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检索条件"任意字段=10th International Conference on Algorithms and Architectures for Parallel Processing"

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Scalable duplicate pruning strategies for parallel A* graph search

Scalable duplicate pruning strategies for parallel A* graph ...

引用

international Symposium on parallel and Distributed processing (IPDPS)

作者： N.R. Mahapatra S. Dutt Department of Electrical Engineering University of Minnesota Minneapolis MN USA

In parallel A* graph search on distributed-memory machines, different processors may perform significant duplicated work if inter-processor duplicates are not pruned. the only known method for duplicate pruning associates a particular processor with each distinct node of the search space using a suitable hash function. then duplicate nodes arising in different processors are transmitted to the same processor, and thereby pruned. there are two main drawbacks attributable to such an approach: (1) Load balance is determined solely by the hash function and is unsatisfactory. (2) Node transmissions for duplicate pruning are global; this can lead to hot spots in the network. We propose two different duplicate pruning techniques that outperform this hashing-only method by using: (1) separate algorithms for duplicate pruning and load balancing, and (2) a novel search space partitioning scheme that evenly spreads out the bandwidth requirement for pruning over the entire parallel architecture. Using the Traveling Salesman Problem (TSP) as a test case, we find that on a 10-dimensional nCUBE2 hypercube multicomputer, our pruning strategies yield a speedup improvement of more than 135% over previous methods that do not prune any duplicates, and more than 155% over the hashing-only pruning scheme.< >

关键词： Tree graphs Concurrent computing Space exploration Traveling salesman problems Production facilities Load management Bandwidth parallel architectures Testing Hypercubes

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Power Optimization for GPU Programs Based on Software Prefetching

Power Optimization for GPU Programs Based on Software Prefet...

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Yisong Lin Tao Tang Guibin Wang Key Laboratory of Science and Technology for National Defence of Parallel and Distributed Processing National University of Defense and Technology Changsha China

GPUs render higher computing unit density than contemporary CPUs and thus exhibit much higher power consumption despite its higher power efficiency. the power consumption has become an important issue that impacts CPU's applications, thereby necessitating the low power optimization technology for GPUs. Software prefetching is an efficient way to alleviate the memory wall problem which overlaps the computing and memory access latencies. However, software prefetching will cause some power overhead because it increases the number and density of the instructions. thus, we should consider the balance between the performance income and the power overhead when applying the optimization. To address this problem, in this paper we first analyze the multi-thread execution model of GPU and validate the potential space of software prefetching optimization. then we give the software prefetching method for GPU programs to improve the performance. Aiming at two different objects: energy optimization under performance constraint and performance optimization under power constraint, we discuss the optimization methods based on software prefetching and dynamic voltage scaling technologies. the experimental results show that our method can efficiently optimize the energy consumption (performance) under the performance (power) constraint.

关键词： Prefetching Graphics processing unit Optimization Power demand Registers

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Analogic cellular PDE machines

Analogic cellular PDE machines

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international Joint conference on Neural Networks (IJCNN)

作者： C. Rekeczky I. Szatmari P. Foldesy T. Roska Computer and Automation Research Institute Hungary Hungarian Academy of Science Budapest Hungary Jedlik Laboratories Department of Information Technology Pázmány Péter Catholic University Budapest Hungary

this paper gives an overview on analogic cellular array architectures that can also be used to approximate partial differential equations (PDEs). Cellular arrays are massively parallel computing structures composed of cells placed on a regular grid. these cells interact locally an th e array can have both local and global dynamics. the software of this architecture is an analogic algorithm that builds on analog and logical spatio-temporal instructions of the underlying hardware, that is a locally connected cellular nonlinear network (CNN). Within this framework two classes of PDEs, motivated also by image processing methodologies will be discussed: (i) reaction-diffusion (local) types and (ii) contrast modification (global) types. It will be shown that based on cellular diffusion and wave-computing formulations these classes can be approximated on existing CNN Universal Machine (CNN-UM) chips. thus, the last generation of stored program topographic array microprocessors with integrated sensing and computing could also be viewed as the first prototypes of analogic cellular PDE machines implemented on silicon.

关键词： Computer architecture Cellular neural networks Partial differential equations parallel processing Software algorithms Hardware Cellular networks Image processing Turing machines Microprocessors

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parallel and Pipelined CMA for High-Speed and Real-Time Optical Coherent Receivers

Parallel and Pipelined CMA for High-Speed and Real-Time Opti...

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international conference on Optical Communications and Networks (ICOCN)

作者： Yue Dong Liqian Wang Zhiguo Zhang Shanyong Cai Luming Li Jiangxing Chen Xing Liu State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China Information and Communications Branch Jiangxi Electric Power Company Nanchang China

In order to reduce the logic resources required by parallel polarization demultiplexing algorithm, an improvement scheme based on parallel structure of constant mode algorithm (PCMA) was proposed in this paper. We designed a new error accumulation method in this improvement scheme. After that, we make a simulation of our algorithm at a data rate of 10-Gb/s. the simulation results show that the optimized algorithm can accelerate the convergence speed and reduce the steady-state mean square error (MSE) and bit error rate (BER).

关键词： Signal processing algorithms Convergence Optical receivers Optical fiber communication Adaptive optics Optical polarization

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the implementation of GA processor with multiple operators, based on subpopulation architecture

The implementation of GA processor with multiple operators, ...

引用

international conference on Neural Information processing

作者： Minsok Cho Suckwoo Chung Duckjin Chung Integrated Circuit Research Laboratory School of Information & Communication Engineering Inha University Incheon South Korea

ISBN: (纸本)9810475241

We propose the hardware-oriented genetic algorithm processor with efficient exploration based on a subpopulation architecture for high-performance convergence and reducing computation time. We applied the steady-state model among continuous generation model, modified tournament selection, special survival condition and the parallelism of coarse-grain to our proposed GAP. In addition, the crossover operator selection method with respect to the convergence state of each subpopulation was newly employed. In order to implement the efficient hardware structure, the pipelined structure was used. the proposed GAP is implemented on the AGENT2000 board with EFP10K200SRC device.

关键词： Steady-state Hardware Convergence Electronic mail Constraint optimization Laboratories Genetic engineering Genetic algorithms Computer architecture parallel processing

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processing Millions of Single Cells by SHARP 20

Processing Millions of Single Cells by SHARP

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11th ACM conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)

作者： Wan, Shibiao Kim, Junil Fan, Yiping Won, Kyoung Jae St Jude Childrens Res Hosp Ctr Appl Bioinformat 332 N Lauderdale St Memphis TN 38105 USA Univ Copenhagen Biotech Res & Innovat Ctr Copenhagen Denmark

ISBN: (纸本)9781450379649

Single-cell technologies have received extensive attention from bioinformatics and computational biology communities due to their evolutionary impacts on uncovering novel cell types and intra-population heterogeneity in various domains of biology and medicine. Recent advances on single-cell RNA-sequencing (scRNA-seq) technologies have enabled parallel transcriptomic profiling of millions of cells. However, existing scRNA-seq clustering methods are lack of scalability, time-consuming and prone to information loss during dimension reduction. To address these concerns, we present SHARP [1], an ensemble random projection-based algorithm which is scalable to clustering 10 million cells. By adopting a divide-and-conquer strategy, a sparse random projection and two-layer meta-clustering, SHARP has the following advantages: (1) hyper-faster than existing algorithms;(2) scalable to 10-million cells;(3) accurate in terms of clustering performance;(4) preserving cell-to-cell distance during dimension reduction;and (5) robust to dropouts in scRNA-seq data. Comprehensive benchmarking tests on 20 scRNA-seq datasets demonstrate SHARP remarkably outperforms state-of-the-art methods in terms of speed and accuracy. To the best of our knowledge, SHARP is the only R-based tool that is scalable to clustering 10 million cells. With an avalanche of single cells in different tissues to be sequenced in multiple international projects like the Human Cell Atlas, we believe SHARP will serve as one of the useful and important tools for large-scale single-cell data analysis. Several potential future directions include while keeping the scalability and speed of SHARP, how to extend its functions into rare cell type detection and integrating single cell data from different platforms, experiments and conditions.

关键词： Single cell analysis 10 million cells random projection dimension reduction clustering

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GPU parallelization and Optimization of a Combustion Simulation Application

GPU Parallelization and Optimization of a Combustion Simulat...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Zhixiang Liao Yongzhou Liu Yonggang Che State Key Laboratory of High Performance Computing Institute for Quantum Information College of Computer National University of Defense Technology Changsha China

Graphics processing units (GPUs) are widely used in the area of scientific computing. While GPUs provide much higher peak performance, efficient implementation of real applications on the GPU architectures is still a non-trivial task. It is crucial to realize efficient solution algorithms that can better utilize GPU architectures. this paper presents our efforts in parallelizing and optimizing LESAP, a CFD application for scramjet combustion simulation, on NVIDIA GPUs. the GPU parallelization is realized based on the CUDA programming model, with a data-parallel implicit time-marching method that is efficient on the GPU architecture. Furthermore, shared memory and redundant calculation are proposed to reduce memory access overhead during GPU computation, and data transfer between CPU and GPU is optimized by packing the data to be transferred. the experimental results show that the GPU version, when runs on four V100 GPUs, achieves a speedup of 11.26 times compared to the CPU version that runs on two 24-core Intel Skylake Gold 6240R CPUs. Excellent parallel scalability across multiple GPUs is also observed.

关键词： Gold Smart cities Computational modeling Graphics processing units Computer architecture Combustion Central processing Unit

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Unlocking the Perfect Fit: ML Approach for Apparel Size Recommendation

Unlocking the Perfect Fit: ML Approach for Apparel Size Reco...

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international conference on Applied System Innovation (ICASI)

作者： Shashin Bhaskar Shishir Hebbar Ruthvik V Kumar Poorvi B H R Vanamala Department of Electronics and Communication Engineering PES University Bengaluru Karnataka India

ISBN: (数字)9798350394924

ISBN: (纸本)9798350394931

this study is based on the exploration of an e-commerce apparel size recommendation system through machine learning and image processing, evaluating three backbone architectures for YOLOv8—EfficientNet, ResNet, and CSPDarknet—identifying CSPDarknet as the most effective. the novelty of the model is emphasized by its conjunction with a custom-gathered dataset, further optimized by CLAHE for data augmentation and the AdamW optimizer. Validated with precise measurements on a set of numerous diverse individuals, the approach integrates C2f and SPPF blocks to enhance gradient flow and model efficiency, significantly advancing the online shopping experience by optimizing the analysis of one single user image.

关键词： Technological innovation Analytical models Image processing Machine learning Predictive models Data augmentation User experience

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Transient analysis of large linear dynamic networks on hybrid GPU-multicore platforms

Transient analysis of large linear dynamic networks on hybri...

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Annual IEEE Northeast Workshop on Circuits and Systems (NEWCAS)

作者： Xue-Xin Liu Sheldon X.-D. Tan Zao Liu Hai Wang Tailong Xu Department Electrical Engineering University of California Riverside CA USA School of Microelectronics and Solid State Electronics UESTC Chendu China School of Electronics and Information Engineering Anhui University Hefei China

A new transient analysis method is proposed for general linear dynamic networks, such as on-chip power grid networks, using hybrid GPU-based multicore platform. the new method, called ETBR-GPU, first performs sampling-like reduction on the original circuit matrices where the frequency domain responses at different frequency points can be calculated in parallel on multicore CPU. After the reduction, the reduced circuit matrices, which are dense but well suitable for GPU's data parallel computing, are simulated on GPU. Such reduction based simulation technique is very amenable for parallelization on the hybrid multicore and GPU platforms, where coarse-grained task-level and fine-grained lightweight-thread level parallelism can be both exploited. the proposed method is very general, since it can analyze any linear networks with complicated structures and macromodels, and it does not assume some structure properties in order to build problem-specific preconditioners, as many iterative solvers do. Experiments show that the new method achieves about one or two orders of magnitude speedup when compared to the general LU-based simulation method on some recently published IBM power grid benchmark circuits.

关键词： Graphics processing unit Transient analysis Power grids Integrated circuit modeling Instruction sets Central processing Unit Multicore processing

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A new linguistic approach to sentiment automatic processing

A new linguistic approach to sentiment automatic processing

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IEEE international conference on Cognitive Informatics

作者： Zied Kechaou Mohamed Benammar Adel M. Alimi REGIM: REsearch Group on Intelligent Machines National School of Engineers (ENIS) University of Sfax Sfax Tunisia

With the growth of the Web2.0, e-commerce has become very popular in use, many websites offer the opportunity to make sales online and give the opportunity to get own an online review about objects, persons, and products. New opportunities and challenges arise as people can now actively use information technologies to seek and understand other people's opinions (sentiments) when to making their choices. In this paper, we propose a new two-step approach for an automatic sentiment processing, consisting a first step dealing with extract the subjective portions of reviews and a second one which determines the review overall sentiment by identifying whether a review appreciates (positive) or desappreciates (negative) its purpose. this approach consists of a four levels processing chain operating in a parallel way (i.e syntactic, semantic, linguistic and sentiment analysis).

关键词： Semantics Book reviews Pragmatics Motion pictures Syntactics Natural language processing Classification algorithms

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