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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

共 10259 条记录，以下是71-80 订阅

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Impact of Processor Frequency Scaling on performance and Energy Consumption for WZ Factorization on Multicore Architecture 18

Impact of Processor Frequency Scaling on Performance and Ene...

引用

18th conference on computer Science and Intelligence Systems, FedCSIS 2023

作者： Bylina, Beata Bylina, Jaroslaw Piekarz, Monika Institute of Computer Science Marie Curie-Sklodowska University Pl. M. Curie-Skodowskiej 5 Lublin20-031 Poland

ISBN: (纸本)9788396744784

With the growing demand for computing power, new multicore architectures have emerged to provide better performance. Reducing their energy consumption is one of the main challenges in achieving high performance computing. Current research trends develop new software and hardware techniques to achieve the best performance and energy compromise. In this work, we investigate the effect of processor frequency scaling using Dynamic Voltage Frequency Scaling on performance and energy consumption for the WZ factorization. This factorization is implemented both without optimization techniques and with strip mining. This technique involves transforming the program loop to improve program performance. Based on time and energy tests, we have shown that for the WZ factorization algorithm, regardless of the presence of manual optimization, it pays to reduce the frequency to save energy without losing performance. The conclusion can be extended to analogous algorithms - also having a high ratio of memory access to computational operations. © 2023 Polish Information Processing Society.

关键词： Energy utilization

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Summary of Semantic Segmentation in Small Samples 21

Summary of Semantic Segmentation in Small Samples

引用

21st International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Yixin, Zhang Wenyu, Chen Yuting, Zeng School of Computer Science and Engineering School of Cybersecurity University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

Small-sample semantic segmentation is an important task in computer vision. Its purpose is to achieve accurate pixel-level classification in the case of very limited annotation data. Small sample scenarios usually do not have enough annotated data to train deep learning models, so the models are easy to overfit and have insufficient generalization performance. It mainly includes techniques based on meta-learning, generative models and transfer learning. Meta-learning improves the efficiency of the model by learning how to quickly adapt to the new task, and the generative model expands the data set by generating synthetic data, while transfer learning helps improve the segmentation performance by extracting knowledge from tasks in related fields. © 2024 IEEE.

关键词： Contrastive Learning

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high-Density Building Form Generation Considering Daylight performance 42

High-Density Building Form Generation Considering Daylight P...

引用

42nd Annual conference of the Association for computer Aided Design in Architecture, ACADIA 2022

作者： Xiao, Jun Liu, Yubo Deng, Qiaoming School of Architecture South China University of Technology China State Key Laboratory of Subtropical Building Science China

ISBN: (纸本)9798986080581

high-quality and uniform daylighting is important for frequently occupied buildings such as classrooms and large offices in which people have stable, uniformly distributed seats. It is difficult to achieve required daylighting in high-density contemporary buildings that receive daylight by side windows. Further, practioners of contemporary design cannot correctly predict the daylight performance in a complex-form building with the mixed use of side windows and skylights. In this case, aiming for a high-density building designed with high-quality daylighting, this article develops a building form generation program for daylight performance optimization integrating Cellular Automata (CA) and Genetic Algorithm (GA), tools that can provide a global daylighting optimization through the balance of competition and concession of agents. The CA model provides randomness and structural restriction, while GA provide optimization and convergence by evaluating and selecting a series of CA models. The model applies designed CA rules on daylighting and a mathematic proxy model for daylight performance in GA fitness calculation. Validation and calibration are included to gather statistics of building floor parameters, which are necessary for this program to provide more precise predictions. The program is examined on the site located in southeast China. As a result, the proxy model maintains a low predictive error with R2 higher than 0.67 and 1/100 computational time compared to real-time simulation, and the high-density generative result provide 69% eligible rooms under the British daylighting standard. © 2023 Hybrids and Haecceities - Proceedings of the 42nd Annual conference of the Association for computer Aided Design in Architecture, ACADIA 2022. All rights reserved.

关键词： Genetic algorithms

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Machine Learning-Based Prediction of MPI Communication Time in high-performance Computing Applications 6

Machine Learning-Based Prediction of MPI Communication Time ...

引用

6th International conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Wang, Zhuang Zhuang, Yuan Zeng, Yunhui Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China

ISBN: (纸本)9798331541750

The rapid advancements in high-performance computing (HPC) have made large-scale parallel computing feasible. As a commonly used parallel programming model, Message Passing Interface (MPI) plays a crucial role in HPC systems and directly affects the performance and efficiency of these applications. Therefore, modeling and predicting the performance of MPI communication is critically important. This paper utilizes a machine learning-based approach to predict MPI communication time. It uses performance analysis tools to collect real-time communication data and select critical features related to execution time. The data is categorized, then, for each type of MPI communication, a dedicated regression model is trained. Firstly, XGBoost and Artificial Neural Networks (ANN) are employed for function level modeling. Then, fusion techniques are used to combine XGBoost and ANN models to improve prediction accuracy. Finally, these function level models are integrated into an application level model to predict the overall communication time. This model is evaluated using CG, FT, and CPMD. Experimental results show that the average prediction errors of the model are 12.6%, 13.2%, and 13.5%, with a minimum error of 7%. © 2024 IEEE.

关键词： Prediction models

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Optimization of Convolution Operators Using Image-to-Column Transformation and SGEMM 21

Optimization of Convolution Operators Using Image-to-Column ...

引用

21st International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Changlu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519254

Efficient convolution operations are vital for deep learning but are frequently computationally demanding. This paper introduces an optimization strategy leveraging the Image-to-Column (im2col) transformation in combination with Single-Precision General Matrix Multiplication (SGEMM) to reformulate convolution as matrix multiplication. The approach incorporates advanced CUDA techniques, such as shared memory utilization and tiling, to further enhance performance. Experimental results demonstrate runtime reductions of up to 95%, underscoring the method's effectiveness for large-scale datasets and its value in accelerating deep learning workloads. © 2024 IEEE.

关键词： Convolution

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Towards high-performance Transactions via Hierarchical Blockchain Sharding 30th

Towards High-performance Transactions via Hierarchical Block...

引用

30th European conference on Parallel and Distributed Processing (Euro-Par)

作者： Tang, Haibo Zhang, Huan Zhang, Zhenyu Zhang, Zhao Jin, Cheqing Zhou, Aoying East China Normal Univ Shanghai Peoples R China Minist Educ Engn Res Ctr Blockchain Data Management Shanghai Peoples R China

ISBN: (纸本)9783031695766;9783031695773

Blockchain sharding, a promising approach to improve system performance, divides the network into several small parallel working shards. However, the performance of existing sharded blockchain systems may degrade seriously due to the existence of cross-shard transactions. To overcome such drawbacks, we propose a blockchain system called HieraChain to process transactions with robust cross-shard transactions tolerance, based on a novel hierarchical sharding architecture. The upper-layer shards order the cross-shard transactions and the participants process them asynchronously to pipeline the transactions ordering. Furthermore, HieraChain proposes an optimized locality-aware protocol to trade off the local access patterns and the induced remote access events. Extensive experimental results demonstrate that HieraChain outperforms the state-of-the-art approaches significantly in the presence of cross-shard transactions, achieving up to 3x and 2x higher throughput than Saguaro and SharPer under general workload respectively. Moreover, our locality-aware approach further reduces transaction latency by 68 % and 51% compared to our basic approach and traditional baselines, respectively.

关键词： cross-shard transaction hierarchical sharding locality-awareness blockchains

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CaRV - Accelerating program Optimization through Capture, Replay, Validate

CaRV - Accelerating Program Optimization through Capture, Re...

引用

2023 International conference on high performance Computing, Network, Storage, and Analysis, SC Workshops 2023

作者： Barakhshan, Parinaz Eigenmann, Rudolf University of Delaware NewarkDE United States

ISBN: (纸本)9798400707858

This paper presents a new methodology and tool that speeds up the process of optimizing science and engineering programs. The tool, called CaRV (Capture, Replay, and Validate), enables users to experiment quickly with large applications, comparing individual program sections before and after optimizations in terms of efficiency and accuracy. Using language-level checkpointing techniques, CaRV captures the necessary data for replaying the experimental section as a separate execution unit after the code optimization and validating the optimization against the original program. The tool reduces the amount of time and resources spent on experimentation with long-running programs by up to two orders of magnitude, making program optimization more efficient and cost-effective. © 2023 ACM.

关键词： Cost effectiveness

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Practical Implementation of Q-Learning and Object Detection for Mobile Robot Path Planning

Practical Implementation of Q-Learning and Object Detection ...

引用

2024 International conference on Advanced Robotics and Intelligent Systems, ARIS 2024

作者： Valcourt, John P. Chandler, Franya M. Avrelus, Chamma Lee, Jou-Yi Güllü, Ali Ihsan Shah, Syed H. Yuan Ze University Dept. of Computer Science and Engineering Taoyuan Taiwan Yuan Ze University International Bachelor Program in Ece Taoyuan Taiwan Yuan Ze University Mechanicai/Industrial Engineering Taoyuan Taiwan Yuan Ze University International Bachelor Program in Engineering Taoyuan Taiwan Gebze Technical University Defense Electronics Engineering Kocaeli Turkey

ISBN: (纸本)9798350362572

Expanding on an earlier study that assessed the performance of a Q-learning approach for solving the path planning problem for mobile robots, this research implemented the RL approach in a real-world setting employing the RoboMas-ter EP Core. The robotic system also included object detection and recognition through the robot's sensors and a pre-trained YOLOv9 model. The robot navigated to predefined target points while avoiding stationary obstacles. The Q-learning algorithm was trained using the Google Colaboratory platform. Experi-ments conducted at various speeds identified an optimal speed and high success rates in obstacle avoidance and target region accuracy were achieved. Additionally, the object detection sys-tem demonstrated strong performance in real-time applications. Despite these successes, challenges such as high friction and multitasking inefficiencies were identified. Future research should address these limitations by enhancing control systems and the robot's multitasking capabilities, as well as using a computer with better processing power to improve overall system performance further. © 2024 IEEE.

关键词： Robot programming

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AstroMLab 2: AstroLLaMA-2-70B Model and Benchmarking Specialised LLMs for Astronomy

AstroMLab 2: AstroLLaMA-2-70B Model and Benchmarking Special...

引用

2024 Workshops of the International conference for high performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Pan, Rui Nguyen, Tuan Dung Arora, Hardik Accomazzi, Alberto Ghosal, Tirthankar Ting, Yuan-Sen University of Illinois Urbana-Champaign Department of Computer Science Urbana United States University of Pennsylvania Department of Computer and Information Science PhiladelphiaPA19104 United States Indian Institute of Technology Patna India Harvard University Center for Astrophysics Cambridge United States Oak Ridge National Laboratory National Center for Computational Sciences Oak RidgeTN United States The Ohio State University Department of Astronomy Columbus United States

ISBN: (纸本)9798350355543

Continual pretraining of large language models on domain-specific data has been proposed to enhance performance on downstream tasks. In astronomy, the previous absence of astronomy-focused benchmarks has hindered objective evaluation of these specialized LLM models. Leveraging a recent initiative to curate high-quality astronomical MCQs, this study aims to quantitatively assess specialized LLMs in astronomy. We find that the previously released AstroLLaMA series, based on LLaMA-2-7B, underperforms compared to the base model. We demonstrate that this performance degradation can be partially mitigated by utilizing high-quality data for continual pretraining, such as summarized text from arXiv. Despite the observed catastrophic forgetting in smaller models, our results indicate that continual pretraining on the 70B model can yield improvements. However, the current supervised fine-tuning dataset still constrains the performance of instruct models. In conjunction with this study, we introduce a new set of models, AstroLLaMA-3-8B and AstroLLaMA-2-70B, building upon the previous AstroLLaMA series. © 2024 IEEE.

关键词： astronomy-tailored LLMs continual pretraining quantitative evaluation

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Personalized Recommendation Algorithm Based on Knowledge Graphs with high-Order Information 42nd

Personalized Recommendation Algorithm Based on Knowledge Gra...

引用

42nd National conference on Theoretical computer Science

作者： Zhang, Siyao Wang, Zhihui Hu, Jinru Chen, Jianrui Shaanxi Normal Univ Sch Comp Sci Xian 710199 Peoples R China

ISBN: (纸本)9789819614899;9789819614905

With the rapid development of network technology, recommendation systems attract increasing research because of its wide applications in e-commerce. Nevertheless, most existing recommendation models based on graph neural networks do not consider the transitivity of subgraph structures in interactive data. This makes the models unable to capture the complex dependencies and mutual influences between users and items, resulting in the inability to achieve high-quality personalized recommendations. To address the above challenge, we propose a novel recommendation algorithm based on knowledge graph with high-order graph convolutional network, named KG(2)CN. Firstly, we introduce the subgraph structure on the knowledge graph to capture high-order contextual information between users and items. Secondly, the mined subgraph information and graph convolutional network are combined to learn high-order features of users and items. Finally, the decoder is applied to predict the ratings of target users on the uninteracted items, thereby recommending the Top-K items. Experimental results on the Book-Crossing and *** datasets show that the proposed KG(2)CN obtains better performance in F1 score and AUC metrics.

关键词： Recommendation Algorithm Knowledge Graph Graph Convolutional Network high-order Pattern

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