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HPC master design: experience from Pisa

HPC master design: experience from Pisa

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Marco Danelutto Dept. of Computer Science Univ. of Pisa

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

HPC is a widely used term, often referred to the applications, architectures and programming models and tools targeting highly parallel machines such as those of the *** lists. Recent advances in computing hardware resources require application of HPC techniques when using much smaller machines. Indeed, proper parallel programming tools and applications are needed also to exploit parallel hardware resources in personal computers (laptops, desktops, servers). This paper outlines key challenges in designing master’s degree programs in HPC and shares lessons learned from various experiences in developing and implementing such programs in Italy and Europe.

关键词： Portable computers parallel programming High performance computing Europe parallel machines Hardware Telecommunications Stakeholders Servers Programming profession

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Evaluating Radiomics Feature Reduction for Thyroid Nodule Segmentation in Thermal Imaging 3rd

Evaluating Radiomics Feature Reduction for Thyroid Nodule Se...

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3rd international conference on Artificial Intelligence over Infrared Images for Medical applications, AIIIMA 2024

作者： Etehadtavakol, Mehdi Etehadtavakol, Mahnaz Moallem, Golnaz Ng, Eddie Y. K. Student Research Committee School of Medicine Shahid Beheshti University of Medical Sciences Tehran Iran School of Medicine Shahid Beheshti University of Medical Sciences Tehran*** Iran Department of Medical Physics School of Medicine Isfahan University of Medical Sciences Isfahan81745-33871 Iran Medical Image and Signal Processing Research Center Isfahan University of Medical Sciences Isfahan81746-73461 Iran Postdoctoral Research Fellow Stanford School of Medicine Palo AltoCA United States School of Mechanical and Aerospace Engineering College of Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore

ISBN: (纸本)9783031765834

Radiomics encompasses a variety of image attributes, including intensity, texture, shape, and spatial relationships among pixels. In this study, we applied four feature selection techniques: Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), t-distributed Stochastic Neighbor Embedding (t-SNE), and Recursive Feature Elimination (RFE), to reduce the dimensionality of radiomic features extracted from thyroid thermal images. Subsequently, we employed three models: U-Net with a VGG16 backbone, U-Net with ResNet50 backbone, and U-Net with DenseNet121 backbone to segment thyroid nodules. The U-Net with a VGG16 backbone combined with the LDA model achieved an average Dice coefficient of 0.4035, significantly outperforming the other models. These results highlight the potential of feature reduction techniques in enhancing thyroid nodule segmentation in thermal imaging. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Feature Reduction Thermal Imaging Thyroid Nodule Segmentation

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A Preliminary Study on Performance Modeling at Scale for Geophysical applications

A Preliminary Study on Performance Modeling at Scale for Geo...

引用

Euromicro conference on parallel, distributed and Network-Based processing

作者： Fabrice Dupros Sylvain Jubertie Hideo Aochi Intel Corporation Paris France Brgm Orléans France

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Modeling the performance of real-world applications at scale is essential for designing next-generation platforms and shaping the development of future algorithms. However, accurately capturing the complexity of application execution graphs and their interaction with large-scale hardware environments remains a significant challenge. In recent years, several frameworks have been developed to tackle this issue by providing tools to simulate and analyze complex workloads on distributed systems. This paper focuses on seismic wave propagation problems as a representative use case to explore the challenges of modeling at scale. We employ the SimGrid simulation toolkit, a versatile framework for simulating distributed systems, to analyze the performance of large-scale applications. Particular emphasis is placed on the role of critical networking characteristics, such as bandwidth and topology, in influencing overall scalability and performance.

关键词： Network topology Scalability Bandwidth Hardware Topology Complexity theory Seismic waves Next generation networking

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A Massive Open Online Course (MOOC) on High-Performance parallel Computing for *** Web-learning Platform

A Massive Open Online Course (MOOC) on High-Performance Para...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Giuliano Laccetti Marco Lapegna Ilaria Merciai dept. of Mathematics and Applications Univ. of Naples Federico II Naples Italy Federica Web Learning Univ. of Naples Fedrico II Naples Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Massive Open Online Courses (MOOCs) represent an accessible and user-friendly tool for disseminating innovative and cutting-edge topics to broad segments of civil society via online learning platforms, enabling users to learn at their own pace and on their own schedule. In this contribution, we describe the design and the implementation of a Massive Open Online Course on parallel Computing and High-Performance Computing, developed for Federica Web Learning: the University Center for innovation, experimentation, and dissemination of multimedia teaching at the University of Naples Federico II.

关键词： Training Computer aided instruction Technological innovation Schedules Electronic learning High performance computing Multimedia computing parallel processing Character recognition Videos

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The P3 Explorer: An Open Database of Performance, Portability, and Productivity

The P3 Explorer: An Open Database of Performance, Portabilit...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Matthew A. Smith Steven A. Wright Zaman Lantra Gihan Mudalige Department of Computer Science University of York York UK Department of Computer Science University of Warwick Coventry UK

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

This paper introduces a web-based tool designed to organise and present visual representations of performance, portability, and productivity (P3) data from previously published scientific studies. The P3 Explorer operates as both an open repository of application performance data and a data dashboard, providing visual heuristic analyses of performance portability and developer productivity, created using Intel’s P3 Analysis library. The goal of the project is to create a community-led database of P3 studies to better inform application developers of alternative approaches to developing new applications that target high performance on diverse hardware, considering the productivity of the developer. In this paper, we evaluate our tool using a recently published study outlining a performance portable domain-specific language for particle-in-cell applications.

关键词： Productivity Visualization Pipelines Data visualization Metadata Libraries Reproducibility of results Hardware Visual databases Domain specific languages

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Application and Evaluation of Real-Time Analysis Algorithms in Dynamic Data processing Systems

Application and Evaluation of Real-Time Analysis Algorithms ...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Si Chen College of Digital Business Liaoning Vocational University of Technology Jinzhou Liaoning China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Dynamic data processing systems are key technologies in modern data-driven applications, widely used in fields such as the Internet of Things, finance and healthcare. This study explores the application and evaluation of real-time analytical algorithms for dynamic data processing. An efficient algorithmic framework combining adaptive windowing, incremental learning and distributed computing is proposed to achieve low-latency and high-accuracy decision-making capabilities. Through experimental validation in a real data streaming environment, the results show that the proposed algorithm significantly outperforms traditional algorithms with about 35% reduction in latency and 20% improvement in prediction accuracy. The research results in this paper show that the real-time analysis algorithm has a promising application in dealing with dynamic heterogeneous data streams. Meanwhile, the article also discusses the limitations and future optimisation directions of the method, which provides a reference for further research in related fields.

关键词： Performance evaluation Incremental learning Accuracy Heuristic algorithms Data processing Prediction algorithms Real-time systems Low latency communication distributed computing Streams

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DaCP: Accelerating Synchronization-Free SpTRSV via GPU-Friendly Data Communication and parallelism Strategies 20th

DaCP: Accelerating Synchronization-Free SpTRSV via GPU-Frie...

引用

20th IFIP WG 10.3 international conference on Network and parallel Computing, NPC 2024

作者： Guo, Mingfeng Deng, Liang Dai, Zhe Li, Ruitian Lin, Gaofeng Liu, Jie Computational Aerodynamics Institute China Aerodynamics Research and Development Center Mianyang China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9789819628292

Sparse triangular solve (SpTRSV) is a vital component in various scientific applications, and numerous GPU-based SpTRSV algorithms have been proposed. Synchronization-free SpTRSV is currently the mainstream algorithm on GPU due to its short preprocessing time and outstanding performance. However, we observed that this algorithm still has two performance bottlenecks. Firstly, the thread-level parallel mode can introduce to thread divergence issues within GPU warps during the writing phase. Secondly, the thread-level and warp-level fusion mode may struggles to fully exploit GPU resources due to suboptimal mapping relationships between rows and threads. To address these issues, this paper proposes DaCPSpTRSV, a new synchronization-free algorithm with GPU-friendly data communication and parallelism strategies. Specifically, we first develop a fast-forward thread-level approach, incorporating an efficient global memory access pattern and a light-weight dependency control mechanism, to optimize data communication and alleviate thread divergence. A fine-grained fusion strategy is then proposed to maximize GPU parallelism by adaptively selecting the suitable thread-level or warp-level modes. Moreover, the commonly-used compressed sparse row (CSR) format is employed in our DaCPSpTRSV, enhancing the versatility of our algorithm. We evaluate our approach using 245 matrices from the SuiteSparse Matrix Collection on two NVIDIA GPUs, demonstrating speedup ratios of up to 4.77×, 4.94×, 1.67×, and 1.62× compared to cuSPARSE, Sync-Free, CapelliniSpTRSV, and YuenyeungSpTRSV, respectively. The project is open-sourced at https://***/gmfff12334/DaCP. © IFIP international Federation for Information processing 2025.

关键词： Graphics processing unit

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A Multi-Level parallel Algorithm for Detection of Single Scatterers in SAR Tomography

A Multi-Level Parallel Algorithm for Detection of Single Sca...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Massimiliano Russo Mehwish Nisar Antonio Pauciullo Pasquale Imperatore Marco Lapegna Diego Romano Dept. of Mathematics and Applications University of Naples Federico II Naples Italy Institute for the Electromagnetic Sensing of the Environment (IREA) National Research Council Naples Italy Inst. for High Performance Computing and Networking National Research Council Naples Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Synthetic Aperture Radar (SAR) tomography is an advanced technique for monitoring deformations of the Earth’s surface. However, the computational complexity of SAR tomography algorithms often restricts their application to large-scale datasets. To address this issue, we introduce a multi-level parallel implementation of a single scatterer detection algorithm specifically designed to exploit the capabilities of modern heterogeneous High-Performance Computing (HPC) systems. By efficiently distributing the computational workload at different levels across multiple processing units, our parallel approach significantly reduces processing time, facilitating the analysis of extensive SAR datasets. We assess the performance of our parallel implementation using real-world SAR data, showcasing its effectiveness in enhancing both the efficiency and scalability of SAR tomography. Our work contributes to advancing remote sensing techniques and offers valuable insights into the application of HPC for large-scale environmental monitoring.

关键词： High performance computing Scalability Graphics processing units Tomography Performance analysis parallel algorithms Synthetic aperture radar Surface treatment Remote sensing Optimization

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parallel Execution Strategies for Cellular Automata on Shared Memory Architectures

Parallel Execution Strategies for Cellular Automata on Share...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Andrea Giordano Alessio De Rango Giuseppe Mendicino Donato D’Ambrosio Davide Macrì Luigi Rizzo Rocco Rongo William Spataro ICAR-CNR Rende Italy Department of Environmental Engineering University of Calabria Italy Department of Mathematics and Computer Science University of Calabria Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Cellular Automata (CA) are extensively utilized for modeling and simulating complex systems due to their simplicity, flexibility, and ability to represent diverse phenomena. However, the computational intensity of CA models, particularly in large-scale applications, demands the development of efficient parallel execution techniques. This is especially critical when targeting shared memory architectures, where resource management and thread coordination play pivotal roles in achieving optimal performance. In this study, we examine a range of parallel execution strategies specifically designed to enhance the efficiency and scalability of CA models. The experimental evaluation highlights good scalability in execution time and resource utilization, making these strategies highly suitable for extensive simulations across various domains. Additionally, the results emphasize that imbalance conditions—often arising during CA computations—can considerably influence the effectiveness of choosing a given strategy.

关键词： Computational modeling Scalability Instruction sets Memory architecture Automata parallel processing Resource management Synchronization Optimization Load modeling

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Boosting Performance of Iterative applications on GPUs: Kernel Batching with CUDA Graphs

Boosting Performance of Iterative Applications on GPUs: Kern...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Jonah Ekelund Stefano Markidis Ivy Peng KTH Royal Institute of Technology Sweden

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Graphics processing Units (GPUs) have become the standard in accelerating scientific applications on heterogeneous systems. However, as GPUs are getting faster, one potential performance bottleneck with GPU-accelerated applications is the overhead from launching several fine-grained kernels. CUDA Graph addresses these performance challenges by enabling a graph-based execution model that captures operations as nodes and dependence as edges in a static graph. Thereby consolidating several kernel launches into one graph launch. We propose a performance optimization strategy for iteratively launched kernels. By grouping kernel launches into iteration batches and then unrolling these batches into a CUDA Graph, iterative applications can benefit from CUDA Graph for performance boosting. We analyze the performance gain and overhead from this approach by designing a skeleton application. The skeleton application also serves as a generalized example of converting an iterative solver to CUDA Graph, and for deriving a performance model. Using the skeleton application, we show that when unrolling iteration batches for a given platform, there is an optimal size of the iteration batch, which is independent of workload, balancing the extra overhead from graph creation with the performance gain of the graph execution. Depending on workload, we show that the optimal iteration batch size gives more than $1.4 \times$ speed-up in the skeleton application. Furthermore, we show that similar speed-up can be gained in Hotspot and Hotspot3D from the Rodinia benchmark suite and a Finite-Difference Time-Domain (FDTD) Maxwell solver.

关键词： Graphics processing units Performance gain Boosting Skeleton Iterative methods Kernel Time-domain analysis Optimization Standards Finite difference methods

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