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Experience report: exploiting advanced database optimization features for Large-Scale SAP R/3 installations 02

Experience report: exploiting advanced database optimization...

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Proceedings of the 28th international conference on Very Large Data Bases

作者： Bernhard Zeller Alfons Kemper Universität Passau Passau Germany

The database volumes of enterprise resource planning (ERP) systems like SAP R/3 are growing at a tremendous rate and some of them have already reached a size of several Terabytes. OLTP (Online Transaction processing) databases of this size are hard to maintain and tend to perform poorly. Therefore most database vendors have implemented new features like horizontal partitioning to optimize such mission critical applications. Horizontal partitioning was already investigated in detail in the context of shared nothing distributed database systems but today's ERP systems mostly use a centralized database with a shared everything architecture. In this work, we therefore investigate how an SAP R/3 system performs when the data in the underlying database is partitioned horizontally. Our results show that especially joins, in parallel executed statements, and administrative tasks benefit greatly from horizontal partitioning while the resulting small increase in the execution times of insertions, deletions and updates is tolerable. These positive results have initiated the SAP cooperation partners to pursue a partitioned data layout in some of their largest installed productive systems.

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Cayley DHTs — a group-theoretic framework for analyzing DHTs based on cayley graphs 04

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Proceedings of the Second international conference on parallel and distributed processing and applications

作者： Changtao Qu Wolfgang Nejdl Matthias Kriesell L3S and University of Hannover Hannover Germany Institute of Mathematics (A) University of Hannover Hannover Germany

ISBN: (纸本)9783540241287

Static DHT topologies influence important features of such DHTs such as scalability, communication load balancing, routing efficiency and fault tolerance. While obviously dynamic DHT algorithms which have to approximate these topologies for dynamically changing sets of peers play a very important role for DHT networks, important insights can be gained by clearly focussing on the static DHT topology as well. In this paper we analyze and classify current DHTs in terms of their static topologies based on the Cayley graph group-theoretic model and show that most DHT proposals use Cayley graphs as static DHT topologies, thus taking advantage of several important Cayley graph properties such as vertex/edge symmetry, decomposability and optimal fault tolerance. Using these insights, Cayley DHT design can directly leverage algebraic design methods to generate high-performance DHTs adopting Cayley graph based static DHT topologies, extended with suitable dynamic DHT algorithms.

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applications of Evolutionary Computation 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Cecilia Chio Alexandros Agapitos Stefano Cagnoni Carlos Cotta Francisco Fernández Vega Gianni A. Caro Rolf Drechsler Anikó Ekárt Anna I. Esparcia-Alcázar Muddassar Farooq William B. Langdon Juan J. Merelo-Guervós Mike Preuss Hendrik Richter Sara Silva Anabela Simões Giovanni Squillero Ernesto Tarantino Andrea G. B. Tettamanzi Julian Togelius Neil Urquhart A. Şima Uyar Georgios N. Yannakakis

ISBN: (数字)9783642291784

ISBN: (纸本)9783642291777

This book constitutes the refereed proceedings of the international conference on the applications of Evolutionary Computation, Evoapplications 2012, held in Málaga, Spain, in April 2012, colocated with the Evo* 2012 events EuroGP, EvoCOP, EvoBIO, and EvoMUSART. The 54 revised full papers presented were carefully reviewed and selected from 90 submissions. Evoapplications 2012 consisted of the following 11 tracks: EvoCOMNET (nature-inspired techniques for telecommunication networks and other parrallel and distributed systems), EvoCOMPLEX (algorithms and complex systems), EvoFIN (evolutionary and natural computation in finance and economics), EvoGAMES (bio-inspired algorithms in games), EvoHOT (bio-inspired heuristics for design automation), EvoIASP (evolutionary computation in image analysis and signal processing), EvoNUM (bio-inspired algorithms for continuous parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for risk management, security and defense applications), EvoSTIM (nature-inspired techniques in scheduling, planning, and timetabling), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments).

关键词： Computation by Abstract Devices Programming Techniques Computer Communication Networks Math applications in Computer Science Image processing and Computer Vision Artificial Intelligence

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A NoSQL Data Model for Scalable Big Data Workflow Execution

A NoSQL Data Model for Scalable Big Data Workflow Execution

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IEEE international conference on Services Economics (SE)

作者： Aravind Mohan Mahdi Ebrahimi Shiyong Lu Alexander Kotov Wayne State University Detroit MI USA

While big data workflows haven been proposed recently as the next-generation data-centric workflow paradigm to process and analyze data of ever increasing in scale, complexity, and rate of acquisition, a scalable distributed data model is still missing that abstracts and automates data distribution, parallelism, and scalable processing. In the meanwhile, although NoSQL has emerged as a new category of data models, they are optimized for storing and querying of large datasets, not for ad-hoc data analysis where data placement and data movement are necessary for optimized workflow execution. In this paper, we propose a NoSQL data model that: 1) supports high-performance MapReduce-style workflows that automate data partitioning and data-parallelism execution. In contrast to the traditional MapReduce framework, our MapReduce-style workflows are fully composable with other workflows enabling dataflow applications with a richer structure, 2) automates virtual machine provisioning and deprovisioning on demand according to the sizes of input datasets, 3) enables a flexible framework for workflow executors that take advantage of the proposed NoSQL data model to improve the performance of workflow execution. Our case studies and experiments show the competitive advantages of our proposed data model. The proposed NoSQL data model is implemented in a new release of DATAVIEW, one of the most usable big data workflow systems in the community.

关键词： Data models Big data Virtual machining Cloud computing parallel processing Databases

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A C++ Library for Memory Layout and Performance Portability of Scientific applications 28th

A C++ Library for Memory Layout and Performance Portabilit...

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28th international European conference on parallel and distributed Computing , Euro-Par 2022

作者： Incardona, Pietro Gupta, Aryaman Yaskovets, Serhii Sbalzarini, Ivo F. Technische Universität Dresden Faculty of Computer Science Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Dresden Germany Center for Systems Biology Dresden Dresden01307 Germany Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI Dresden/Leipzig Germany

ISBN: (纸本)9783031312083

We present a C++14 library for performance portability of scientific computing codes across CPU and GPU architectures. Our library combines generic data structures like vectors, multi-dimensional arrays, maps, graphs, and sparse grids with basic, reusable algorithms like convolutions, sorting, prefix sum, reductions, and scan. The memory layout of the data structures is adapted at compile-time using tuples with optional memory mirroring between CPU and GPU. We combine this transparent memory mapping with generic algorithms under two alternative programming interfaces: a CUDA-like kernel interface for multi-core CPUs, Nvidia GPUs, and AMD GPUs, as well as a lambda interface. We validate and benchmark the presented library using micro-benchmarks, showing that the abstractions introduce negligible performance overhead, and we compare performance against the current state of the art. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphics processing unit

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An Optimization Method of WebP Images Lossy Compression Algorithm for FPGAs

An Optimization Method of WebP Images Lossy Compression Algo...

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

作者： Yaqian Zhao Zhenhua Guo Baoyu Fan Kai Gao Li Wang Fang Cao Inspur Electronic Information Industry Co. Ltd. Jinan China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

To improve the efficiency of WebP images lossy compression algorithm for heterogeneous accelerating based on FPGAs in data center server, this paper proposed an optimization method of WebP image lossy compression algorithm for FPGAs. By adopting the optimization method of this paper, the overall throughput performance of WebP heterogeneous acceleration solution based on OpenCL language can be up to 281MPixel/s. The energy efficiency ratio is 2.29x higher than Intel Xeon E5-2690v3 processor. Compare with the industry-leading WebP heterogeneous acceleration solution of Xilinx and CTAccel, the throughput performance of this paper is 2.5x better than Xilinx and 1.8x better than CTAccel. Finally, this paper improves the utilization of RAM resources according to the characteristics of the RAM block in the FPGA.

关键词： Field programmable gate arrays Compression algorithms Image coding Pipeline processing Quantization (signal) Acceleration Transforms

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The 16,384-node parallelism of 3D-CNN Training on An Arm CPU based Supercomputer

The 16,384-node Parallelism of 3D-CNN Training on An Arm CPU...

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international conference on High Performance Computing

作者： Akihiro Tabuchi Koichi Shirahata Masafumi Yamazaki Akihiko Kasagi Takumi Honda Kouji Kurihara Kentaro Kawakami Tsuguchika Tabaru Naoto Fukumoto Akiyoshi Kuroda Takaaki Fukai Kento Sato Fujitsu Limited Kawasaki Kanagawa Japan RIKEN Center for Computational Science Kobe Hyogo Japan

ISBN: (纸本)9781665410175

As the computational cost and datasets available for deep neural network training continue to increase, there is a significant demand for fast distributed training on supercomputers. However, porting and tuning applications for new advanced supercomputers requires tremendous amount of development efforts. Therefore, we present software tuning best practice for a 3D-CNN model training on a new Arm CPU based supercomputer, Fugaku. We (i) tune computation in DL by a JIT translator for aarch64, (ii) optimize collective communication such as Allreduce for 6D mesh/torus network topology, (iii) tune I/O by data staging with compression and data loader with caching, and (iv) parallelize training in data and model parallelism. We apply the proposed methods to a CosmoFlow 3D-CNN model, and achieve the training in 30 minutes using 16,384 nodes consisting of 4096 data- and 4 model-parallelism. This is the fastest result of any CPU-based systems in MLPerf HPC v0.7 in the world.

关键词： Training Computational modeling Neural networks parallel processing Supercomputers Software Computational efficiency

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ConvMB: Improving Convolution-Based Knowledge Graph Embeddings by Adopting Multi-Branch 3D Convolution Filters

ConvMB: Improving Convolution-Based Knowledge Graph Embeddin...

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

作者： Xiaobo Guo Fali Wang Neng Gao Zeyi Liu Kai Liu Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

In this paper, we propose a novel embedding model, named ConvMB, for knowledge base completion. ConvE captures the potential semantic associations of the knowledge base by learning the local feature interactions among the same entry of the embeddings. ConvKB further makes full use of the global feature interactions among the same entries of the embeddings to get a better performance. CapsE gains improvements by considering the deep correlations of the feature interaction. To the best of our knowledge, the deep associations of the feature interactions have not been considered further by existing convolution-based knowledge graph embedding models from the point of acrossing entries. Our model ConvMB fills this gap by adopting a multi-branch 3D convolutional structure and outperform state-of-the-art models. In ConvMB, the embeddings of the head entity, relation and tail entity of a triplet are firstly reshaped as a 2D matrix respectively. Then these three 2D matrixes are stacked into a 3D matrix, which is fed into the convolutional layer. Multiple filters of different shapes in different branches are operated on the 3D matrix to generate different feature maps. The shape of filters in the first layer is 1 × 1 × 3, which can be used to capture the feature interactions among the same entries and can get the corresponding feature maps. The shape of filters in layers after the first one include 2 × 2 × τ, 3 × 3 × τ, 4 × 4 × τ, 5 × 5 × τ, τ is number of filters in each layer of each branch. These filters can further act on the stacked feature maps to get deep associations across different entries. The final feature maps produced by each branch are concatenated into a single feature vector representing the input triplet. The single feature vector is multiplied with a weight vector via a dot product to predict whether the triplet is correct or not. Experiments show that link prediction performance is significant improved by ConvMB, relative to previous SOTA embedding models o

关键词： Solid modeling Three-dimensional displays Shape Convolution Knowledge based systems Semantics Knowledge representation

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An Improved Vehicle Detection Algorithm Based on YOLOV3

An Improved Vehicle Detection Algorithm Based on YOLOV3

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

作者： Xiaoqing Sun Qian Huang Yanping Li Yuan Huang School of Computer and Information Hohai University Nanjing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

With the development of science and technology, the application of vehicle object detection in intelligent video monitoring and vehicle assisted driving has become more extensive. Traditional vehicle object detection algorithms have some inefficiencies in generalization ability and recognition rate. To solve this problem, this paper proposes an improved vehicle detection method based on YOLO(You Only Look Once) V3. Our model improves the YOLOV3 algorithm, integrating the label smoothing, and adopts K-means++ algorithm to analyze the data aggregation and avoid the disappearance of the gradient, make our train model has better generalization ability. Finally, we use the UA-DETRAC data set for training, and the experimental results showed that our method has a better detection speed and a higher recognition rate for vehicle detection, which is 6% mAP higher than the original YOLOV3 algorithm.

关键词： Object detection Feature extraction Predictive models Smoothing methods Training Vehicle detection Data models

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DCSA: The Deployment Mechanism of Chained Serverless applications in JointCloud Environment

DCSA: The Deployment Mechanism of Chained Serverless Applica...

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IEEE international conference on Joint Cloud Computing (JCC)

作者： Yaojie Li Peichang Shi Jianfei Liu Rui Li Fei Gao Penghui Ma Dong Xie Guodong Yi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

Serverless computing, comprised of Function as a Service (FaaS) and Backend as a Service (BaaS), has garnered widespread attention owning to its features such as maintenance-free operations, pay-per-use pricing, and automatic scalability. However, practical usage encounters several challenges: 1) The diversity of user applications makes comprehensive performance evaluation difficult, as benchmark and application tests only reflect performance under specific conditions and cannot fully capture users’ actual experiences across different serverless platforms. 2) Disparities in performance and costs across different serverless platforms make it challenging to achieve optimal performance and cost efficiency through single-cloud deployment, thereby underutilizing the advantages of each platform. 3) Vendor lock-in issues restrict the migration of user applications and exacerbate dependence on a single cloud *** address these challenges, this paper proposes a collaborative mechanism, referred to as DCSA, which integrates FaaS and storage services to achieve automatic cross-cloud deployment of user applications while considering both performance and cost comprehensively. Firstly, we adapt the interfaces of different serverless platforms, effectively reducing the complexity of cross-cloud deployment. Secondly, we develop cost and latency models for the cross-cloud deployment of chained serverless applications and propose a deployment scheduling algorithm that simultaneously considers both latency and cost. Finally, we conduct experiments to evaluate the performance of the proposed algorithm. Results demonstrate that our method can effectively reduce latency (up to 2.3%) and lower costs (up to 9.9%).

关键词： Performance evaluation Adaptation models Costs Scheduling algorithms Scalability Serverless computing Collaboration

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