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

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AggrBoost: A Load-Balanced Computing Engine for Boosting Aggregation in GCN Accelerators

AggrBoost: A Load-Balanced Computing Engine for Boosting Agg...

引用

IEEE international Workshop on Anti-counterfeiting, Security, Identification, ASID

作者： Fan Wang Wei Ni Shengji Yang Song Yu-kun Zhang Duo-li School of Microelectronics. Hefei University of Technology Hefei China

ISBN: (数字)9798331505639

ISBN: (纸本)9798331505646

The emergence of Graph Convolutional Network marks a significant breakthrough in machine learning techniques for non-Euclidean spaces. During the graph aggregation process, several challenges arise, including load imbalance, low data reuse rate, and high off-chip data traffic. This paper proposes a computing engine for aggregation phase in GCNs based on a multi-edge parallel aggregation strategy with source node partitioning. This work featuring local sharing of source node feature vectors through source node partitioning and an encoding method named CSB (Compressed Sparse Block), achieves efficient load balancing under high parallelism through the equitable distribution of edge streams. It addresses memory access conflicts during the feature update process of destination nodes by utilizing distributed storage of partial sums, and effectively eliminates the pipeline stalls within partitions by Index-Driven MAC (Multiply-Accumulate). The PE utilization can exceed 97% on several popular datasets, making it suitable for application in GCN hardware accelerators.

关键词： Graph convolutional networks Pipelines parallel processing Load management Vectors Security Object recognition Engines Streams Hardware acceleration

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Design and Implementation a Secured and distributed System using CBC, Socket, and RMI Technologies 2021

Design and Implementation a Secured and Distributed System u...

引用

10th international conference on Software and Computer applications, ICSCA 2021

作者： Duc Phung, Thang Van Nguyen, Thai Quy Tran, Ban FPT University Viet Nam

ISBN: (纸本)9781450388825

This project aims to implement a tool that allows applications following the map-reduce principle and the AES encryption algorithm. The map-reduce policy is used in big data processing for speeding up the treatment of large data sets (large files). AES encryption algorithm is used to secure the data that is transmitted over the network. This principle is to split a large file into blocks (files) that are distributed on several nodes. An application (we call it Map) can be executed on each of these blocks in parallel. All the results (files) from these computations are then gathered on the original node where another application (we call it Reduce) is executed to compute the final result (one file). The typical application we will use is the word count application. This application counts the occurrences of each word in a big file. Each Map counts the presence of words in each block, producing a table . Then the Reduce aggregates the created tables to obtain the final result (a table ) © 2021 ACM.

关键词： Cryptography

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Optimizing File Similarity Search techniques in Data Lakes using parallel processing and Big Data Frameworks

Optimizing File Similarity Search Techniques in Data Lakes u...

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Smart Cities 4.0, international conference on

作者： Sara Sherif Daoud Saad Ayman Alserafi Department of Business Informatics German University in Cairo Cairo Egypt

ISBN: (数字)9781665474306

ISBN: (纸本)9781665474313

Smart cities generate a massive amount of disparate data from multiple sources, necessitating the use of Data Lakes for efficient storage and management, especially with the advent of Big Data. However, it is critical to use appropriate search techniques and governance to prevent the Data Lake from becoming a data swamp. The Data Lake encompasses a wide range of documents and topics, which makes finding similar datasets, e.g., through schema matching approaches, difficult and computationally intensive. In this paper, we propose an approach that leverages Data Lakes and Big Data frameworks to improve the search for related or near-identical structured textual datasets. We present a framework for re-implementing the early pruning algorithm “DS-Prox” within a Big Data framework using Spark. The framework involves prototyping with two machine learning models: a Multilayer Perceptron neural network and a Random Forest ensemble learner. The effectiveness of our approach is demonstrated with good accuracy levels, highlighting the benefits of managing heterogeneous Big Data in Data Lakes.

关键词： Data lakes Machine learning algorithms Smart cities Neural networks parallel processing Multilayer perceptrons Big Data applications

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Enhancing the Scalability of Knowledge Discovery Services for Massive Data Mining on Clouds

Enhancing the Scalability of Knowledge Discovery Services fo...

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

作者： Vijayakumar Sajjan Jayam Pradeep Kumar Vikram Kalvala Narsaiah Domala B. Kiran Kumar C P Pavan Kumar Hota Department Of CSE Malla Reddy University Hyderabad Department Of ECE Chaitanya Deemed to be University Hyderabad Department Of ECE MLR Institute Of Technology Hyderabad Department Of Artificial Intelligence Shri Vishnu Engineering College for Women Bhimavaram

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

The traditional data mining techniques do not work well when it comes to the big data and the tremendous growth rate of the digital data also becomes a major challenge to the discovery of knowledge. Cloud computing offers an ideal solution to computationally and storage intensive tasks with scalable and on-demand services that can process big data at short notice. This paper reviews the current models that employ cloud structure for distributed data mining as well as models like CosDic and the Data Mining Cloud Framework (DMCF). The way of processing large datasets is also represented well by the Map Reduce model where it is possible to implement the data processing in parallel on several nodes. Despite the fact that cloud-based systems are very useful when it comes to the management of large amounts of data, challenges such as privacy, data security and data coordination still persist. These innovative methodologies include Edge Computing and Federated Learning that are anticipated to solve the mentioned issues by enhancing the speed of data processing and its safety. Thus, scalable cloud data mining to meet growing needs of knowledge discovery in big data contexts will have to develop new privacy protection mechanisms, integration of cloud models, and complex algorithms.

关键词： Cloud computing Federated learning Reviews Scalability Computational modeling Big Data Knowledge discovery Data processing Data models Edge computing

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Automatic Low-Overhead Load-Imbalance Detection in MPI applications 27th

Automatic Low-Overhead Load-Imbalance Detection in MPI Appli...

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27th international European conference on parallel and distributed Computing (Euro-Par)

作者： Arzt, Peter Fischler, Yannic Lehr, Jan-Patrick Bischof, Christian Tech Univ Darmstadt Dept Comp Sci Sci Comp Darmstadt Germany

ISBN: (纸本)9783030856656;9783030856649

Load imbalances are a major reason for efficiency loss in highly parallel applications. Hence, their identification is of high relevance in performance analysis and tuning. We present a low-overhead approach to automatically identify load-imbalanced regions and filter out irrelevant ones based on new selection heuristics in our PIRA tool for automatic instrumentation refinement for the Score-P measurement system. For the LULESH mini-app as well as the Ice-sheet and Sea-level System Model simulation package we, thus, correctly identify existing load imbalances while maintaining a runtime overhead of less than 10% for all but one input. Moreover, the traces generated are suitable for Scalasca's automatic trace analysis.

关键词： Score-P Load imbalance HPC Performance analysis

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Cutting the Request Completion Time in Key-value Stores with distributed Adaptive Scheduler 41

Cutting the Request Completion Time in Key-value Stores with...

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41st IEEE international conference on distributed Computing Systems (ICDCS)

作者： Jiang, Wanchun Li, Haoyang Yan, Yulong Ji, Fa Jiang, Ming Wang, Jianxin Zhang, Tong Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781665445139

Nowadays, the distributed key-value stores have become the basic building block for large scale cloud applications. In large-scale distributed key-value stores, many key-value access operations, which will be processed in parallel on different servers, are usually generated for the data required by a single end-user request. Hence, the completion time of the end request is determined by the last completed key-value access operation. Accordingly, scheduling the order of key-value access operations of different end requests can effectively reduce their completion time, improving the user experience. However, existing algorithms are either hard to employ in distributed key-value stores due to the relatively large cooperation overhead for centralized information or unable to adapt to the time-varying load and server performance under different traffic patterns. In this paper, we first formalize the scheduling problem for small mean request completion time. As a step further, because of the NP-hardness of this problem, we heuristically design the distributed adaptive scheduler (DAS) for distributed key-value stores. DAS reduces the average request completion time by a distributed combination of the largest remaining processing time last and shortest remaining process time first algorithms. Moreover, DAS is adaptive to the time-varying server load and performance. Extensive simulations show that DAS reduces the mean request completion time by more than 15 similar to 50% compared to the default first come first served algorithm and outperforms the existing Rein-SBF algorithm under various scenarios.

关键词： distributed Key-value Stores Request Completion Time Scheduling Adaptive

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Diamont: Dynamic Monitoring of Uncertainty for distributed Asynchronous Programs 21st

Diamont: Dynamic Monitoring of Uncertainty for Distributed A...

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21st international conference on Runtime Verification (RV)

作者： Fernando, Vimuth Joshi, Keyur Laurel, Jacob Misailovic, Sasa Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9783030884949;9783030884932

Many application domains including graph analytics, the Internet-of-Things, precision agriculture, and media processing operate on noisy data and/or produce approximate results. These applications can distribute computation across multiple (often resource-constrained) processing units. Analyzing the reliability and accuracy of such applications is challenging, since most existing techniques operate on specific fixed error models, check for individual properties, or can only be applied to sequential programs. We present Diamont, a system for dynamic monitoring of uncertainty properties in distributed programs. Diamont programs consist of distributed processes that communicate via asynchronous message passing. Diamont includes datatypes that dynamically monitor uncertainty in data and provides support for checking predicates over the monitored uncertainty at runtime. We also present a general methodology for verifying the soundness of the runtime system and optimizations using canonical sequentialization. We implemented Diamont for a subset of the Go language and evaluated eight programs from precision agriculture, graph analytics, and media processing. We show that Diamont can prove important end-to-end properties on the program outputs for significantly larger inputs compared to prior work, with modest execution time overhead: 3% on average and 16.3% at maximum.

关键词： Precision agriculture

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Machine Learning Approaches for Early Detection of Breast Cancer

Machine Learning Approaches for Early Detection of Breast Ca...

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IEEE international conference on Intelligent techniques in Control, Optimization and Signal processing (INCOS)

作者： B.Pandi Priya R. Ramani PSR Engineering college Sivakasi Tamilnadu India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

A crucial concern in biomedical research revolves around forecasting genome disorders, which contribute to diverse and severe ailments such as cancer, dementia, diabetes, cystic fibrosis, and leigh syndrome, resulting in significant global mortality rates. Historically, theoretical and explanatory methods were employed to anticipate genome disorders. Progressions in innovation prompted upgraded hereditary information inclusion, traversing almost the whole genome and protein, provoking the presentation of machine and profound learning approaches for genome jumble expectation. Simultaneously, parallel machine and deep learning strategies emerged as part of this technological evolution. Past exploration broadly investigated genome jumble forecast utilizing directed, solo, and semi-regulated learning procedures, with a dominating spotlight on twofold issue expectation utilizing hereditary grouping information. Machine learning has become a prevalent tool in medical applications, particularly in identifying cancer cell types. Breast cancer, a prominent cause of female mortality, can be mitigated through early detection of cancerous cells. Diagnostic tests such as MRI, mammogram, ultrasound, and biopsy are commonly employed for this purpose. In the domain of AI and information mining, analysts mean to recognize the best strategy for supporting enormous data sets with high expectation exactness. The essential goal of our methodology is to distinguish and anticipate bosom malignant growth in view of side effects inside the information dataset. Our system utilizes a combination of machine learning algorithms to achieve accurate breast cancer predictions.

关键词： Deep learning Ultrasonic imaging Genomics Signal processing algorithms Clustering algorithms Breast cancer Vectors

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Energy-Efficient Scheduling of Moldable Streaming Computations for the Edge-Cloud Continuum

Energy-Efficient Scheduling of Moldable Streaming Computatio...

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international conference on Fog and Mobile Edge Computing (FMEC)

作者： Sajad Khosravi Christoph Kessler Sebastian Litzinger Jörg Keller Linköping University Linköping Sweden FernUniversität in Hagen Hagen Germany

ISBN: (数字)9798350366488

ISBN: (纸本)9798350366495

We consider the problem of cost-effectively mapping a swarm of soft real-time stream processing applications with moldable-parallel tasks to multicore resources in the device-edge-cloud continuum, consisting of mobile devices, edge resources and cloud resources. We leverage flexibility from different parallelization degrees and frequency levels (DVFS) for the tasks, keeping application throughput constraints and communication bandwidth limitations while minimizing overall cost (including device/edge resource energy and cloud resource renting). We present two offline algorithmic solutions with a global view of the environment: an integer linear program (ILP) extending the crown scheduling approach for multi-layer distributed systems and a greedy heuristic algorithm. Our experimental evaluation for several real-world and synthetic scenarios shows that the time required for solving the scheduling problem to cost-optimality by the ILP is feasible for nontrivial scenarios. The heuristic achieves about 12% worse cost efficiency on average, yet operates much faster (by 1-2 orders of magnitude), allowing to scale up the problem size more than the ILP approach.

关键词： Costs Multi-access edge computing Processor scheduling Multicore processing Heuristic algorithms Throughput Scheduling Real-time systems Mobile handsets Energy efficiency

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An Efficient Vectorization Scheme for Stencil Computation

An Efficient Vectorization Scheme for Stencil Computation

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international Symposium on parallel and distributed processing (IPDPS)

作者： Kun Li Liang Yuan Yunquan Zhang Yue Yue Hang Cao State Key Laboratory of Computer Architecture Chinese Academy of Sciences Institute of Computing Technology Beijing School of Computer Science and Technology University of Chinese Academy of Sciences Beijing

Stencil computation is one of the most important kernels in various scientific and engineering applications. A variety of work has focused on vectorization and tiling techniques, aiming at exploiting the in-core data parallelism and data locality respectively. In this paper, the downsides of existing vectorization schemes are analyzed. Briefly, they either incur data alignment conflicts or hurt the data locality when integrated with tiling. Then we propose a novel transpose layout to preserve the data locality for tiling and reduce the data reorganization overhead for vectorization simultaneously. To further improve the data reuse at the register level, a time loop unroll-and-jam strategy is designed to perform multistep stencil computation along the time dimension. Experimental results on the AVX2 and AVX-S12 CPUs show that our approach obtains a competitive performance with the classic vectorization methods (Auto Vectorization and Data Reorganization), state-of-the-art compilers (Pluto and SDSL), and highly-optimized work (DLT and Tessellation).

关键词： Concurrent computing distributed processing Layout parallel processing Registers Computational efficiency Kernel

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