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检索条件"任意字段=9th International Conference on Algorithms and Architectures for Parallel Processing"

共 2826 条记录，以下是51-60 订阅

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parallel Implementation of Number-theoretic Transform on GPU Clusters 24th

Parallel Implementation of Number-Theoretic Transform on G...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Takahashi, Daisuke Center for Computational Sciences University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8577 Japan

ISBN: (纸本)9789819615414

In this paper, we propose a parallel implementation of the number-theoretic transform (NTT) on GPU clusters. the butterfly operation of the NTT can be performed using modular addition, subtraction, and multiplication. We show that the four-step fast Fourier transform algorithm can be applied to the NTT. We parallelized the four-step NTT using MPI and OpenACC on GPU clusters. We achieved a performance of over 745 giga-operations per second on 32 nodes of a GPU cluster equipped with NVIDIA H100 PCIe GPUs for a 235-point NTT with a modulus of 63 bits. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Number theory

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Control System Autonomy Improvement: An Attempt to Introduce Meta-heuristic algorithms into Closed-loop UAV Control Systems 18

Control System Autonomy Improvement: An Attempt to Introduce...

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18th international conference on Control Automation Robotics and Vision

作者： Dong, Shuyan Das, Saptarshi thornton, Alex Townley, Stuart Univ Exeter Fac Environm Sci & Econ Ctr Environm Math Penryn Campus Penryn TR10 9FE Cornwall England Univ Exeter Fac Environm Sci & Econ Ctr Ecol & Conservat Penryn Campus Penryn TR10 9FE Cornwall England

ISBN: (纸本)9798331518509;9798331518493

this study proposes integrating Genetic algorithms (GAs) into control systems to enhance autonomy, particularly for unmanned aerial vehicle (UAV) operations. Traditional control systems, which rely on expert knowledge and complex mathematical calculations, limit autonomy. In contrast, GAs offer robust global search capabilities, helping to avoid local optima and enhancing computational efficiency through parallel processing. Utilizing a modified Nonlinear Auto-Regressive eXogenous (NARX) model with feedback regulation ensures system stability and accurate tracking of target values, allowing the system to learn dynamic relationships essential for control in complex nonlinear conditions. We introduce a new GA-NARX based autonomous UAV control system designed for exploration in unfamiliar environments. Our enhanced system features a self-optimizing control mechanism that enables global optimization for peak performance. this advanced control system minimizes human-machine interaction by leveraging GAs' predictive abilities to anticipate future states while significantly improving the control precision. Overall, the design of this autonomous control system aims to optimize coordination and control strategies for UAV swarms, offering innovative solutions for efficient flight patterns.

关键词： Unmanned aerial vehicles (UAV)

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PESUnet3+: An Enhanced Unet3+ Network Architecture with parallel Supervised Encoders for Medical Image Segmentation 9

PESUnet3+: An Enhanced Unet3+ Network Architecture with Para...

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9th international conference on Intelligent Computing and Signal processing, ICSP 2024

作者： Deng, Jiajie Hu, Shiyu College of Science Shanghai University Shanghai China

ISBN: (纸本)9798350376548

Recent advancements in medical image segmentation have been significantly propelled by deep learning methodologies, markedly enhancing precision and computational efficacy. this progress is particularly pronounced in the realm of CT scan image processing. Our contribution, PESUnet3+, is an evolved architecture derived from Unet3+, tailored expressly for the segmentation of cancer-related medical imagery. this architecture is distinguished by the integration of sophisticated attention modules and a novel encoder design, which collectively elevate its performance metrics on the Liver Tumor Segmentation (LITS) dataset beyond the conventional Unet3+ framework. A central innovation of PESUnet3+ is its novel hierarchical supervision mechanism within the encoder stage, termed PESM. this mechanism integrates parallel convolutional down-sampling modules with Squeeze-and-Excitation (SE) attention mechanisms to derive supervised attention scores. these scores are subsequently amalgamated with the feature maps from the original down-sampling pathway, thereby augmenting the encoder' s efficacy. Moreover, the incorporation of an Atrous Spatial Pyramid Pooling (ASPP) module enhances the feature maps at the pinnacle layer, while Selective Kernel (SK) attention modules refine the expressiveness of lower layers. the architecture is further optimized by embedding residual connections within the skip pathways from encoder to decoder, enriching the flow of information. Empirical evaluations conducted on the LITS dataset substantiate PESUnet3+'s superiority, demonstrating a notable increase of 0.0429 in the Dice coefficient compared to its Unet3+ predecessor. Further ablation studies accentuate the pivotal supervisory role of the PESM module, showing substantial improvements in segmentation performance across various networks, with Dice coefficient enhancements ranging from 0.006 to 0.0183. © 2024 IEEE.

关键词： Image segmentation

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Discovery of Frequent Pagesets from Weblog Using Hadoop Mapreduce Based parallel Apriori Algorithm 9

Discovery of Frequent Pagesets from Weblog Using Hadoop Mapr...

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9th international conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Sowmya, H K Uma Reddy, N V Kavyashree, C Anandhi, R.J. New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bangalore India

ISBN: (纸本)9789380544441

Web usage mining is a critical stage in analyzing user behavior that involves mining frequently visited pages. Mining usage patterns from a big weblog file using existing frequent mining techniques are inefficient in terms of storage and performance. these algorithms did not perform well while mining big volumes of data due to CPU, storage, and main memory restrictions. To address these challenges, this paper proposes the parallel Apriori technique, which is based on Hadoop MapReduce and is an effective, extensible, and easy programming paradigm for processing large volumes of data. It employs pruning and session reduction techniques to eliminate infrequent pagesets, reducing the time required to process massive amounts of weblog data. It also employs clusters of processing nodes to improve performance. Experiments are conducted to demonstrate performance of the suggested approach using four genuine weblog data sets. Experiments reveal that our suggested system beats existing sequential association rule mining systems in finding similar pagesets, such as Apriori, Eclat, and FP-Growth. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Digital storage

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MUSTANG: A Workhorse for NASA Spaceflight Avionics 9

MUSTANG: A Workhorse for NASA Spaceflight Avionics

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IEEE 9th international conference on Space Mission Challenges for Information Technology (SMC-IT)

作者： Green, Christopher Haghani, Noosha Hernandez-Pellerano, Amri Gheen, Bobby Lanham, Austin Fraction, James NASA Goddard Space Flight Ctr Greenbelt MD 20771 USA

ISBN: (纸本)9798350341379

the Modular Unified Space Technology Avionics for Next Generation (MUSTANG) is a small integrated Avionics system including Command and Data Handling (C&DH), Power System Electronics (PSE), Attitude Control System Interfaces (ACS), and Propulsion Electronics. the MUSTANG Avionics Architecture is built upon many years of knowledge capture and lessons learned at the Goddard Space Flight Center. With a motivation towards modularity and keeping board redesign costs to a minimum, MUSTANG offers flexibility in features with a backplane-less design and allows the user to choose the options (cards) needed for their system. It incorporates a distributed power system that provides secondary power to all its subcomponents reducing the number of primary services needed for an Avionics. MUSTANG can be integrated into one system or divided into several smaller components. MUSTANG supports redundancy and cross-strap ability for a more robust and reliable Avionics system. A variation of MUSTANG exists for Instrument Electronics called iMUSTANG and allows the user to select functionality applicable to the instrument electronics. MUSTANG is not meant to replace Avionics for all spacecraft. there are limitations due to its relatively compact size, but the MUSTANG design has proven broadly applicable on many spacecraft and instrument bus avionics architectures.

关键词： MUSTANG GSFC avionics modular command and data handling C&DH CDH onboard processing

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Parareal with a Physics-Informed Neural Network as Coarse Propagator 29th

Parareal with a Physics-Informed Neural Network as Coarse Pr...

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29th international conference on parallel and Distributed Computing (Euro-Par)

作者： Ibrahim, Abdul Qadir Goetschel, Sebastian Ruprecht, Daniel Hamburg Univ Technol Inst Math Chair Computat Math Hamburg Germany

ISBN: (纸本)9783031396977;9783031396984

parallel-in-time algorithms provide an additional layer of concurrency for the numerical integration of models based on time-dependent differential equations. Methods like Parareal, which parallelize across multiple time steps, rely on a computationally cheap and coarse integrator to propagate information forward in time, while a parallelizable expensive fine propagator provides accuracy. Typically, the coarse method is a numerical integrator using lower resolution, reduced order or a simplified model. Our paper proposes to use a physics-informed neural network (PINN) instead. We demonstrate for the Black-Scholes equation, a partial differential equation from computational finance, that Parareal with a PINN coarse propagator provides better speedup than a numerical coarse propagator. Training and evaluating a neural network are both tasks whose computing patterns are well suited for GPUs. By contrast, mesh-based algorithms with their low computational intensity struggle to perform well. We show that moving the coarse propagator PINN to a GPU while running the numerical fine propagator on the CPU further improves Parareal's single-node performance. this suggests that integrating machine learning techniques into parallel-in-time integration methods and exploiting their differences in computing patterns might offer a way to better utilize heterogeneous architectures.

关键词： Parareal parallel-in-time integration PINN Machine learning GPUs heterogeneous architectures

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A Performance Modelling-Driven Approach to Hardware Resource Scaling

A Performance Modelling-Driven Approach to Hardware Resource...

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29th international conference on parallel and Distributed Computing (Euro-Par)

作者： Rodrigues, Alexandre Sousa, Leonel Ilia, Aleksandar Univ Lisbon INESC ID Inst Super Tecn Rua Alves Redol 9 P-1000029 Lisbon Portugal

ISBN: (纸本)9783031488023;9783031488030

the continuous demand for higher computational performance and the stagnating developments in the general purpose processor landscape have led to a surge in interest for highly specialized and efficient hardware. Combined with the rising popularity of parameterizable hardware, a new opportunity to optimize these architectures for particular workloads arises, largely driven by the RISC-V Instruction Set Architecture (ISA). this work present an application-specific optimization methodology for general purpose processors, enabling the development of architectures which are faster and more efficient for their designated workloads. Driven by the Cache-Aware Roofline Model (CARM) insights, the methodology guides the configuration of the memory and computational subsystems of the processor. We apply this methodology to two applications, demonstrating up to a 2.67x performance increase and a 1.34x improvement to energy efficiency.

关键词： Hardware Design Roofline modelling RISC-V

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On a Template Programming Approach for Shared Memory parallel architectures with Applications to the Fully Implicit Stokes Solver 17th

On a Template Programming Approach for Shared Memory Paral...

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17th international Scientific conference on parallel Computational Technologies, PCT 2023

作者： Evstigneev, N.M. Ryabkov, O.I. Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9783031388637

In this paper, we consider a fully implicit Stokes solver implementation targeting both GPU and multithreaded CPU architectures. the solver is aimed at the semistructured mesh often emerging during permeability calculations in geology. the solver basically consists of four main parts: geometry and topology analysis, linear system construction, linear system solution, and postprocessing. A modified version of the AMGCL library developed by the authors in earlier research is used for the solution. Previous experiments showed that the GPU architecture can deliver extremely high performance for such types of problems, especially when the whole stack is implemented on the GPU. However, the GPU memory limitation significantly reduces the available mesh sizes. For some applications, the computation time is not as important as the mesh size. therefore, it is convenient to have both GPU (for example, CUDA) and multithreaded CPU versions of the same code. the direct code port is time-consuming and error-prone. Several automatic approaches are available: OpenACC standard, DVM-system, SYCL, and others. Often, however, these approaches still demand careful programming if one wants to deliver maximum performance for a specific architecture. Some problems (such as the analysis of connected components, in our case) require totally different optimal algorithms for different architectures. Furthermore, sometimes native libraries deliver the best performance and are preferable for specific parts of the solution. For these reasons, we used another approach, based on C++ language abilities as template programming. the main two components of our approach are array classes and ‘for each’ algorithms. Arrays can be used on both CPU and CUDA architectures and internally substitute the memory layout that best fits the current architecture (as an ‘array of structures’ or ‘structure of arrays’). ‘For each’ algorithms generate kernels or parallel cycles that implement parallel processing for ind

关键词： Graphics processing unit

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Dynamic Path-Sensitive Memory Dependence Prediction 9

Dynamic Path-Sensitive Memory Dependence Prediction

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9th international conference on Integrated Circuits and Microsystems, ICICM 2024

作者： Liu, Weiding Li, Lunbo College of Computer Science & Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331509453

Memory dependence prediction is essential in superscalar out-of-order processors as it improves instruction-level parallelism efficiency by predicting dependencies between memory access instructions. However, existing state-of-the-art memory dependence predictors lack the ability to effectively utilize the dynamic execution information of conflicting instructions, leading to an excessive number of false positives and subsequently hindering overall performance. this work proposes Dynamic Path-Sensitive Memory Dependence Prediction (DPMDP), a novel dynamic path-sensitive memory dependence prediction method that dynamically selects the history length for each conflicting load instruction when multiple paths exist. Our key observation is that for load-store pairs with an initial occurrence of a violation if the conflicting store is waiting for an address translation that causes an execution delay, it does not create a dependency in the predictor initially and only creates it upon subsequent occurrences effectively. this observation is proven by running the SPEC CPU 2006 suite on the clock-accurate simulator GEM5. through cycle-accurate simulation of the SPEC CPU 2006 suite, we show that DPMDP achieved an average performance improvement of 1.08% compared to Store Set and 13.06% compared to PHAST. © 2024 IEEE.

关键词： parallel processing systems

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PPQSort: Pattern parallel Quicksort 15th

PPQSort: Pattern Parallel Quicksort

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15th international conference on parallel processing and Applied Mathematics, PPAM 2024

作者： Hévr, Gabriel Šimeček, Ivan Department of Computer Systems Faculty of Information Technology Czech Technical University in Prague Thákurova 9 Prague16000 Czech Republic

ISBN: (纸本)9783031856969

this paper presents PPQSort (Pattern parallel Quicksort), a new parallel quicksort algorithm that provides high performance and ease of use. PPQSort uses C++ threads for parallelization, achieving efficient sorting without external libraries and allowing seamless integration across different computing environments. this paper describes novel quicksort optimizations, including branchless partitioning and their efficient parallel implementation. PPQSort is compared with existing parallel quicksort algorithms on different machines and with different input data. Experimental evaluation results demonstrate that PPQSort is fast and robust, consistently outperforming the fastest available parallel quicksort implementations for almost all inputs. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： C++ (programming language)

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