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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161-170 订阅

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autoGEMM: Pushing the Limits of Irregular Matrix Multiplication on Arm Architectures 24

autoGEMM: Pushing the Limits of Irregular Matrix Multiplicat...

引用

2024 International conference for high performance Computing, Networking, Storage and Analysis

作者： Wu, Du Meng, Jintao Zhu, Wenxi Deng, Minwen Wang, Xiao Luo, Tao Wahib, Mohamed Wei, Yanjie Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Tokyo Inst Technol Tokyo Japan RIKEN Ctr Computat Sci Kobe Hyogo Japan Tencent AI Lab Shenzhen Peoples R China Oak Ridge Natl Lab Oak Ridge TN USA ASTAR Inst High Performance Comp Singapore Singapore

ISBN: (数字)9798350352917

ISBN: (纸本)9798350352924;9798350352917

This paper presents an open-source library that pushes the limits of performance portability for irregular General Matrix Multiplication (GEMM) on the widely-used Arm architectures. Our library, autoGEMM, is designed to support a wide range of Arm processors: from edge devices to HPC-grade CPUs. autoGEMM generates optimized kernels for various hardware configurations by auto-combining fragments of autogenerated micro-kernels that employ hand-written optimizations to maximize computational efficiency. We optimize the kernel pipeline by tuning the register reuse and the data load/store overlapping. In addition, we use a dynamic tiling scheme to generate balanced tile shapes. Finally, we position autoGEMM on top of the TVM framework where our dynamic tiling scheme prunes the search space for TVM to identify the optimal combination of parameters for code optimization. Evaluations on five different classes of Arm chips demonstrate the advantages of autoGEMM. For small matrices, autoGEMM achieves 98% of peak and up to 2.0x speedup over state-of-the-art libraries such as LIBXSMM and LibShalom. For irregular matrices (i.e. tall skinny and long rectangles), autoGEMM is 1.3-2.0x faster than widely-used libraries such as OpenBLAS and Eigen. autoGEMM is publicly available at: https://***/wudu98/autoGEMM.

关键词： performance evaluation Codes Shape Pipelines computer architecture Libraries Registers Kernel Optimization Tuning

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high Definition Flame Image Recognition Technology based on Improved YOLOv8 2

High Definition Flame Image Recognition Technology based on ...

引用

2nd International conference on computer Vision and Intelligent Technology

作者： Long, Yuehui Shen, Jiaxiang Ji, Renxin Yan, Hualong China Fire & Rescue Inst Beijing Peoples R China

ISBN: (纸本)9798331540050;9798331540043

Aiming at the problem that conventional YOLOv8 model cannot recognize high definition flame images taken by UAV, this paper proposes a high definition flame recognition method based on the improved YOLOv8 model. By introducing GhostNetV2 module into the YOLOv8 backbone network, this method greatly increases the model parameters and improves the feature extraction capability and flame recognition capability of the model while speeding up the fire detection speed. The Efficient Local Attention (ELA) mechanism is added to further enhance the perception of flame characteristics. By introducing the Slicing Aided Hyper Inference(SAHI) Framework, the detection rate of small targets has been significantly improved. Furthermore, we have enhanced SAHI based on this, resulting in a substantial increase in its speed. The test results show that the performance of the improved YOLOv8 intersects with the YOLOv8n model, and the mAP50 and MAP50-95 improve by 4.0% and 6.5% respectively. Compared with SAHI, the improved SAHI speed increases by 151.97% on average, which effectively realizes the balance between model speed and accuracy, and has broad application value in UAV firefighting tasks.

关键词： flame detection small target detection YOLOv8 SAHI

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Anonymous and Efficient Authentication Scheme for Privacy-Preserving Federated Cross Learning 20th

Anonymous and Efficient Authentication Scheme for Privacy-Pr...

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20th International conference on Intelligent Computing (ICIC)

作者： Li, Zeshuai Liang, Xiaoyan Hebei Univ Key Lab High Trusted Informat Syst Hebei Prov Baoding Peoples R China Hebei Univ Sch Cyber Secur & Comp Baoding Peoples R China

ISBN: (纸本)9789819756056;9789819756063

Federated learning (FL) reduces privacy risks by allowing participants to train models locally without transmitting raw data to a central server. However, existing research suggests that adversaries are able to obtain data information by analyzing successive model updates, and can also violate privacy by inferring a link between the data and the client. The most advanced solutions to the problem of privacy leakage are mainly differential privacy (DP) and cryptography techniques. Whereas, the implementation of DP tends to degrade data utility, while cryptographic techniques often incur prohibitive computational costs and communication overheads. To solve these challenges, we propose a ring signature anonymous authentication scheme based on elliptic curve cryptography, which ensures high computing efficiency and data utility and privacy security. We provide formal security proofs and analysis. Moreover, performance evaluations illustrate the superior computational efficiency of our proposed protocol. Tailored encryption for parameter cross-training and tracking mechanisms for identifying malicious actors are proposed to align with federated cross learning (FedCross). Comprehensive empirical evaluations demonstrate that the anonymous authentication scheme does not compromise model performance and provides robust privacy safe-guards.

关键词： Privacy preserving Ring signature anonymous authentication FedCross

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Predictive Distributed Learning based on Stability and Complexity Measures

Predictive Distributed Learning based on Stability and Compl...

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2024 International conference on Science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Igodan, Efosa Charles Obe, Olumide O. Thompson, A.-F. Owolafe, Otasowie Usiosefe, Linda Osarumen Katyo, Peter University of Benin Faculty of Physical Sciences Dept. of Computer Science Benin City Nigeria University of Technology School of Computing Federal Dept. of Computer Science Akure Nigeria University of Technology School of Computing Dept. of Cyber Security Akure Nigeria Nigerian Custom Service Risk and Management Unit ICT - Modernization Department Abuja Nigeria

ISBN: (纸本)9798350358155

Due to the inherent properties of high dimensional datasets, the majority of feature selection methods already in use do not scale well and eventually become inapplicable. As a result of this limitation, the performance of most classifiers are degraded. The objective of this paper is to propose an improved distributed feature selection technique by sample while maintaining class distribution towards obtaining optimal subsets using data complexity-stability measures. Whilst the Kuncheva and Jaccard were applied in obtaining stable subsets, the Fisher's Discriminant Ratio was used to discriminate good candidate features from bad candidate features. The performance of the implementation framework was demonstrated using Brain Tumor Microarray datasets (using the original and the Synthetic Minority Oversampling Technique - induced datasets). The experiments contrasted the accuracy of three machine learning classifiers (decision tree, support vector machines and naive Bayes), trained with subsets of features generated by three selectors (information gain, gain ratio, and Chi-Squared). We observed that the highest accuracy percentages (100%, 100%, and 99.5%, respectively) were obtained by SVM, decision tree and naive Bayes respectively when the distributed learning techniques combined with data-complexity-stability measures were applied compared to existing techniques. Additionally, the stability of the feature subsets generated showed significant reduction both in the runtime complexities (2.97sec, 3.94sec, and 4.54sec), and the percentage of the number of selected features (0.52%, 1.39%, and 1.43%) with an alpha value of 0.75 respectively. These results showed that our proposed method achieved maximum performance and has future prospects. © 2024 IEEE.

关键词： Contrastive Learning

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Accelerating Medical Visual Question Answering by Image Rearrangement 21

Accelerating Medical Visual Question Answering by Image Rear...

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

作者： Han, Wang Lei, Zhao Center for Future Media The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610000 China Sichuan Artificial Intelligence Research Institute Yibin China

ISBN: (纸本)9798331519254

Medical Visual Question Answering is designed for supporting clinical diagnosis by leveraging advanced language and visual comprehension ability. While Large Vision-Language Models offer medical visual question answering powerful generative and interpretative capabilities, they encounter significant computational challenges due to quadratic complexity that scales with sequence length, especially as image embeddings dominate the input sequence. To address this, we propose an Image Rearrangement algorithm, which reduces the number of image embeddings with minimal impact on model performance. The Image Rearrangement algorithm aggregates similar image embeddings based on their relative alignment to text embeddings. We evaluate the Image Rearrangement algorithm method across three distinct datasets using two medical visual question answering models, observing that the Image Rearrangement algorithm achieves substantial computational savings while maintaining diagnostic accuracy. © 2024 IEEE.

关键词： Visual languages

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Multithreaded parallelism for heterogeneous clusters of QPUs 39

Multithreaded parallelism for heterogeneous clusters of QPUs

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39th International conference on high performance Computing, ISC high performance 2024

作者： Seitz, Philipp Geiger, Manuel Mendl, Christian B. Technical University of Munich TUM School of Computation Information and Technology Department of Computer Science Boltzmannstraße 3 Garching85748 Germany Technical University of Munich TUM School of Computation Information and Technology Department of Computer Science TUM Institute for Advanced Study Boltzmannstraße 3 Garching85748 Germany

ISBN: (纸本)9783982633602

In this work, we present MILQ, a quantum unrelated parallel machines scheduler and cutter. The setting of unrelated parallel machines considers independent hardware backends, each distinguished by differing setup and processing times. MILQ optimizes the total execution time of a batch of circuits scheduled on multiple quantum devices. It leverages state-of-the-art circuit-cutting techniques to fit circuits onto the devices and schedules them based on a mixed-integer linear program. Our results show a total improvement of up to 26 % compared to a baseline approach. © 2024 Research Paper Proceedings of the ISC high performance 2024. All rights reserved.

关键词： Integer programming

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Investigating Resilience of Loops in HPC programs: A Semantic Approach with LLMs

Investigating Resilience of Loops in HPC Programs: A Semanti...

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2024 IEEE high performance Extreme Computing conference, HPEC 2024

作者： Jiang, Hailong Zhu, Jianfeng Fang, Bo Chen, Chao Guan, Qiang Kent State University Department of Computer Science KentOH United States Pacific Northwest National Laboratory RichlandWA United States Intel Corporation Santa ClaraCA United States

ISBN: (纸本)9798350387131

Transient hardware faults, resulting from particle strikes, are significant concerns in highperformance Computing (HPC) systems. As these systems scale, the likelihood of soft errors rises. Traditional methods like Error-Correcting Codes (ECCs) and checkpointing address many of these errors, but some evade detection, leading to silent data corruptions (SDCs). This paper evaluates the resilience of HPC program loops, which are crucial for performance and error handling, by analyzing their computational patterns, known as the thirteen dwarfs of parallelism. We employ fault injection techniques to quantify SDC rates and utilize Large Language Models (LLMs) with prompt engineering to identify the loop semantics of the dwarfs in real source code. Our contributions include defining and summarizing loop patterns for each dwarf, quantifying their resilience, and leveraging LLMs for precise identification of these patterns. These insights enhance the understanding of loop resilience, aiding in the development of more resilient HPC applications. © 2024 IEEE.

关键词： Semantics

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SSHRF-GAN: Spatial-Spectral Joint high Receptive Field GAN for Old Photo Restoration 6th

SSHRF-GAN: Spatial-Spectral Joint High Receptive Field GAN f...

引用

6th Chinese conference on Pattern Recognition and computer Vision (PRCV)

作者： Wen, Duren Li, Xueming Zhang, Yue Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9789819985456;9789819985463

Old photo restoration is a challenging problem since it suffers from mixed degradation like noise, blurriness, scratch, dots, or color fading during its preserving or digitalizing process. The common practice of old photo restoration is to apply single degradation restoring methods sequentially, but it cannot restore the old images at one time. The existing mixed degradation restoring methods are designed in a complex architecture or training paradigm. To tackle this problem in a simple way, we propose SSHRF-GAN: Spatial-Spectral joint high Receptive Field GAN. It contains three independent modules for the mixed degradation that appeared in old photos: (1) high Receptive Field Fast Convolution block for noise and blurriness restoration;(2) Partial convolution block for scratch, dots, and cracks inpainting and (3) Image-wise histogram attention for color tone mapping. We evaluate our methods on synthesized datasets and gather real old data. The result shows that our simple network has a comparative performance with those sequential applied single degradation restoration models and previously proposed complex models.

关键词： Image restoration Combined degradation Spatial-spectral joint convolutional network

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Accel-GCN: high-performance GPU Accelerator Design for Graph Convolution Networks 42

Accel-GCN: High-Performance GPU Accelerator Design for Graph...

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42nd IEEE/ACM International conference on computer-Aided Design (ICCAD)

作者： Xie, Xi Peng, Hongwu Hasan, Amit Huang, Shaoyi Zhao, Jiahui Fang, Haowen Zhang, Wei Geng, Tong Khan, Omer Ding, Caiwen Univ Connecticut Storrs CT 06269 USA Univ Rochester Rochester NY 14627 USA Synopsys Mountain View CA USA

ISBN: (纸本)9798350322255

Graph Convolutional Networks (GCNs) are pivotal in extracting latent information from graph data across various domains, yet their acceleration on mainstream GPUs is challenged by workload imbalance and memory access irregularity. To address these challenges, we present Accel-GCN, a GPU accelerator architecture for GCNs. The design of Accel-GCN encompasses: (i) a lightweight degree sorting stage to group nodes with similar degree;(ii) a block-level partition strategy that dynamically adjusts warp workload sizes, enhancing shared memory locality and workload balance, and reducing metadata overhead compared to designs like GNNAdvisor;(iii) a combined warp strategy that improves memory coalescing and computational parallelism in the column dimension of dense matrices. Utilizing these principles, we formulate a kernel for SpMM in GCNs that employs block-level partitioning and combined warp strategy. This approach augments performance and multilevel memory efficiency and optimizes memory bandwidth by exploiting memory coalescing and alignment. Evaluation of AccelGCN across 18 benchmark graphs reveals that it outperforms cuSPARSE, GNNAdvisor, and graph-BLAST by factors of 1.17x, 1.86x, and 2.94x respectively. The results underscore AccelGCN as an effective solution for enhancing GCN computational efficiency. The implementation can be found on Github*.

关键词： Graph Convolution Network sparse matrix multiplication (SpMM) parallel computing GPUs

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Optimization of the online collaboration platform: an event queue management algorithm based on debounce and throttle techniques 4

Optimization of the online collaboration platform: an event ...

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4th International conference on computer Vision, Application, and Algorithm, CVAA 2024

作者： Xue, Tianbao Lan, Quanxiang School of Mathematics and Computer Science Panzhihua University Sichuan Panzhihua China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Against the backdrop of accelerating digital transformation, online collaboration platforms have become indispensable tools for remote work and learning. However, with the increase in the number of users, the problem of high concurrency event processing has become increasingly prominent. Therefore, this paper proposes an event queue management algorithm (EQA-DT) based on debouncing and throttling techniques to optimize the efficiency of event processing in high concurrency scenarios. The EQA-DT algorithm uses a debounce technique to control the frequency of event capture, ensuring that only the last event triggered by the user is recorded within a short period of time. It also uses a throttling technique to periodically push events in the queue to the server, limiting the frequency of events being pushed to the server. We compared the EQA-DT algorithm with traditional event processing methods in an experimental setting. The results showed that in high concurrency scenarios, the EQA-DT algorithm is superior to traditional event processing methods in terms of reducing server pressure, reducing the length of the event queue, and improving system response speed. This algorithm provides an effective solution for improving the performance of online collaboration platforms and lays the foundation for future research in this area. © 2025 SPIE.

关键词： Concurrency control

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