in Serial and parallel Image processing

Developing a new modified Algorithms for Digital Image Sampl...

引用

Paradigm Shift in Information Technologies with Innovative applications in Global Scenario (ICPSITIAGS), IEEE international conference on

作者： Jayashankari J Shalini V Megha Pandey Swetha S Mohit Kumar Sharma Jatin Khurana Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune INDIA Maharishi School of Engineering and Technology Maharishi University of Information Technology U P India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India Department of Electrical Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology University Institute of Engineering and technology Chitkara University Punjab India

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

The improvement of a new changed algorithms for virtual photograph sampling and quantization in serial and parallel image processing is proposed. The set of rules improves upon existing methods with the aid of combining picture sampling, quantization, and parallelization techniques with an efficient, in-region serial photograph processing framework. The ensuing algorithm has several advantages, which includes more accurate and steady photograph output, quicker convergence fees, and flexibility in process scheduling. The set of rules is nicely-suitable to parallel computing environments, together with clusters and multi-center structures. Via widespread experimentation and simulation, the proposed set of rules is located to be effective and to outperform present techniques. Moreover, the performance of the proposed set of rules changed into further confirmed thru a comparison to present methods, and the parameters of the version had been tuned in order to optimize overall performance.

关键词： Quantization (signal) Processor scheduling Digital images Noise Green products parallel processing Image sampling

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