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Efficient OLAP query processing across cuboids in distributed data warehousing environment

EXPERT systems WITH APPLICATIONS 2024年 239卷

作者： Roy, Santanu Raj, Saikat Chakraborty, Tamal Chakrabarty, Anirban Cortesi, Agostino Sen, Soumya Future Inst Engn & Management Kolkata India Ca Foscari Univ DAIS Venice Italy Univ Calcutta Kolkata India

This research work introduces a novel approach to enhance the performance of distributed data warehouses. Distribution of data has been done across multiple data center for the underlying data warehouse to ensure parallel processing that improves the performance of the system. The proposed system focuses on data availability locally, resulting in faster query processing times. In order to further optimize the query processing and to alleviate the centralized data warehouse load, the study suggests distributing the cuboids at different sites to maintain the lattice of cuboids logically. Algorithms have been proposed to execute OLAP aggregation operations that significantly enhanced query response times in distributed data warehouse. Empirical tests on real-world datasets proved the method's effectiveness, showcasing its superiority in terms of reduced OLAP query execution time, enhanced space efficiency, minimized network transmission delays, reduced bandwidth requirements and more predictable and stable data transmission compared to existing approaches.

关键词： distributed data warehouse OLAP Lattice of cuboids Hyper-lattice of cuboids Load balancing

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Modeling the Training Iteration time for Heterogeneous distributed Deep Learning systems

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INTERNATIONAL JOURNAL OF INTELLIGENT systems 2023年第1期2023卷

作者： Zeng, Yifu Chen, Bowei Pan, Pulin Li, Kenli Chen, Guo Changsha Univ Coll Comp Sci & Engn Changsha 410022 Hunan Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China

distributed deep learning systems effectively respond to the increasing demand for large-scale data processing in recent years. However, the significant investment in building distributed learning systems with powerful computing nodes places a huge financial burden on developers and researchers. It will be good to predict the precise benefit, i.e., how many times of speedup it can get compared with training on single machine (or a few), before actually building such big learning systems. To address this problem, this paper presents a novel performance model on training iteration time for heterogeneous distributed deep learning systems based on the characteristics of the parameter server (PS) system with bulk synchronous parallel (BSP) synchronization style. The accuracy of our performance model is demonstrated by comparing real measurement results on TensorFlow when training different neural networks with various kinds of hardware testbeds: the prediction accuracy is higher than 90% in most cases.

关键词： Learning systems

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parallel Implementation of Decision Tree for Online Fraud Detection using OpenMP, MPI and CUDA

Parallel Implementation of Decision Tree for Online Fraud De...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Dhanushkodi S Christopher Columbus C Muhammad Kifayathullah Rajeev Sekar Suryakumar P School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

Online fraud detection is critical for ensuring the security of digital transactions. However, the growing volume of data and the need for real-time processing pose significant computational challenges. Traditional Decision Tree algorithms, commonly used for classification tasks, struggle to handle large datasets in real-time scenarios due to their computational complexity. This research addresses these limitations by parallelizing the Decision Tree algorithm to enhance its efficiency for online fraud detection. We leverage three parallel programming models—OpenMP for multi-core CPU processing, MPI for distributed systems, and CUDA for GPU acceleration to optimize the construction and evaluation phases of the Decision Tree. By distributing the computational workload across these architectures, we aim to reduce execution times while maintaining high classification accuracy significantly. A thorough performance evaluation compares the parallel implementations to traditional serial approaches, focusing on execution speed, scalability, and detection accuracy. The results reveal substantial improvements in processing speed, enabling real-time fraud detection without sacrificing accuracy. These findings provide valuable insights into developing faster, scalable, and more reliable fraud detection systems, contributing to enhanced security for financial transactions in an increasingly digital world.

关键词： Technological innovation Accuracy Graphics processing units Signal processing algorithms Signal processing real-time systems Fraud Classification algorithms Decision trees Security

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A Message Passing Interface Library for High-Level Synthesis on Multi-FPGA systems 15

A Message Passing Interface Library for High-Level Synthesis...

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15th IEEE International Symposium on Embedded Multicore/Many-Core systems-on-Chip, MCSoC 2022

作者： Hironaka, Kazuei Iizuka, Kensuke Amano, Hideharu Keio University Dept. of Information and Computer Science Yokohama Japan

ISBN: (纸本)9781665464994

One obstacle to application development on multi-FPGA systems with high-level synthesis (HLS) is a lack of support for a programming interface. Implementing and debugging an application on multiple FPGA boards is difficult without a standard interface. Message Passing Interface (MPI) is a standard parallel programming interface commonly used in distributed memory systems. This paper presents a tool-independent MPI library called FiC-MPI that can be used in HLS for multi-FPGA systems in which each FPGA node is connected directly. By using FiC-MPI, various parallel software, including a general-purpose benchmark, can be easily implemented. FiC-MPI was implemented and evaluated on the M-KUBOS cluster consisting of Zynq MPSoC boards connected with a static time-division multiplexing network. By using the FiC-MPI simulator, parallel programs can be debugged before implementing on real machines. As a case study, the Himeno-BMT benchmark was implemented with FiC-MPI. It achieved 178.7 MFLOPS with a single node and scaled to 643.7 MFLOPS with four nodes, and 896.9 MFLOPS with six nodes of the M-KUBOS cluster. Through the implementation, the easiness of developing parallel programs with FiC-MPI on multi-FPGA systems was demonstrated. © 2022 IEEE.

关键词： parallel programming

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A scalable, distributed framework for significant subgroup discovery

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KNOWLEDGE-BASED systems 2024年 284卷

作者： Jyoti Kailasam, Sriram Buzmakov, Aleksey Indian Inst Technol Sch Comp & Elect Engn Mandi 175005 Himachal Prades India Natl Inst Technol Dept Comp Sci & Engn Warangal 506004 Telangana India Natl Res Univ Higher Sch Econ Perm 614070 Russia

Subgroup discovery is a supervised data mining technique having many applications in medical domains, market basket analysis, and social media analysis. It helps in mining subgroups (or patterns) with a high association to a target property, measured using a quality function. However, the process is computationally intensive as it is necessary to go through the search space of all subgroups to find the top-k interesting ones w.r.t. the quality function. Further, as we verify many associations, it is quite possible that a certain level of association might be achieved by chance. To address this issue, the state-of-the-art TopKWY algorithm employs permutation testing to control false discoveries. Still, testing multiple subgroups against thousands of permuted target labels further increases computational complexity. Additionally, TopKWY is limited to a specific quality function and lacks a parallel/distributed implementation to handle scalability challenges. In this paper, we propose a parallel and distributed framework for subgroup discovery named ParaDiS that extends permutation testing to a broader class of quality functions. ParaDiS scales to large datasets while effectively controlling the false discovery rate. It features different optimizations to reduce communication/computation overheads and a distributed best-first search strategy to improve pruning across different workers. We compare its performance for several real-world datasets and achieve an order of magnitude reduction in the execution time compared to the sequential approach.

关键词： distributed subgroup discovery Irregular structure Significant subgroup Closed pattern

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real-time Traffic Congestion Detection for Driver-Centric Applications

Real-Time Traffic Congestion Detection for Driver-Centric Ap...

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International Conference on distributed Computing systems workshop

作者： Philipp Kisters Tim Bauer Wolf Posdorfer Janick Edinger Distributed Operating Systems Universität Hamburg Hamburg Germany Mercedes-Benz Group AG Stuttgart Germany Computer Networks Universität Hamburg Hamburg Germany

With the increasing time drivers spent behind the wheel, availability of accurate, reliable, and comprehensive information on real-time traffic conditions is becoming increasingly important for individual road users, industry, and traffic authorities. Due to a growing amount of traffic combined with insufficient transportation infrastructure, traffic congestion is one of the major challenges modern society has to face in urban areas. While this results in serious environmental issues such as pollution, congestions also have significant effects on drivers’ stress levels, which further is an essential factor in driver behavior and road safety. In this paper, we analyze real-time traffic situations on a fine-granular level by computing the local congestion factor, which represents traffic conditions on currently traversed route segments. We show that travel time predictions for short- and mid-term routes provided by four major public traffic information providers can be used for computing congestion factors in real-time, which can then be used as input for driver-centric applications.

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Microsecond-scale Preemption for Concurrent GPU-accelerated DNN Inferences 16

Microsecond-scale Preemption for Concurrent GPU-accelerated ...

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16th USENIX Symposium on Operating systems Design and Implementation (OSDI)

作者： Han, Mingcong Zhang, Hanze Chen, Rong Chen, Haibo Shanghai Jiao Tong Univ SEIEE Inst Parallel & Distributed Syst Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China Minist Educ Engn Res Ctr Domain Specif Operating Syst Shanghai Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781939133281

Many intelligent applications like autonomous driving and virtual reality require running both latency-critical and best-effort DNN inference tasks to achieve both real time and work conserving on GPU. However, commodity GPUs lack efficient preemptive scheduling support and state-of-the-art approaches either have to monopolize GPU or let the real-time tasks to wait for best-effort tasks to complete, which causes low utilization or high latency, or both. This paper presents REEF, the first GPU-accelerated DNN inference serving system that enables microsecond-scale kernel preemption and controlled concurrent execution in GPU scheduling. REEF is novel in two ways. First, based on the observation that DNN inference kernels as mostly idempotent, REEF devises a reset-based preemption scheme that launches a real-time kernel on the GPU by proactively killing and restoring best-effort kernels at microsecond-scale. Second, since DNN inference kernels have varied parallelism and predictable latency, REEF proposes a dynamic kernel padding mechanism that dynamically pads the real-time kernel with appropriate best-effort kernels to fully utilize the GPU with negligible overhead. Evaluation using a new DNN inference serving benchmark (DISB) with diverse workloads and a real-world trace on an AMD GPU shows that REEF only incurs less than 2% overhead in the end-to-end latency for real-time tasks but increases the overall throughput by up to 7.7x, compared to dedicating the GPU to real-time tasks. To demonstrate the feasibility of our approaches on closed-source GPUs, we further ported and evaluated a restricted version of REEF on an NVIDIA GPU with a reduction of the preemption latency by up to 12.3x (from 6.3x).

关键词： Scheduling

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Fast and scalable in-network lock management using lock fission 24

Fast and scalable in-network lock management using lock fiss...

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Proceedings of the 18th USENIX Conference on Operating systems Design and Implementation

作者： Hanze Zhang Ke Cheng Rong Chen Haibo Chen Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University and Shanghai AI Laboratory and MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University and Engineering Research Center for Domain-specific Operating Systems Ministry of Education China Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University and Shanghai AI Laboratory and Engineering Research Center for Domain-specific Operating Systems Ministry of Education China Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University and Engineering Research Center for Domain-specific Operating Systems Ministry of Education China and Key Laboratory of System Software (Chinese Academy of Sciences)

ISBN: (纸本)9781939133403

distributed lock services are extensively utilized in distributed systems to serialize concurrent accesses to shared resources. The need for fast and scalable lock services has become more pronounced with decreasing task execution times and expanding dataset scales. However, traditional lock managers, reliant on server CPUs to handle lock requests, experience significant queuing delays in lock grant latency. Advanced network hardware (e.g. programmable switches) presents an avenue to manage locks without queuing delays due to their high packet processing power. Nevertheless, their constrained memory capacity restricts the manageable lock scale, thereby limiting their effect in large-scale *** paper presents FISSLOCK, a fast and scalable distributed lock service that exploits the programmable switch to improve (tail) latency and peak throughput for millions of locks. The key idea behind FISSLOCK is the concept of lock fission, which decouples lock management into grant decision and participant maintenance. FISSLOCK leverages the programmable switch to decide lock grants synchronously and relies on servers to maintain participants (i.e., holders and waiters) asynchronously. By using the programmable switch for routing, FISSLOCK enables on-demand fine-grained lock migration, thereby reducing the lock grant and release delays. FISSLOCK carefully designs and implements grant decision procedure on the programmable switch, supporting over one million locks. Evaluation using various benchmarks and a real-world application shows the efficiency of FISSLOCK. Compared to the state-of-the-art switch-based approach (NetLock), FISSLOCK cuts up to 79.1% (from 43.0%) of median lock grant time in the microbenchmark and improves transaction throughput for TATP and TPC-C by 1.76× and 2.28×, respectively.

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SwarmL: A Language for Programming Fully distributed Intelligent Building systems

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BUILDINGS 2023年第2期13卷 499页

作者： Chen, Wenjie Yang, Qiliang Jiang, Ziyan Xing, Jianchun Zhao, Shuo Zhou, Qizhen Han, Deshuai Feng, Bowei Army Engn Univ PLA Coll Def Engn Nanjing 211101 Peoples R China Tsinghua Univ Bldg Energy Res Ctr Beijing 100084 Peoples R China China Xian Satellite Control Ctr Xian 710043 Peoples R China Rocket Force Univ Engn Coll Combat Support Xian 710025 Peoples R China

Fully distributed intelligent building systems can be used to effectively reduce the complexity of building automation systems and improve the efficiency of the operation and maintenance management because of its self-organization, flexibility, and robustness. However, the parallel computing mode, dynamic network topology, and complex node interaction logic make application development complex, time-consuming, and challenging. To address the development difficulties of fully distributed intelligent building system applications, this paper proposes a user-friendly programming language called SwarmL. Concretely, SwarmL (1) establishes a language model, an overall framework, and an abstract syntax that intuitively describes the static physical objects and dynamic execution mechanisms of a fully distributed intelligent building system, (2) proposes a physical field-oriented variable that adapts the programming model to the distributed architectures by employing a serial programming style in accordance with human thinking to program parallel applications of fully distributed intelligent building systems for reducing programming difficulty, (3) designs a computational scope-based communication mechanism that separates the computational logic from the node interaction logic, thus adapting to dynamically changing network topologies and supporting the generalized development of the fully distributed intelligent building system applications, and (4) implements an integrated development tool that supports program editing and object code generation. To validate SwarmL, an example application of a real scenario and a subject-based experiment are explored. The results demonstrate that SwarmL can effectively reduce the programming difficulty and improve the development efficiency of fully distributed intelligent building system applications. SwarmL enables building users to quickly understand and master the development methods of application tasks in fully distributed intelligent

关键词： swarm intelligence fully distributed intelligent building system parallel programming domain-specific language

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Utilizing Blockchain Technology for Financial Services in parallel, distributed, and Grid Computing Frameworks

Utilizing Blockchain Technology for Financial Services in Pa...

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Computing and Intelligent reality Technologies (ICCIRT), International Conference on

作者： Prakash Chokkamreddy Anusha Palagati Akurathi Lakshmi Pathi Rao P Maheswari Vanitha Mahadevan Santhosh Kumar Balan School of Management Studies Guru Nanak Institutions Technical Campus Ibrahimpatnam Hyderabad India Dept. of Computer Science and Engineering Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India Department of Computer Applications KSR College of Arts and Science for Women Tiruchengode India College of Engineering and Technology University of Technology and Applied Sciences Al Mussanah Sultanate of Oman

ISBN: (数字)9798331510299

ISBN: (纸本)9798331510305

Application of blockchain in financial services has opened new ways of efficiency in transaction processing, assets management and security. The application of parallel, distributed, and grid computing with blockchain in improving the functionality of financial services like real time financial transactions, high frequency trading, portfolio and risk analysis is examined in this paper. It has been ascertained that namely parallel and distributed computing together with blockchain are the most suitable for facing increasing needs in data in the sphere of financial services. It also investigates the use of grid computing frameworks and their effects on the correspondingly balanced flows of medium amongst multiple systems. Various issues such as security, privacy violations, and adherence to the law are also reviewed, and existing prospects for developing through smart contracts and consensus mechanisms using blockchain. Exploring how some of the most successful pilot use-cases of blockchain are applicable for future growth in the financial services industry offers insights into how blockchain will soon enter mainstream as a disruptive technology in the broader global financial sector.

关键词： Privacy Law Smart contracts Consensus algorithm Grid computing real-time systems Blockchains Security Risk analysis Financial services

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