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检索条件"任意字段=17th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2017"

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17th international conference on algorithms and architectures for parallel processing, ica3pp 2017

17th International Conference on Algorithms and Architecture...

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17th international conference on algorithms and architectures for parallel processing, ica3pp 2017

ISBN: (纸本)9783319654812

the proceedings contain 66papers. the special focus in this conference is on algorithms and architectures for parallel processing. the topics include: Pipelining computation and optimization strategies for scaling GROMACS on the sunway many-core processor;exploring FPGA-GPU heterogeneous architecture for ADAS;new huge page allocator with main memory compression;an fpga-based real-time moving object tracking approach;automatic acceleration of stencil codes in android devices;optimizing concurrent evacuation transfers for geo-distributed datacenters in SDN;energy-balanced and depth-controlled routing protocol for underwater wireless sensor networks;on the energy efficiency of sleeping and rate adaptation for network devices;private and efficient set intersection protocol for big data analytics;a topology-aware framework for graph traversals;adaptive traffic signal control with network-wide coordination;a novel parallel dual-character string matching algorithm on graphical processing units;distributed nonnegative matrix factorization with HALS algorithm on mapreduce;GPU-accelerated block-max query processing;KD-tree and healpix-based distributed cone search indexing system for multi-band astronomical catalogs;an out-of-core branch and bound method for solving the 0-1 knapsack problem on a GPU;the curve boundary design and performance analysis for DGM based on openFOAM;leakage-resilient password-based authenticated key exchange;secure encrypted data deduplication with ownership proof and user revocation;optimally selecting the timing of zero-day attack via spatial evolutionary game and performance analysis of a ternary optical computer based on M/M/1 queueing system.

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PEbfs: Implement High-Performance Breadth-First Search on PEZY-SC3s 24th

PEbfs: Implement High-Performance Breadth-First Search on P...

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24th international conference on algorithms and architectures for parallel processing, ica3pp 2024

作者： Guo, Weihao Wang, Qinglin Liu, Xiaodong Peng, Muchun Yang, Shun Liang, Yaling Shi, Yongzhen Cao, Ligang Liu, Jie Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha410073 China Engineering Research Center for National Fundamental Software National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819615506

the breadth-first search (BFS) algorithm is a fundamental algorithm in graph theory, and it’s parallelization can significantly improve performance. therefore, there have been numerous efforts to leverage the powerful parallel computing capabilities of hardware like GPGPU to implement high-performance BFS algorithms. However, the energy efficiency is relatively low due to the high power consumption of the platforms on which the algorithm is adapted to. To deal with these challenges, this paper introduces PEbfs that is a high-performance BFS algorithm based on the PEZY-SC3s efficient processor. We integrated three search algorithms, two algorithm optimization strategies, and a directional optimization scheme into PEbfs. through multiple evaluations of the performance of PEbfs on the public SNAP dataset, the results demonstrate that the average energy efficiency ratio of PEbfs is higher than that of Enterprise and Tigr, the two most advanced implementations on Nvidia’s GPGPU: It achieves 3.08× the average energy efficiency ratio of Enterprise and 4.53× that of Tigr. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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Cross-Modal Mask and Detail Alignment for Text-Based Person Retrieval 24th

Cross-Modal Mask and Detail Alignment for Text-Based Perso...

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24th international conference on algorithms and architectures for parallel processing, ica3pp 2024

作者： Guo, Ao Liu, Xuan Liu, Xianggan Hu, Bingmeng Yuan, Jie Chu, Chiawei Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance Ministry of Education Minzu University of China Beijing100081 China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan Lingshui Li572499 China Natural Language Processing and Knowledge Graph Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China City University of Macau China

ISBN: (纸本)9789819615278

Text-based person retrieval primarily aims to retrieve the images of target persons represented by a given text query. In this task, how to effectively align images and text globally and locally is an important challenge. At the same time, since multiple modalities are involved, reducing the differences between different modalities is also an important challenge. Existing work has focused more on reconstructing an image rather than the semantic consistency between the image and text modalities. therefore, we introduce the Cross-Modal Mask and Detail Alignment (CMDA) framework to address these challenges. Under this framework, our proposed Cross-Modal Mask Alignment module (CMA) semantically aligns features generated by supplementing randomly masked image/text with another modality text/image. Additionally, to narrow the gap between the image and text modalities, we designed the Cross-Modal Detail Alignment module (CDA), which establishes connections between images and texts and facilitates interactions between these two different modalities. Experimental results show that our model exhibits outstanding performance across multiple public datasets, i.e., CUHK-PEDES, ICFG-PEDES, and RSTPReID. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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Accelerating processing of Scale-Free Graphs on Massively-parallel architectures 17th

Accelerating Processing of Scale-Free Graphs on Massively-Pa...

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17th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Chernoskutov, Mikhail Ural Fed Univ Krasovskii Inst Math & Mech Ekaterinburg Russia

ISBN: (纸本)9783319654829;9783319654812

processing of big scale-free graphs on parallel architectures with high parallelization opportunities connected with a lot of overheads. Due to skewed degree distribution each thread receives different amount of computational workload. In this paper we present a method devoted to address this challenge by modificating CSR data structure and redistributing work across threads. the method was implemented in breadth-first search and single source shortest path algorithms for GPU architecture.

关键词： parallel processing Graph algorithms Workload balancing

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High-Performance Graphics in Racket with DirectX 17th

High-Performance Graphics in Racket with DirectX

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17th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Bossard, Antoine Kanagawa Univ Grad Sch Sci 2946 Tsuchiya Hiratsuka Kanagawa 2591293 Japan

ISBN: (纸本)9783319654829;9783319654812

Nowadays, modern computer systems rely heavily on parallel processing, and not only because of the multicore CPUs bundled with any machine, even mobile devices, but more and more thanks to the parallel processing capacities of graphics processing units (GPU), general-purpose computing on graphics processing units (GPGPU) being one example. In this paper, relying on the DirectX 12 framework, we propose an innovative approach to enable parallel processing for graphical rendering on both the CPU and GPU for the popular Racket functional programming language (formerly PLT Scheme), and importantly without compromising Racket's usability and programmer-friendliness. Our performance evaluations show significant improvements with respect to execution time (x3 speed-up in some cases), CPU utilisation time (reduced by as much as 80% in some scenarios) and the frame rate when using moving graphics.

关键词： GPU parallel processing Functional Programming Scheme

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the Open Community Runtime on the Intel Knights Landing Architecture 17th

The Open Community Runtime on the Intel Knights Landing Arch...

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17th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Dokulil, Jiri Benkner, Siegfried Yaghob, Jakub Univ Vienna Fac Comp Sci Vienna Austria Charles Univ Prague Dept Software Engn Prague Czech Republic

ISBN: (纸本)9783319654829;9783319654812

the Intel Xeon Phi Knights Landing manycore processor comes with new interesting features: on-chip high-bandwidth memory and several user-selectable NUMA configurations. In this paper, we look into how these affect applications that target the Open Community Runtime (OCR), an asynchronous tasked-based runtime system for future parallel architectures. We have extended our OCR runtime to make it NUMA aware and to allow it to use the high-bandwidth memory. We have conducted a range of experiments, comparing OpenMP, TBB, our OCR implementation, and the reference OCR implementation on different machine configurations using a memory intensive seismic simulation.

关键词： Open Community Runtime Knights Landing Intel Xeon Phi High-bandwidth memory parallel runtime systems NUMA

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Accelerating Exhaustive Pairwise Metagenomic Comparisons 17th

Accelerating Exhaustive Pairwise Metagenomic Comparisons

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17th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Perez-Wohlfeil, Esteban Torreno, Oscar Trelles, Oswaldo Univ Malaga Dept Comp Architecture Blvd Louis Pasteur 35 Malaga Spain

ISBN: (纸本)9783319654829;9783319654812

In this manuscript, we present an optimized and parallel version of our previous work IMSAME, an exhaustive gapped aligner for the pairwise and accurate comparison of metagenomes. parallelization strategies are applied to take advantage of modern multiprocessor architectures. In addition, sequential optimizations in CPU time and memory consumption are provided. these algorithmic and computational enhancements enable IMSAME to calculate near optimal alignments which are used to directly assess similarity between metagenomes without requiring reference databases. We show that the overall efficiency of the parallel implementation is superior to 80% while retaining scalability as the number of parallel cores used increases. Moreover, we also show that sequential optimizations yield up to 8x speedup for scenarios with larger data.

关键词： High Performance Computing Pairwise comparison parallel computing Next Generation Sequencing Metagenome comparison

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A Hybrid parallel Search Algorithm for Solving Combinatorial Optimization Problems on Multicore Clusters 17th

A Hybrid Parallel Search Algorithm for Solving Combinatorial...

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17th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Sanz, Victoria De Giusti, Armando Naiouf, Marcelo Natl Univ La Plata III LIDI Sch Comp Sci La Plata Argentina CIC Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn Buenos Aires DF Argentina

ISBN: (纸本)9783319654829;9783319654812

Multicore clusters are widely used to solve combinatorial optimization problems, which require high computing power and a large amount of memory. In this sense, Hash Distributed A* (HDA*) parallelizes A*, a combinatorial optimization algorithm, using the MPI library. HDA* scales well on multicore clusters and on multicore machines. Additionally, there exist several versions of HDA* that were adapted for multicore machines, using the Pthreads library. In this paper, we present Hybrid HDA* (HHDA*), a hybrid parallel search algorithm based on HDA* that combines message-passing (MPI) with shared-memory programming (Pthreads) to better exploit the computing power and memory of multicore clusters. We evaluate the performance and memory consumption of HHDA* on a multicore cluster, using the 15-puzzle as a case study. the results reveal that HHDA* achieves a slightly higher average performance and uses considerably less memory than HDA*. these improvements allowed HHDA* to solve one of the hardest 15-Puzzle instances.

关键词： parallel search algorithms Hybrid programming Multicore cluster Combinatorial optimization problems Hash Distributed A*

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Power-Performance Evaluation of parallel Multi-objective EEG Feature Selection on CPU-GPU Platforms 17th

Power-Performance Evaluation of Parallel Multi-objective EEG...

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17th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Jose Escobar, Juan Ortega, Julio Francisco Diaz, Antonio Gonzalez, Jesus Damas, Miguel Univ Granada Dept Comp Architecture & Technol CITIC Granada Spain

ISBN: (纸本)9783319654829;9783319654812

Heterogeneous CPU-GPU platforms include resources to benefit from different kinds of parallelism present in many data mining applications based on evolutionary algorithms that evolve solutions with time-demanding fitness evaluation. this paper describes an evolutionary parallel multi-objective feature selection procedure with subpopulations using two scheduling alternatives for evaluation of individuals according to the number of subpopulations. Evolving subpopulations usually provides good diversity properties and avoids premature convergence in evolutionary algorithms. the proposed procedure has been implemented in OpenMP to distribute dynamically either subpopulations or individuals among devices and OpenCL to evaluate the individuals taking into account the devices characteristics, providing two parallelism levels in CPU and up to three levels in GPUs. Different configurations of the proposed procedure have been evaluated and compared with a master-worker approach considering not only the runtime and achieved speedups but also the energy consumption between both scheduling models.

关键词： Heterogeneous subpopulations scheduling Energy-aware computing EEG classification Multi-objective feature selection GPU

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Configuring Concurrent Computation of Phylogenetic Partial Likelihoods: Accelerating Analyses Using the BEAGLE Library 17th

Configuring Concurrent Computation of Phylogenetic Partial L...

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17th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Ayres, Daniel L. Cummings, Michael P. Univ Maryland Ctr Bioinformat & Computat Biol College Pk MD 20742 USA

ISBN: (纸本)9783319654829;9783319654812

We describe our approach in augmenting the BEAGLE library for high-performance statistical phylogenetic inference to support concurrent computation of independent partial likelihoods arrays. Our solution involves identifying independent likelihood estimates in analyses of partitioned datasets and in proposed tree topologies, and configuring concurrent computation of these likelihoods via CUDA and opencL frameworks. We evaluate the effect of each increase in concurrency on throughput performance for our partial likelihoods kernel for a four-state nucleotide substitution model on a variety of parallel computing hardware, such as NVIDIA and AMD GPU5, and Intel multicore cPus, observing up to 16-fold speedups over our previous implementation. Finally, we evaluate the effect of these gains on an domain application program, MrBayes. For a partitioned nucleotide-model analysis we observe an average speedup for the overall run time of 2.1-fold over our previous parallel implementation, and 10-fold over the native MrBayes with SSE.

关键词： Bayes methods Biology computing Evolution (biology) Phylogeny Maximum likelihood estimation Multicore processing parallel programming High performance computing

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