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检索条件"任意字段=7th International Conference on Parallel Processing and Applied Mathematics"

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11th international conference on parallel processing and applied mathematics, PPAM 2015

11th International Conference on Parallel Processing and App...

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11th international conference on parallel processing and applied mathematics, PPAM 2015

ISBN: (纸本)9783319321516

the proceedings contain 56 papers. the special focus in this conference is on Models, Algorithms, Energy Aspects of Computation, Scheduling for parallel Computing and Language-Based parallel Programming Models. the topics include: Virtualizing CUDA enabled GPGPUS on arm clusters;a distributed hash table for shared memory;mathematical approach to the performance evaluation of matrix multiply algorithm;a scalable numerical algorithm for solving tikhonov regularization problems;energy performance modeling with TIA and EML;considerations of computational efficiency in volunteer and cluster computing;parallel programs scheduling with architecturally supported regions;adaptive multi-level workflow scheduling with uncertain task estimates;divisible loads scheduling in hierarchical memory systems with time and energy constraints;extending Gustafson-barsis’s law for dual-architecture computing;free scheduling of tiles based on the transitive closure of dependence graphs;multi-threaded construction of neighbour lists for particle systems in openMP;high productivity and high performance;parallel ant brood graph partitioning in Julia;scalability model based on the concept of granularity;performance and power-aware modeling of MPI applications for cluster computing;running time prediction for web search queries;performance analysis of a parallel, multi-node pipeline for DNA sequencing;parallelising the computation of minimal absent words;modeling and simulations of edge-emitting broad-area semiconductor lasers and amplifiers;application of the parallel inmost platform to subsurface flow and transport modelling;genetic algorithm and exact diagonalization approach for molecular nanomagnets modelling and parallel Monte Carlo simulations for spin models with distributed lattice.

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Educational Images Colour and Memory Retention Conceptual Framework: A Review of Related Literature Conceptualizing Memory Retention of Educational Images based on its Color 24

Educational Images Colour and Memory Retention Conceptual Fr...

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7th international conference on Educational Technology Management, ICETM 2024

作者： Ainooson-Noonoo, Daniel Kweku Khedkar, Pallavi Parul Institute of Enginering and Technology Parul University Gujarat Vadadora India Department of Mathematics and ICT Education University of Cape Coast Central Region Cape Coast Ghana Parul University Gujarat Vadadora India

ISBN: (纸本)9798400717468

Images are often used as teaching resources in various ways to support teaching. Teachers may use different colors for the same image to teach the same concept. Color is a visual stimulus to the human eye, which may get a child’s attention or not. Attention is also a key element in memory retention, so it is necessary to determine the impact of the color format of an educational image on memory retention. Although there are a number of studies on color’s effects on memory retention, only a few have examined the color scheme of an image. Another gap identified in the review is that children have not been mainly involved in the various studies on how color affects memory retention. therefore, this review was needed. It is recommended that a scientific study of how the color scheme of a particular type of educational image affects a child’s memory retention is embarked on. this paper serves as an additional source of knowledge and aid in developing digital educational materials. © 2024 Copyright held by the owner/author(s).

关键词： Color image processing

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Using parallel Performance Data to Classify parallel Algorithms 15th

Using Parallel Performance Data to Classify Parallel Algorit...

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15th international conference on parallel processing and applied mathematics, PPAM 2024

作者： McKinsey, Michael Brink, Stephanie Pearce, Olga Texas A&M University College StationTX United States Lawrence Livermore National Laboratory LivermoreCA United States

ISBN: (纸本)9783031856969

Can we tell which parallel algorithm is executing by looking at the performance of the algorithm? In this work, we design and demonstrate a study for parallel algorithm classification of parallel sorting algorithms. We leverage Caliper to collect the performance data, and thicket for our exploratory data analysis (EDA). For classification, we develop a workflow with PyTorch and Scikit-learn to evaluate the effectiveness of support vector machines (SVMs), neural networks, and decision trees on parallel performance data. We demonstrate classification accuracy for these three different models of 95.3%, 94.0%, and 90.1% across five different parallel algorithm classes. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Data accuracy

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Exploring the Design Space for Message-Driven Systems for Dynamic Graph processing Using CCA 15th

Exploring the Design Space for Message-Driven Systems for...

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15th international conference on parallel processing and applied mathematics, PPAM 2024

作者： Chandio, Bibrak Qamar Brodowicz, Maciej Sterling, thomas Department of Intelligent Systems Engineering Indiana University Bloomington Bloomington United States

ISBN: (纸本)9783031856969

Computer systems that have been successfully deployed for dense regular workloads fall short of achieving scalability and efficiency when applied to irregular and dynamic graph applications. Conventional computing systems rely heavily on static, regular, numeric intensive computations while High Performance Computing systems executing parallel graph applications exhibit little locality, spatial or temporal, and are fine-grained and memory intensive. With the strong interest in AI which depend on these very different use cases combined with the end of Moore’s Law at nanoscale, dramatic alternatives in architecture and underlying execution models are required. this paper identifies an innovative non-von Neumann architecture, Continuum Computer Architecture (CCA), that redefines the nature of computing structures to yield powerful innovations in computational methods to deliver a new generation of highly parallel hardware architecture. CCA reflects a genus of highly parallel architectures that while varying in specific quantities (e.g., memory blocks), share a multiple of attributes not found in typical von Neumann machines. Among these are memory-centric components, message-driven asynchronous flow control, and lightweight out-of-order execution across a global name space. Together these innovative non-von Neumann architectural properties guided by a new original execution model will deliver the new future path for extending beyond the von Neumann model. this paper documents a series of interrelated experiments that together establish future directions for next generation non-von Neumann architectures, especially for graph processing. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： parallel architectures

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Character Recognition Using Convolutional Neural Networks Based on CUDA

Character Recognition Using Convolutional Neural Networks Ba...

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international Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Set Paing Tun Applied Mathematics and Artificial Intelligence Department National Research University “MPEI” Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

the article explores character recognition using convolutional neural networks (CNNs) optimized with the CUDA platform to enhance computational efficiency. It outlines the CNN architecture, methods for leveraging GPU-based parallel data processing, and presents experimental results derived from the MNIST dataset. the study highlights that implementing CUDA drastically reduces processing time while maintaining a high level of predictive accuracy. the findings emphasize the potential of GPU acceleration in handling intensive computational tasks, making it a promising approach for real-time applications in image recognition and machine learning.

关键词： Deep learning Training Energy consumption Graphics processing units parallel processing Real-time systems Energy efficiency Convolutional neural networks Character recognition Optimization

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the Impact of SYCL Data Management on Performance Portability 15th

The Impact of SYCL Data Management on Performance Portabil...

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15th international conference on parallel processing and applied mathematics, PPAM 2024

作者： Marowka, Ami Parallel Research Lab Tel-Aviv Israel

ISBN: (纸本)9783031856990

SYCL programming model does not guarantee performance portability across different architectures. However, the HPC community severely needs platform-independent performance portable applications more than ever. therefore, the main challenge of SYCL implementers and application developers is to look for direct or indirect solutions in order to improve the portability, performance, and performance portability of SYCL applications. In this paper we study and analyze the impact of the two main SYCL abstractions for data management, i.e., the unified shared memory and buffer-accessor approaches, on the three pillars of performance portability: portability, productivity, and performance. Experiments were carried out on state-of-the-art CPU and GPU platforms in order to shed light on the effect of different SYCL features on performance. the conclusions that emerged from this study show that by avoiding the use of SYCL performance portability inhibitors, it is possible to develop applications with a realistic level of performance portability. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Information management

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End-To-End High-Quality Transformer Object Detection Model applied to Human Head Detection 7th

End-To-End High-Quality Transformer Object Detection Model A...

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7th Chinese conference on Pattern Recognition and Computer Vision

作者： Zhou, Zhen Li, Rongchun Qiao, Peng Jiang, Jingfei Natl Univ Def Technol Sch Comp Natl Key Lab Parallel & Distributed Comp Changsha 410073 Peoples R China

ISBN: (纸本)9789819788576;9789819788583

Head detection is a challenging and widely applied object detection task. Although previous CNN-based head detectors have made good progress, the inherent locality of CNN restricts the extraction of global contextual information, which leads to low precision and recall rates in head detection. In this article, we propose an end-to-end high-quality head detector based on Transformer, which effectively models the contextual relationships between heads, other objects and the background. To extract and generate discriminative feature maps suitable for detecting small head targets, we incorporate specific CNN-based auxiliary detector heads for joint training. the GIoU-aware classification loss function is improved to generate bounding boxes with high localization quality and high classification confidence, and a feature fusion module is introduced to enhance the feature representation capabilities of the model. We conduct experiments on COCO 2017 dataset and Brainwash head dataset, and the results demonstrate that our method outperforms in both COCO generalized object detection and Brainwash head detection tasks compared to previous CNN-based detectors as well as other current mainstream Transformer-based object detection models.

关键词： Head detection Vision transformer Auxiliary detector heads GIoU-aware Feature fusion

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Euro-Par 2001 parallel processing : 7th international Euro-PAr conference, Manchester, UK, August 28

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2001年

作者： international Euro-Par conference

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Kernel Estimates of Pedestrian Density applied in Simulation of Recreational Pedestrian Movement 15th

Kernel Estimates of Pedestrian Density Applied in Simulati...

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15th international conference on parallel processing and applied mathematics, PPAM 2024

作者： Novotný, Tomáš Vacková, Jana Hrabák, Pavel Czech Technical University in Prague Faculty of Information Technology Thákurova 9 Prague 6160 00 Czech Republic

ISBN: (纸本)9783031857027

A model for simulating pedestrian recreational movement along a tourist trail is introduced. the model reduces the two-dimensional nature of bidirectional flow to one dimension while preserving the inter-agent interaction and density-induced velocity reduction. this is achieved by applying kernel estimates of pedestrian density to describe the pedestrian mass in the perceived surroundings of an agent. the applicability of the model is investigated by means of numerical simulations using three speed distribution scenarios and three arrival rate scenarios. the simulation results are in correspondence with expectations and recommendations related to capacity for day-use facilities in national parks (pedestrian level of service D). the model mimics the expected trail capacity behaviour represented by arrival rate, leading to speed deterioration and stoppages. Heterogeneity of the agents similarly as non-homogeneity of arrival rate leads to clogging at smaller average arrival rates. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Digital elevation model

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Beqi: Revitalize the Senegalese Wolof Language with a Robust Spelling Corrector

Beqi: Revitalize the Senegalese Wolof Language with a Rob...

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7th EAI international conference on Innovations and Interdisciplinary Solutions for Underserved Areas, InterSol 2024

作者： Mbaye, Derguene Diallo, Moussa Baamtu Dakar Senegal Université Cheikh Anta Diop Dakar Senegal

ISBN: (纸本)9783031864926

the progress of Natural Language processing (NLP), although fast in recent years, is not at the same pace for all languages. African languages in particular are still behind and lack automatic processing tools. Some of these tools are very important for the development of these languages but also have an important role in many NLP applications. this is particularly the case for automatic spell checkers. Several approaches have been studied to address this task and the one modeling spelling correction as a translation task from misspelled (noisy) text to well-spelled (correct) text shows promising results. However, this approach requires a parallel corpus of noisy data on the one hand and correct data on the other hand, whereas Wolof is a low-resource language and does not have such a corpus. In this paper, we present a way to address the constraint related to the lack of data by generating synthetic data and we present sequence-to-sequence models using Deep Learning for spelling correction in Wolof. We evaluated these models in three different scenarios depending on the subwording method applied to the data and showed that the latter had a significant impact on the performance of the models, which opens the way for future research in Wolof spelling correction. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Natural language processing systems

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