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parallel and hierarchically-distributed Shoreline Alert Model (SAM)

Parallel and hierarchically-distributed Shoreline Alert Mode...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Ciro Giuseppe de Vita Gennaro Mellone Aniello Florio Catherine Alessandra Torres Charles Diana Di Luccio Marco Lapegna Guido Benassai Giorgio Budillon Raffaele Montella Dep. of Science and Technologies University of Naples “Parthenope” Naples Italy Dep. of Computer Science and Engineering University of Madrid Carlos III Madrid Spain Dept. of Mathematics and Applications University of Naples “Federico II” Naples Italy

In this paper, the Shoreline Alert Model (SAM) is presented as a component of a computation platform based on workflows dedicated to extreme weather/marine event simulation. The model aims to mitigate the effects of global change by providing decision-makers, scientists, and engineers with a novel, next-generation tool set for facing extreme weather events and implementing related management or emergency responses. SAM uses a parallelization schema, allowing users to run it on heterogeneous parallel architectures. As a result, SAM produces approximately 24 times faster results than the baseline when using shared memory with distributed memory and dealing with about 20,000 transects along the Campania coastline. The system is based on the algorithms of the open-source numerical models WRF (Weather Research and Forecasting) and WW3 (Wave-watch III) implemented with refraction and shoaling routines together with run-up equations to form the modeling chain used for coastal flooding assessment.

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Asia Pacific Web and Web-Age Information Management Joint international conference on Web and Big Data, APWeb-WAIM 2020 in conjunction with 3rd international Workshop on Knowledge Graph Management and applications, KGMA 2020, 2nd international Workshop on Semi-structured Big Data Management and applications, SemiBDMA 2020 and 1st international Workshop on Deep Learning in Large-scale Unstructured Data Analytics, DeepLUDA 2020

Asia Pacific Web and Web-Age Information Management Joint In...

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Asia Pacific Web and Web-Age Information Management Joint international conference on Web and Big Data, APWeb-WAIM 2020 in conjunction with 3rd international Workshop on Knowledge Graph Management and applications, KGMA 2020, 2nd international Workshop on Semi-structured Big Data Management and applications, SemiBDMA 2020 and 1st international Workshop on Deep Learning in Large-scale Unstructured Data Analytics, DeepLUDA 2020

ISBN: (纸本)9789811604782

The proceedings contain 13 papers. The special focus in this conference is on Web and Big Data. The topics include: Knowledge-Driven Multi-dimensional Dialogue Rewriting Model;towards Knowledge Graphs Federations: Issues and Technologies;a Learning Interests Oriented Model for Cold Start Recommendation;a Composite Chain Structure Blockchain Storage Method Based on Blockchain Technology;curriculum-Oriented Multi-goal Agent for Adaptive Learning;preface;neighborhood Query processing and Surrounding Objects Retrieval in Spatial Databases: applications and Algorithms;distributed Storage and Query for Domain Knowledge Graphs;label Propagation Algorithm Based on Topological Potential;LBNet: A Model for Judicial Reading Comprehension;deep Semantic Hashing for Large-Scale Image Retrieval.

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GRAPHGUESS: Approximate Graph processing System with Adaptive Correction 28th

GRAPHGUESS: Approximate Graph Processing System with Adaptiv...

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28th international conference on parallel and distributed Computing (Euro-Par)

作者： Ramezani, Morteza Kandemir, Mahmut T. Sivasubramaniam, Anand Penn State Univ State Coll PA 16801 USA

ISBN: (纸本)9783031125973;9783031125966

Graph-based data structures have drawn great attention in recent years. The large and rapidly growing trend on developing graph processing systems focuses mostly on improving the performance by preprocessing the input graph and modifying its layout. These systems usually take several hours to days to complete processing a single graph on high-end machines, let alone the overhead of pre-processing which most of the time can be dominant. Yet for most graph applications the exact answer is not always crucial, and providing a rough estimate of the final result is adequate. Approximate computing is introduced to trade off accuracy of results for computation or energy savings that could not be achieved by conventional techniques alone. In this work, we design, implement and evaluate GraphGuess, inspired from the domain of approximate graph theory and extend it to a general, practical graph processing system. GraphGuess is essentially an approximate graph processing technique with adaptive correction, which can be implemented on top of any graph processing system. We build a vertex-centric processing system based on GraphGuess, where it allows the user to trade off accuracy for better performance. Our experimental studies show that using GraphGuess can significantly reduce the processing time for large scale graphs while maintaining high accuracy.

关键词： Graph processing Approximate Computing

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GPU Optimization of Biological Macromolecule Multi-tilt Electron Tomography Reconstruction Algorithm 1

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19th international conference on Advanced Intelligent Computing Technology and applications (ICIC)

作者： Fu, Zi-Ang Wan, Xiaohua Zhang, Fa Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Gansu Peoples R China Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China

ISBN: (数字)9789819947492

ISBN: (纸本)9789819947485;9789819947492

Three-dimensional (3D) reconstruction in cryo-electron tomography (cryo-ET) plays an important role in studying in situ biological macromolecular structures at the nanometer level. Owing to limited tilt angle, 3D reconstruction of cryo-ET always suffers from a "missing wedge" problem which causes severe accuracy degradation. Multi-tilt reconstruction is an effective method to reduce artifacts and suppress the effect of the missing wedge. As the number of tilt series increases, large size data causes high computation and huge memory overhead. Limited by the memory, multi-tilt reconstruction cannot be performed in parallel on GPUs, especially when the image size reaches 1 K, 2 K, or even larger. To optimize large-scale multi-tilt reconstruction of cryo-ET, we propose a newGPU-based large-scale multi-tilt tomographic reconstruction algorithm (GMSIRT). Furthermore, we design a two-level data partition strategy in GM-SIRT to greatly reduce the memory required in the whole reconstructing process. Experimental results show that the performance of the GM-SIRT algorithm has been significantly improved compared with DM-SIRT, the distributed multi-tilt reconstruction algorithm on the CPU cluster. The acceleration ratio is over 300%, and the memory requirement only decreases to one-third of DM-SIRT when the image size reaches 2 K.

关键词： Graphics processing unit

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QR Factorization of Block Low-Rank Matrices on Multi-instance GPU 23rd

QR Factorization of Block Low-Rank Matrices on Multi-insta...

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

作者： Ohshima, Satoshi Ida, Akihiro Yokota, Rio Yamazaki, Ichitaro Information Technology Center Nagoya University Aichi Japan Research Institute for Value-Added-Information Generation Japan Agency for Marine-Earth Science and Technology Kanagawa Japan Global Scientific Information and Computing Center Tokyo Institute of Technology Tokyo Japan Scalable Algorithms Department Sandia National Laboratories New Mexico United States

ISBN: (纸本)9783031299261

The QR factorization, which is a fundamental operation in linear algebra, is used extensively in scientific simulations. The acceleration and memory reduction of it are important research targets. QR factorization using block low-rank matrices (BLR-QR) has previously been proposed to address this issue. In this study, we consider its implementation on a GPU. Current CPUs and GPUs have numerous computational cores and the performance consists of the total performance of them. Therefore, the degree of parallelism of the target calculation is important for obtaining high performance. By contrast, many applications, including BLR-QR, do not have sufficient parallelism. Batched computation has attracted attention for achieving high performance in such calculations. However, the use of it requires major code rewriting and is extremely laborious. Thus, we propose the use of the multi-instance GPU (MIG) feature of current GPUs. Using MIG, we succeeded in obtaining a 53.3% time reduction over the CPU and 77.6% over the GPU without MIG. From the above result, we succeeded in demonstrating rapid implementation of BLR-QR on MIG and usefulness of MIG. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphics processing unit

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A Transpose-free Three-dimensional FFT Algorithm on ARM CPUs 23

A Transpose-free Three-dimensional FFT Algorithm on ARM CPUs

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Chen, Tun Jia, Haipeng Li, Zhihao Li, Chendi Zhang, Yunquan Skl of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Huawei Technologies Co. Ltd Shenzhen China

ISBN: (纸本)9781665494571

According to the traditional multi-dimensional FFT, memory layouts of high-dimensional data are discontinuous. Transposition is introduced to keep high-dimensional data continuous in memory. However, transposition increases memory access and is a hot spot for multi-dimensional FFT. This paper proposes an optimization framework to eliminate explicit transpositions and optimize the three-dimensional (3D) FFT. This framework includes three research points. 1) combines the width-first and breadth-first search to optimize the butterfly network of one-dimensional (1D) FFT;2) adopts a column-order algorithm to eliminate data transposition;3) adopts a blocking algorithm of cache-aware to better use the hardware resources of ARM architecture. Based on this optimized framework, a multi-dimensional FFT library named MDFFT is implemented. The experiments demonstrate that MDFFT generally performs better than FFTW and ARMPL on ARM CPUs. © 2021 IEEE.

关键词： Three-dimensional displays Smart cities Layout parallel processing Libraries Hardware Optimization

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TAPA-CS: Enabling Scalable Accelerator Design on distributed HBM-FPGAs 24

TAPA-CS: Enabling Scalable Accelerator Design on Distributed...

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29th ACM international conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2024

作者： Prakriya, Neha Chi, Yuze Basalama, Suhail Song, Linghao Cong, Jason UCLA Los Angeles United States

ISBN: (纸本)9798400703867

Despite the increasing adoption of FPGAs in compute clouds, there remains a significant gap in programming tools and abstractions which can leverage network-connected, cloud-scale, multi-die FPGAs to generate accelerators with high frequency and throughput. We propose TAPA-CS, a task-parallel dataflow programming framework which automatically partitions and compiles a large design across a cluster of FPGAs while achieving high frequency and throughput. TAPA-CS has three main contributions. First, it is an open-source framework which allows users to leverage virtually "unlimited" accelerator fabric, high-bandwidth memory (HBM), and on-chip memory. Second, given as input a large design, TAPA-CS automatically partitions the design to map to multiple FPGAs, while ensuring congestion control, resource balancing, and overlapping of communication and computation. Third, TAPA-CS couples coarse-grained floorplanning with interconnect pipelining at the inter- and intra-FPGA levels to ensure high frequency. FPGAs in our multi-FPGA testbed communicate through a high-speed 100Gbps Ethernet infrastructure. We have evaluated the performance of TAPA-CS on designs, including systolic-array based CNNs, graph processing workloads such as page rank, stencil applications, and KNN. On average, the 2-, 3-, and 4-FPGA designs are 2.1×, 3.2×, and 4.4× faster than the single FPGA baselines generated through Vitis HLS. TAPA-CS also achieves a frequency improvement between 11%-116% compared with Vitis HLS. © 2024 Copyright held by the owner/author(s).

关键词： Field programmable gate arrays (FPGA)

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Zero-Shot Face Swapping with De-identification Adversarial Learning 22nd

Zero-Shot Face Swapping with De-identification Adversarial L...

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22nd international conference on parallel and distributed Computing, applications and Technologies (PDCAT 2021)

作者： Li, Huifang Li, Yidong Liu, Jiaming Hong, Zhibin Hu, Tianshu Ren, Yan Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Baidu Inc Baidu Technol Pk Bldg 2Xibeiwang East Rd Beijing 100193 Peoples R China QI ANXIN Technol Grp Inc Beijing 100044 Peoples R China

ISBN: (纸本)9783030967727;9783030967710

In this paper, we propose a Zero-shot Face Swapping Network (ZFSNet) to swap novel identities where no training data is available, which is very practical. In contrast to many existing methods that consist of several stages, the proposed model can generate images containing the unseen identity in a single forward pass without fine-tuning. To achieve it, based on the basic encoder-decoder framework, we propose an additional de-identification (De-ID) module after the encoder to remove the source identity information, which contributes to removing the source identity retaining in the encoding stream and improves the model's generalization capability. Then we introduce an attention component (ASSM) to blend the encoded source feature and the target identity feature adaptively. It amplifies proper local details and helps the decoder attend to the related identity feature. Extensive experiments evaluated on the synthesized and real images demonstrate that the proposed modules are effective in zero-shot face swapping. In addition, we also evaluate our framework on zero-shot facial expression translation to show its versatility and flexibility.

关键词： Face swapping Facial expression translation Adversarial learning

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AP3: Adaptive Power Prediction Framework based on Spatial Partition Multi-Phase Model 23

AP3: Adaptive Power Prediction Framework based on Spatial Pa...

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作者： Chen, Juan Ou, Zhixin Guo, Yifei Qi, Xinxin Sun, Yuyang Deng, Lin Chen, Hongyu Lin, Zihan College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665494571

The accuracy of processor power modeling is an important foundation for power management and optimization on parallel computing system. It is difficult to build a high-accuracy instantaneous CPU/DRAM power prediction model. One of the main reasons for the low accuracy is that the processor architecture sometimes influence greatly on the accuracy of power prediction. For example, the accuracy of processor power model is affected by inaccurate modeling of the uncore part of processor. Another reason comes from the limitation of static models for instantaneous power prediction. Despite the existence of various optional linear/nonlinear models, the fixed training set and model coefficients are insufficient to make high-accuracy instantaneous predictions for various target programs. Aiming at the above two issues, this paper proposes an Adaptive Power Prediction framework based on spatial Partition multi-phase model (AP^{3}). Spatial partition mainly solves the impact of uncore power on the prediction accuracy, and adaptability solves the limitation of the static model on the power prediction accuracy. According to the experimental results on both ARM-based and x86-based processor platforms, AP^{3} greatly increases the CPU and DRAM power instantaneous prediction accuracy. Spatial partition reduce the prediction error (MRE) by 0.3%-8.2% compared to previous single model, while adaptive update further reduces the error (MRE) by 1.7%-7.1% compared to previous static model. © 2021 IEEE.

关键词： Training Adaptation models Smart cities Multicore processing Computational modeling Power system management Random access memory

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Blockchain and the General Data Protection Regulation: Healthcare Data processing 23rd

Blockchain and the General Data Protection Regulation: Healt...

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23rd international conference on Computational Science and Its applications, ICCSA 2023

作者： Perchinunno, Paola Massari, Antonella L’Abbate, Samuela Crocetta, Corrado Department of Economics Management and Business Law University of Bari "Aldo Moro" Bari Italy Department of Humanities Research and Innovation University of Bari "Aldo Moro" Bari Italy

ISBN: (纸本)9783031371103

The General Data Protection Regulation (GDPR) of the European Union became binding in May 2018. The objective of the GDPR is essentially twofold. On the one hand, it seeks to facilitate the free movement of personal data between the various EU Member States, and, on the other hand, it establishes a framework for the protection of fundamental rights, based on the right to data protection as set out in Article 8 of the Charter of Fundamental Rights. The European Parliament has declared that the Blockchain must be considered a "tool that strengthens the autonomy of citizens by giving them the opportunity to control their data and decide which ones to share in the register, as well as the ability to choose who can see such data", thus favoring the transparency of transactions. Blockchain (or distributed register technology – DLT) technologies and their potential for the European Union’s digital single market have been widely discussed in recent years. It has been argued that blockchain technologies could be a suitable tool to achieve some of the goals of GDPR. Blockchains can be designed to allow data sharing, and improve transparency on data access. This study analyzes the relationship between blockchain and GDPR, to highlight existing problems and study possible solutions in relation to the processing of health data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Transparency

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