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Efficient SPARQL Query processing in MapReduce

Efficient SPARQL Query Processing in MapReduce

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Computational Intelligence Approaches and applications (ICCIAA), international conference on

作者： Zaid Momani Abdelraouf Ishtaiwi Ahmed Noori Rashid Department Data Science and Artificial Intelligence University of Petra Amman Jordan Department of Computer networking systems University of Anbar Ramadi Iraq

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Existing approaches to parallel SPARQL query processing in MapReduce often suffer from suboptimal parallelism, particularly when handling complex queries with multiple joins. Recent advancements have explored novel multiway join techniques, indexing mechanisms, and distributed graph systems to address these challenges. In this paper, we extend the discussion by incorporating state-of-the-art strategies such as adaptive query optimization and fine-grained partitioning approaches. Our enhanced methodology demonstrates how multiway joins can be efficiently implemented in a single MapReduce job by leveraging recent innovations in RDF storage and query processing. Experimental results validate the scalability and partial effectiveness of our proposed method on large-scale datasets, with notable improvements for specific query patterns over baseline approaches.

关键词： Performance evaluation Technological innovation Query processing Scalability Organizations Benchmark testing parallel processing Data processing Resource description framework Hardware

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Covariances computation in the Gaia AVU-GSR parallel Solver with I/O techniques: a performance study as a function of writing cycle length 33

Covariances computation in the Gaia AVU-GSR Parallel Solver ...

引用

33rd Euromicro international conference on parallel, distributed, and Network-Based processing, PDP 2025

作者： Cesare, Valentina Becciani, Ugo Vecchiato, Alberto Lattanzi, Mario Gilberto Aldinucci, Marco Bucciarelli, Beatrice National Institute for Astrophysics Astrophysical Observatory of Catania Italy Astrophysical Observatory of Turin National Institute for Astrophysics Italy University of Turin Department of Computer Science Italy

ISBN: (纸本)9798331524937

The solver module of the Astrometric Verification Unit Global Sphere Reconstruction (AVU GSR) pipeline aims to find the astrometric parameters of ∼108 stars in the Milky Way, the attitude and instrumental settings of the Gaia satellite, and the parametrized post Newtonian parameter γ with a resolution of 10-100 micro-arcseconds. To perform this task, the code, which runs in production on Leonardo CINECA infrastructure, solves a system of linear equations with the iterative LSQR algorithm, where the coefficient matrix is large (10-50 TB) and sparse and the iterations stop when least square convergence is reached. The solver was ported to GPU with CUDA, obtaining a ∼14x acceleration factor over an original version CPU-parallelized with OpenMP. This work concentrates on a code section dedicated to covariances calculation, representing an important scientific task for Gaia mission, since the problems unknowns present strong correlations. Given the number of unknowns at mission end, the variances-covariances matrix is expected to occupy ∼1 EB, which represents a substantial "Big Data"issue. To compute a subset of the total covariances, we defined an I/Obased pipeline made of two jobs. The first job, the LSQR, writes the files every itnCovCP iterations, and the second job reads them and calculates the corresponding covariances. The two jobs can be launched either in sequence or concurrently. Previous studies demonstrated that the covariances calculation does not significantly slowdown the AVU-GSR production up to ∼3 107 covariances. Here we investigate the performance of the covariances pipeline as a function of itnCovCP. The results show that writing smaller files more frequently or writing larger files less frequently does not affect the global performance of the solver, whose speed only depends on the number of covariances to calculate and of system unknowns. © 2025 IEEE.

关键词： Covariance matrix

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Relevance-Based Multi-User Data Compression for Fronthaul Rate Reduction in Cell-Free Massive MIMO Systems 14

Relevance-Based Multi-User Data Compression for Fronthaul Ra...

引用

14th international ITG conference on Systems, Communications and Coding, SCC 2025

作者： Danaee, Alireza Hassanpour, Shayan Wubben, Dirk Dekorsy, Armin University of Bremen Department of Communications Engineering Germany

ISBN: (纸本)9798331522896

In the Cell-Free massive Multiple-Input MultipleOutput (CF-mMIMO) systems, a large number of distributed users are simultaneously served by multiple Radio Access Points (RAPs). In the uplink, each RAP receives noisy observations from several users and must locally compress these signals before forwarding them to the corresponding Central processing Unit (CPU) via multiple fronthaul channels, each subject to a rate limitation. The challenge is to design the compressed signals at the RAPs such that the received signals at the CPU retain as much information as possible about the users (to be retrieved). To address this, we adopt compression techniques based on the Information Bottleneck (IB) principle to design local quantizers at the RAPs by ensuring an efficient balance between the informativity and compactness of the compressed signals. We discuss here two different compression schemes: one that processes the signals independently across fronthaul links and another that leverages the side information from previously retrieved signals at the CPU. By using side information, the latter generally provides a better tradeoff between the compression efficiency and performance, albeit with an increased complexity. Through numerical simulations, we demonstrate the effectiveness of both IB-based schemes compared to the conventional compression methods, showing their potential for improving fronthaul rate efficiency and overall system performance in typical digital transmission scenarios. © 2025 IEEE.

关键词： parallel processing systems

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DaCP: Accelerating Synchronization-Free SpTRSV via GPU-Friendly Data Communication and parallelism Strategies 20th

DaCP: Accelerating Synchronization-Free SpTRSV via GPU-Frie...

引用

20th IFIP WG 10.3 international conference on Network and parallel Computing, NPC 2024

作者： Guo, Mingfeng Deng, Liang Dai, Zhe Li, Ruitian Lin, Gaofeng Liu, Jie Computational Aerodynamics Institute China Aerodynamics Research and Development Center Mianyang China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9789819628292

Sparse triangular solve (SpTRSV) is a vital component in various scientific applications, and numerous GPU-based SpTRSV algorithms have been proposed. Synchronization-free SpTRSV is currently the mainstream algorithm on GPU due to its short preprocessing time and outstanding performance. However, we observed that this algorithm still has two performance bottlenecks. Firstly, the thread-level parallel mode can introduce to thread divergence issues within GPU warps during the writing phase. Secondly, the thread-level and warp-level fusion mode may struggles to fully exploit GPU resources due to suboptimal mapping relationships between rows and threads. To address these issues, this paper proposes DaCPSpTRSV, a new synchronization-free algorithm with GPU-friendly data communication and parallelism strategies. Specifically, we first develop a fast-forward thread-level approach, incorporating an efficient global memory access pattern and a light-weight dependency control mechanism, to optimize data communication and alleviate thread divergence. A fine-grained fusion strategy is then proposed to maximize GPU parallelism by adaptively selecting the suitable thread-level or warp-level modes. Moreover, the commonly-used compressed sparse row (CSR) format is employed in our DaCPSpTRSV, enhancing the versatility of our algorithm. We evaluate our approach using 245 matrices from the SuiteSparse Matrix Collection on two NVIDIA GPUs, demonstrating speedup ratios of up to 4.77×, 4.94×, 1.67×, and 1.62× compared to cuSPARSE, Sync-Free, CapelliniSpTRSV, and YuenyeungSpTRSV, respectively. The project is open-sourced at https://***/gmfff12334/DaCP. © IFIP international Federation for Information processing 2025.

关键词： Graphics processing unit

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Fast Maximal Independent Sets on Dynamic Graphs

Fast Maximal Independent Sets on Dynamic Graphs

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Prajjwal Nijhara Aditya Trivedi Dip Sankar Banerjee Department of Computer Science and Engineering Indian Institute of Technology Jodhpur NH62 Karwar Jodhpur Rajasthan India

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Finding the Maximal Independent Set (MIS) in a graph is a well-known problem with applications in resource allocation, load balancing, and routing optimization. This task is particularly challenging for large graphs as it requires multiple iterations over the entire set of vertices. Recently, there has been significant interest in developing techniques to maintain the MIS dynamically in evolving graphs rather than re-computing from scratch. In this paper, we propose new data structures and techniques for computing MIS in parallel on dynamic graphs. We specifically propose techniques to handle insertions and deletions in a batched setting. We conducted detailed experiments on shared memory multicore CPUs using graphs ranging from 50 million to ${1. 2}$ billion edges. Our results show that using our technique for insertions and deletions can provide up to 15.64x and 10.57x speedups on average over comparable baselines. Additionally, the final MIS we produce varies by only about ${0. 1 8 \%}$ in cardinality compared to the existing state-of-the-art.

关键词： Multicore processing Scalability Graphics processing units Routing Load management Data structures Real-time systems Distance measurement Resource management Optimization

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Out-of-Memory GPU Sorting Using Asynchronous CUDA Streams 25th

Out-of-Memory GPU Sorting Using Asynchronous CUDA Streams

引用

25th international conference on parallel and distributed Computing, applications and Technologies, PDCAT 2024

作者： HoseinyFarahabady, MohammadReza Zomaya, Albert Y. School of Computer Science Center for Distributed and High Performance Computing The University of Sydney SydneyNSW Australia

ISBN: (纸本)9789819642069

Graphics processing Units (GPUs) are widely used as powerful hardware accelerators for data-intensive tasks. However, their efficacy can be hindered by constraints in device memory and data transfer speeds via the PCI-E link. This paper explores the use of GPUs for executing scalable sorting operations that exceeds the GPU’s global memory by a factor of at least two orders of magnitude. To address this challenge, we leverage the power of NVIDIA’s asynchronous CUDA streams. Our solution enhances GPU performance by implementing a pipeline model to offload sorting operations from the Solid-State Drive (SSD) to GPU memory via the host’s main memory. This strategy maximizes the multiway concurrency level to reduce the overhead associated with data transfer within the GPU memory hierarchy. We perform extensive performance evaluations by sorting 128 billion data elements initially stored on an external NVMe SSD. The results confirm the effectiveness of our approach, showcasing a speed-up of 4.3× compared to the results for CPU-based out-of-memory sorting of skewed data based on Intel’s Threading Building Blocks (TBB) sorting algorithm. Our results include the cost of transfer times to and from the main memory, a factor often overlooked in similar studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graphics processing unit

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Low-Carbon Geographically distributed Cloud-Edge Task Scheduling 24th

Low-Carbon Geographically Distributed Cloud-Edge Task Schedu...

引用

24th international conference on Algorithms and Architectures for parallel processing, ICA3PP 2024

作者： Zhu, Yingjie Qi, Ji Wang, Zehao Wei, Shengjie Chen, Yan Cao, Tuo Luo, Gangyi Qian, Zhuzhong State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Software Technology Co. Ltd. Jiangsu Suzhou China

ISBN: (纸本)9789819615506

Edge computing is a rapidly developing research area known for its ability to reduce latency and improve energy efficiency, and it also has a potential for green computing. Many geographically distributed edge servers are powered by renewable energy sources, due to the difficulties of using traditional power supplies or because of advancements in energy harvesting technologies. These green edge servers can cut down carbon emissions by processing tasks locally, but the inherent limitations of their computing capacity result in some tasks having to be uploaded to a data center to meet service-level agreement (SLA) requirements. To further reduce carbon emissions in cloud-edge systems, scheduling tasks to those low-carbon data centers while meeting latency constraints is highly beneficial. In this paper, we propose a low-carbon cloud-edge scheduling algorithm that utilizes Lyapunov optimization techniques and Markov approximation to address the long-term optimization problem of carbon emissions. Our algorithm guarantees provable performance, and simulation results demonstrate its effectiveness in striking a balance between carbon emissions and task latency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Carbon sequestration

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A Multi-Level parallel Algorithm for Detection of Single Scatterers in SAR Tomography

A Multi-Level Parallel Algorithm for Detection of Single Sca...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Massimiliano Russo Mehwish Nisar Antonio Pauciullo Pasquale Imperatore Marco Lapegna Diego Romano Dept. of Mathematics and Applications University of Naples Federico II Naples Italy Institute for the Electromagnetic Sensing of the Environment (IREA) National Research Council Naples Italy Inst. for High Performance Computing and Networking National Research Council Naples Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Synthetic Aperture Radar (SAR) tomography is an advanced technique for monitoring deformations of the Earth’s surface. However, the computational complexity of SAR tomography algorithms often restricts their application to large-scale datasets. To address this issue, we introduce a multi-level parallel implementation of a single scatterer detection algorithm specifically designed to exploit the capabilities of modern heterogeneous High-Performance Computing (HPC) systems. By efficiently distributing the computational workload at different levels across multiple processing units, our parallel approach significantly reduces processing time, facilitating the analysis of extensive SAR datasets. We assess the performance of our parallel implementation using real-world SAR data, showcasing its effectiveness in enhancing both the efficiency and scalability of SAR tomography. Our work contributes to advancing remote sensing techniques and offers valuable insights into the application of HPC for large-scale environmental monitoring.

关键词： High performance computing Scalability Graphics processing units Tomography Performance analysis parallel algorithms Synthetic aperture radar Surface treatment Remote sensing Optimization

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CTXNL: A Software-Hardware Co-designed Solution for Efficient CXL-Based Transaction processing 25

CTXNL: A Software-Hardware Co-designed Solution for Efficien...

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30th ACM international conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2025

作者： Wang, Zhao Chen, Yiqi Li, Cong Guan, Yijin Niu, Dimin Guan, Tianchan Du, Zhaoyang Wei, Xingda Sun, Guangyu School of Integrated Circuits School of Computer Science Peking University Beijing China Peking University School of Integrated Circuits Beijing China Damo Academy Alibaba Group Hupan Lab Hangzhou China Shanghai Jiao Tong University Institute of Parallel and Distributed Systems Seiee Shanghai China School of Integrated Circuits Beijing Advanced Innovation Center for Integrated Circuits Peking University Beijing China

ISBN: (纸本)9798400710797

Transaction processing systems are the crux for modern data-center applications, yet current multi-node systems are slow due to network overheads. This paper advocates for Compute Express Link (CXL) as a network alternative, which enables low-latency and cache-coherent shared memory accesses. However, directly adopting standard CXL primitives leads to performance degradation due to the high cost of maintaining cross-node cache coherence. To address the CXL challenges, this paper introduces CTXNL, a software-hardware co-designed system that implements a novel hybrid coherence primitive tailored to the loosely coherent nature of transactional data. The core innovation of CTXNL is empowering transaction system developers with the ability to selectively achieve data coherence. Our evaluations on OLTP workloads demonstrate that CTXNL enhances performance, outperforming current network-based systems and achieves up to 2.08x greater throughput than vanilla CXL memory sharing architectures across universal transaction processing policies. © 2025 ACM.

关键词： Cache memory

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Application of Digital Method for processing distributed Digital Linguistic Resources

Application of Digital Method for Processing Distributed Dig...

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international conference on Applied Innovations in IT, ICAIIT 2024

作者： Nadutenko, Maksym Nadutenko, Margaryta Semenog, Olena Fast, Olha of Ukraine Kyiv Ukraine Department of Ukrainian Language and Literature of Sumy A.S Makarenko State Pedagogical University Sumy Ukraine Municipal Higher Educational Institution "Lutsk Pedagogical College" of the Volyn Regional Council Lutsk Ukraine

ISBN: (纸本)9783031892950

This article addresses the challenge of effectively processing and utilizing distributed digital linguistic resources to enhance the teaching of philological disciplines. Current efforts emphasize the application of digital methods, including statistical, corpus-based, and lexicographic techniques, integrated with artificial intelligence (AI) tools, such as machine learning and neural networks. The proposed solution centers on the development of a virtual lexicographic laboratory, the "Multimedia Dictionary of Infomedia Literacy," which offers an innovative digital approach to language education, linguistic research, and telecommunication applications. The primary idea is to employ these digital and AI-driven methods to analyze and organize vast amounts of linguistic data, making them accessible and useful for educational purposes. This approach has been tested in educational environments, demonstrating effectiveness in advancing academic resilience, particularly among doctoral students engaged in philological research. Key results indicate that the digital methods facilitate deeper engagement with language content, offering tools that adapt to individual learning needs and enhance the processing of large datasets. Case studies, including the Science4Brave Cluster, highlight the role of these tools in fostering critical thinking and academic resilience in times of crisis. Significantly, the laboratory’s tools have shown value in telecommunication by supporting the transmission and analysis of linguistic information, adapting content for efficient communication across digital platforms. This work underscores the potential of digital and AI-enhanced methods in processing linguistic resources, advancing language learning, and contributing to the broader discourse on the role of technology in modern linguistics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cluster analysis

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