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检索条件"任意字段=International Conference on Parallel and Distributed Processing Techniques and Applications"

共 11317 条记录，以下是661-670 订阅

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A Framework for Decentralized parallel Complex Event processing on Heterogeneous Infrastructures

A Framework for Decentralized Parallel Complex Event Process...

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9th IEEE international conference on Big Data (IEEE BigData)

作者： Roeger, Henriette Bhowmik, Sukanya Linn, Tobias Univ Stuttgart Inst Parallel & Distributed Syst Stuttgart Germany

ISBN: (纸本)9781665439022

Low-latency stream processing applications (SPA) distribute the event streams on parallel instances of the application's operators to avoid queuing. Each operator has a set of processing nodes for its *** and fog infrastructures promise, among other things, low communication latency and are thus of interest for low-latency SPAs. Yet, they include heterogeneous, even volatile nodes which makes the parallel processing of SPAs challengingAvailable solutions for parallel SPAs often assume homogeneity and stable ***, we aim to balance the stream processing among heterogeneous, instable infrastructures using a decentralized control mechanism. We propose a decentralized, dynamic, and lightweight approach to re-balance SPA-load according to the nodes current performance, while minimizing latencies. In doing so, we introduce the Coda-EFC framework for window-based stream processing on heterogeneous distributed infrastructures. It dynamically adapts its load balancing to node performance without computing quickly outdated schedules and aims for latency-optimal load balancing. We introduce two adaptive load balancing strategiesone tailored for heterogeneous, dynamic infrastructures, the other an adaptation of a state-of-the art approach.

关键词： Stream processing Multi-provider Infrastructure Fog Computing

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Utilizing Network Hardware parallelism for MPI Partitioned Collective Communication

Utilizing Network Hardware Parallelism for MPI Partitioned C...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Yıltan Hassan Temuçin Amirreza Barati Sedeh Whit Schonbein Ryan E. Grant Ahmad Afsahi ECE Department Queen’s University Kingston ON Canada Sandia National Laboratories Albuquerque New Mexico USA

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

parallel distributed applications running on large-scale high-performance computing systems depend on effective point-to-point and collective communication to meet performance goals. Beginning with version 4.0, the Message Passing Interface (MPI) introduced the partitioned communication API, providing tools for addressing communication bottlenecks raised by hybrid communication models. This API allows individual actors (CPU threads, GPU threads, etc.) to initiate communication on portions of complete buffers, enabling additional communication/computation overlap. Intuitively, the utility of partitioned communication could benefit from network-level support: If there are multiple paths between endpoints, an MPI-aware network could disperse partitions across these paths, avoiding the data serialization entailed by a dependency on a single path. The Cerio Rockport Ethernet Fabric has the ability to expose this capability to communication middleware. In this work we develop this capability to allow for user-level path selection for MPI partitioned communication and explore how this capability impacts point-to-point performance, collective design, and Allreduce efficiency in a Large Language Model task

关键词： Message passing Large language models High performance computing Ethernet parallel processing Hardware Fabrics Libraries Middleware Message systems

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Associativity-Aware Transaction processing Optimization for Web applications

Associativity-Aware Transaction Processing Optimization for ...

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international conference on parallel and distributed Systems (ICPADS)

作者： Qianmian Yu Jing Zhou School of Software Shanghai Jiao Tong University Shanghai China

Web applications rely on database systems to store and manage data. Existing major databases use lock-based concurrency control to coordinate transactions. However, lock-based transaction processing can be expensive and result in unnecessary deadlocks because the database systems are oblivious to the application semantics, such as access patterns. This paper examines an issue commonly seen in web applications: inefficient associated accesses. They are problematic because they cause database systems to abort concurrent transactions frequently, degrading application performance. To address this issue, we build Railyzer, a tool that automatically analyzes web applications’ database access patterns and provides possible optimization strategies. First, we establish a criterion to differentiate associated accesses from other accesses. Then, based on a language-agnostic analysis of the application, we create a method for identifying associated accesses. Finally, we use heuristics to locate inefficient ones and suggest fixes. We discovered 83 potential optimizations among the six open source applications. Some of these optimizations improve the application throughput by up to 70%.

关键词： Semantics distributed databases System recovery Throughput Control systems Database systems Concurrency control

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Efficient Differentially Private Tensor Factorization in the parallel and distributed Computing Paradigm

Efficient Differentially Private Tensor Factorization in the...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Feng Zhang Hao Wang Ruixin Guo Erkang Xue Guangzhi Qu School of Computer Science China University of Geosciences Wuhan China Department of Computer Science and Engineering Oakland University Rochester USA

Tensor factorization plays a fundamental role in multiple areas of AI research. Nevertheless, it encounters significant challenges related to privacy breaches and operational efficiency. In this study, we propose a novel approach that addresses both of these issues simultaneously by integrating differential privacy with parallel and distributed computing. To accommodate diverse scenarios, we introduce two models: DPTF-SVRG and ADMM-DPTF, each leveraging specific techniques. DPTF-SVRG is designed for single-GPU environments and utilizes a unique strategy to reduce gradient variance, enabling faster convergence compared to SGD. Moreover, it achieves parallelism on the GPU through a lock-free asynchronous approach. On the other hand, ADMM-DPTF utilizes distributed ADMM to parallelize DPTF-SVRG, enabling multi-GPU parallelism. Experimental results demonstrate that our algorithms outperform existing benchmarks while maintaining differential privacy.

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Data Enhancement Strategy Using Reinforcement Learning And Xgboost With BlockChain Security

Data Enhancement Strategy Using Reinforcement Learning And X...

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Intelligent Technologies (CONIT), international conference on

作者： S Selvapriya N Manvizhi S Ajay V K Kavya Rosario Gilmary Dept. of Information Technology IFET College of Engineering Villupuram Tamilnadu India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

The enormous volumes of data generated by Next Generation Sequencing (NGS) technology have transformed genomics and made efficient data analysis techniques necessary. To create data analytics applications, the extraction, transformation, and loading (ETL) process is necessary. In understanding data features, representation, storage, I/O operations, intermediate findings, in-memory analysis, and total processing time, the data format is crucial, as this study emphasizes. Data conformance is required for effective analysis since different machine learning and data mining algorithms require different types and forms of data. In this paper, we explore data format-centric optimization of the ETL process for NGS data analysis. We investigate how computing efficiency and analytical correctness are affected by the choice of data type. By using distributed computing and parallel processing, we evaluate the effectiveness of several data formats in NGS data analysis processes, such as FASTQ, BAM, and VCF. Our study offers suggestions for developing data analytics applications in the genomics field by clarifying the relationship between data format, ETL, and algorithmic needs. We also look at the trade-offs between resource usage and data format choice. In the end, our study contributes to more accurate and efficient genomics research by addressing the urgent need for well-informed data format options in NGS data analysis.

关键词： Sequential analysis Data analysis Loading Genomics Reinforcement learning parallel processing Data mining

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Optimizing Depthwise Convolutions on ARMv8 Architecture 23rd

Optimizing Depthwise Convolutions on ARMv8 Architecture

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23rd international conference on parallel and distributed Computing, applications, and Technologies, PDCAT 2022

作者： Hao, Ruochen Wang, Qinglin Yin, Shangfei Zhou, Tianyang Zhang, Qingyang Mei, Songzhu Shen, Siqi Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China College of Computer National University of Defense Technology Changsha410073 China Xiamen University Xiamen China

ISBN: (纸本)9783031299261

Depthwise convolutions are widely used in lightweight convolutional neural networks (CNNs). The performance of depthwise convolutions is mainly bounded by the memory access rather than the arithmetic operations for classic convolutions so that direct algorithms are often more efficient than indirect ones (matrix multiplication-, Winograd-, and FFT-based convolutions) with additional memory accesses. However, the existing direct implementations of depthwise convolutions on ARMv8 architectures feature a bad trade-off between register-level reuse of different tensors, which usually leads to sub-optimal performance. In this paper, we propose a new direct implementation of depthwise convolutions by means of implicit padding, register tiling, etc. Compared to the existing ones, our new implementations can incur much less communication overhead between registers and cache. Experimental results on two ARMv8 CPUs show that our implementation can averagely deliver 4.88 × performance improvement over the existing direct ones in open-source libraries. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Programming and Mapping for Mixed Heterogeneous Devices: The Case of Optical Flow

Programming and Mapping for Mixed Heterogeneous Devices: The...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Sergio Alonso Pascual Arturo Gonzalez-Escribano Departamento de Informática Universidad de Valladolid. ETSII Valladolid Spain

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Current parallel systems are increasingly heterogeneous, mixing devices of different types and computing capabilities. Exploiting multiple different devices for the same application continues to be a challenge that ranges from technical problems related to synchronizing and communicating diverse devices to problems of load distribution and flexibility to adjust the computation to the platform resources. In this work, we study the problem of using and extending a heterogeneous portability layer to program and adapt HSOpticalFlow to heterogeneous platforms. HSOpticalFlow is a streaming application to estimate the apparent movement of objects in a sequence of images. It is a simple but characteristic example of the structure of applications based on multilevel ILS (Iterative Loop Stencil), also known as multi-grid methods, applied to a sequence of inputs. Starting from the original CUDA reference code, we present a methodology and programming techniques based on the Controller programming model to implement it as a pipeline among multiple devices. We discuss a technique to determine a proper work partition and mapping for a set of devices. This allows for building very efficient parallel solutions, using similar devices or taking advantage of devices with lower computing power, to reduce the load and increase the productivity of more powerful ones. We present the results of an experimental study using several GPUs of different vendors, architectures, and generations, showing that this solution allows combinations of devices to be efficiently exploited to improve performance. Specifically, the results include speedups of 1.91x using two NVIDIA A100 GPUs and 1.21x using one NVIDIA V100 GPU and one AMD WX9100 GPU, which is about $3 x$ slower than the NVIDIA GPU for this application.

关键词： Productivity Performance evaluation Computational modeling Pipelines Graphics processing units Computer architecture Programming Streaming media Optical flow Tuning

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parallel Scheduling of Complex Requests for a Constellation of Earth Observing Satellites 11

Parallel Scheduling of Complex Requests for a Constellation ...

引用

11th conference on Prestigious applications of Artificial Intelligence, PAIS 2022, co-located with the 31st international Joint conference on Artificial Intelligence and the 25th European conference on Artificial Intelligence, IJCAI-ECAI 2022

作者： Squillaci, Samuel Roussel, Stéphanie Pralet, Cédric ONERA/DTIS Toulouse France

ISBN: (纸本)9781643682945

Nowadays, the Earth observation systems involve multiple satellites, multiple ground stations, and multiple end-users that formulate various observation requests. These requests might be heterogeneous (stereoscopic observations, periodic observations, systematic observations, etc.), and one difficulty is that the search space defined by the possible ways of performing the requests given the multiple satellites and ground stations available is huge. This paper studies several combinatorial optimization techniques for solving such an operational problem, including a constraint programming approach and parallel scheduling techniques that take advantage of the problem structure. These algorithms are evaluated on realistic instances involving various request types and objective functions depending on the cloud cover conditions, that highly impact the quality of the images collected. © 2022 The authors and IOS Press.

关键词： Stereo image processing

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Content-aware auto-scaling of stream processing applications on container orchestration platforms

Content-aware auto-scaling of stream processing applications...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Giuseppe Coviello Kunal Rao Ciro Giuseppe De Vita Gennaro Mellone Priscilla Benedetti Srimat Chakradhar NEC Laboratories America Inc. Princeton NJ USA University of Napoli Parthenope Napoli Italy Vrije Universiteit Brussel - Brussels Belgium and University of Perugia Perugia Italy

Modern applications are designed as an interacting set of microservices, and these applications are typically deployed on container orchestration platforms like Kubernetes. Several attractive features in Kubernetes make it a popular choice for deploying applications, and automatic scaling is one such feature. The default horizontal scaling technique in Kubernetes is the Horizontal Pod Autoscaler (HPA). It scales each microservice independently while ignoring the interactions among the microservices in an application. In this paper, we show that ignoring such interactions by HPA leads to inefficient scaling, and the optimal scaling of different microservices in the application varies as the stream content changes. To automatically adapt to variations in stream content, we present a novel system called DataX AutoScaler that leverages knowledge of the entire stream processing application pipeline to efficiently auto-scale different microservices by taking into account their complex interactions. Through experiments on real-world video analytics applications, such as face recognition and pose classification, we show that DataX AutoScaler adapts to variations in stream content and achieves up to 43% improvement in overall application performance compared to a baseline system that uses HPA.

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Everything Everyway All at Once - Time Traveling Debugging for Stream processing applications

Everything Everyway All at Once - Time Traveling Debugging f...

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international conference on Data Engineering

作者： Timo Räth Marius Schlegel Kai-Uwe Sattler TU Ilmenau Ilmenau Germany

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Debugging, evaluating, and optimizing stream processing applications is challenging due to continuous streams of input data and typically distributed and parallel execution environments. To address these issues, we present an approach for explorative debugging of stream processing pipelines that allows in-depth investigation of a pipeline's execution behavior and evolution. The time traveling debugger enables traveling back in time within the pipeline's execution history and thoroughly analyzing and retracing each fine-grained step. Any changes made to the pipeline's structure or parameters are captured based on provenance information and can be reviewed, compared, and analyzed with the provenance inspector to understand the impact of each alteration on the quality of the pipeline.

关键词： Pipelines distributed databases Debugging Data engineering History

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