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检索条件"任意字段=2024 International Symposium on Computer Architecture and High Performance Computing Workshops"

共 5235 条记录，以下是651-660 订阅

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Designing a Card Game for computer Science Instructors to Evaluate Students’ Parallel and Distributed computing Knowledge

Designing a Card Game for Computer Science Instructors to Ev...

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IEEE international Conference on high performance computing workshops (HiPCW)

作者： Srishti Srivastava Mary L. Smith Computer Science University of Southern Indiana Evansville In USA Computer Science Hawaii Pacific University Honolulu HI USA

ISBN: (数字)9798331509118

ISBN: (纸本)9798331509125

The authors present a framework for computer science (CS) instructors to evaluate and strengthen junior and senior undergraduate students’ understanding of parallel and distributed computing (PDC) concepts through a card game. This card game employs active learning strategies to reinforce and build on key PDC topics covered in undergraduate CS courses. Additionally, it fosters student engagement by encouraging collaborative group activities to review PDC concepts. Questions were created based on PDC concept categories recommended in the NSF/IEEE-TCPP Curriculum Initiative on Parallel and Distributed computing.

关键词： computer science Reviews high performance computing Conferences Active learning Distributed databases Collaboration Games

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Architectural Challenge for Cryogenic Superconductor computing

Architectural Challenge for Cryogenic Superconductor Computi...

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Technology, Systems and Applications (VLSI-TSA/VLSI-DAT), international VLSI symposium on

作者： Koji Inoue Kyushu University

ISBN: (数字)9798350360349

ISBN: (纸本)9798350360356

Single-flux-quantum (SFQ) logic operating in a 4.2-kelvin environment with a superconducting state is a promising candidate for achieving extremely high-performance, low-power computing. Josephson junctions (JJs) are used as switching elements in SFQ logic to compose a superconductor ring that can store and transfer a single magnetic flux quantum. It fundamentally operates with the voltage pulse-driven nature, making achieving extremely low-latency and low-energy JJ switching possible. This talk presents our computer architecture research activities for SFQ logic by targeting classical and quantum computers. The key insight we have learned was that deep, efficient collaboration of computer architects and device scientists has significant potential to make breakthroughs in computer system designs. Based on such concrete collaboration, we have introduced state-of-the-art architectural techniques for classical computing targeting SFQ-based processing elements, AI accelerators, etc. Then now, we are trying to extend our technologies for superconductor qubit control/management required to achieve 1 OK+ scalable cryogenic quantum computers. This talk finally discusses the future trends of such next-generation computer architecture designs.

关键词： Superconducting logic circuits Qubit Collaboration Switches Cryogenics computer architecture Voltage

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Food image classification based on the Swin Transformer model

Food image classification based on the Swin Transformer mode...

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international symposium on Soft computing and Intelligent Systems (SCIS)

作者： Han-na Bae Eu-tteum Baek AI&Bigdata Honam University South Korea

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

Food classification is an important task in computer vision applications ranging from dietary assessment to food recognition on social media. In this study, we propose a food image classification technique utilizing the Swin Transformer, a transformer-based architecture. modern transformer architectures like the Swin Transformer capture long-range dependencies within images, making them suitable for complex visual recognition tasks. The primary objective of this study was to evaluate the performance of the Swin Transformer in food classification tasks and compare it with existing CNN-based models, such as MobileNet, and other transformer-based models like Vision Transformer. The experimental results demonstrate that the Swin Transformer effectively handles intricate relationships in food images, achieving high classification accuracy.

关键词： computer vision Visualization Accuracy Image recognition Social networking (online) computer architecture Transformers Distance measurement Image classification

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Diffusion-Based Augmentation for Lung Cancer Risk Prediction from Temporal CT Images 13

Diffusion-Based Augmentation for Lung Cancer Risk Prediction...

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Joint 13th international Conference on Soft computing and Intelligent Systems and 25th international symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Meng, Hsing-Chen Lin, Yi-Hui Huang, Chun-Rong Shih, Nai-Wen Wu, Shawn Lin, Yen-Yu National Yang Ming Chiao Tung University Graduate Degree Program of Ai Hsinchu Taiwan Taichung Veterans General Hospital Dept. of Radiation Oncology Taichung Taiwan National Yang Ming Chiao Tung University Dept. of Computer Science Hsinchu Taiwan Imaging Sy Research Institute Dept. of Diagnostic NY United States

ISBN: (纸本)9798350373332

Cancer risk prediction based on CT images of a patient taken at multiple time points in the past has become an emerging research topic. While deep learning methods yield promising results, they require a large amount of temporally sequenced data for reliable performance. Acquiring such time-series data is expensive and time-consuming. Hence, data aug-mentation based on a small, annotated, and temporally sequenced medical image dataset is essential to cancer risk prediction. This paper introduces a novel diffusion-based method for augmenting temporal medical images with an application to lung cancer risk prediction. Specifically, we develop our approach based on a conditional diffusion model owing to its high flexibility of condition design. We generate target images of a patient for training by overlaying tumors recently detected and masked by eroded segmentation maps onto CT images taken earlier to simulate tumor growth. Specifically, our conditional diffusion model is trained by using CT images taken in the first or second year, along with tumor segmentation maps in the third year, as well as the generated target images. Three distinct generation strategies are designed by using different combinations of CT images and eroded segmentation maps as conditions to synthesize lung CT images, hence diversifying and enriching the training set. Experimental results demonstrate that learning using 25 % of training data together with our augmented data achieves comparable performance to that using 100% of training data. © 2024 IEEE.

关键词： Lung cancer

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performance Portability of Generated Cardiac Simulation Kernels Through Automatic Dimensioning and Load Balancing on Heterogeneous Nodes

Performance Portability of Generated Cardiac Simulation Kern...

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IEEE international symposium on Parallel and Distributed Processing workshops and Phd Forum (IPDPSW)

作者： Vincent Alba Olivier Aumage Denis Barthou Raphaël Colin Marie-Christine Counilh Stéphane Genaud Amina Guermouche Vincent Loechner Arun Thangamani LaBRI University of Bordeaux CNRS Bordeaux INP Inria Talence France Bordeaux INP Talence France ICube Lab. University of Strasbourg CNRS Inria Strasbourg France

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Electrophysiology simulation applications, such as the community-developed openCARP framework for in-silico experiments, involve applying a broad range of ionic model kernels with different computational weights and arithmetic intensity characteristics. Efficiently processing such kernels on modern heterogeneous architectures necessitates to accurately dimension the set of computing resources to use and to actively balance the load on the available computing units, to account for discrepancies in kernel duration and distinct computing unit speeds. We thus propose the following contributions: 1) the adaptation of an existing load-balancing algorithm to transparently manage the mapping of these ionic model kernels onto the heterogeneous units of a computing node; 2) a resource dimensioning heuristic that constraints the number of devices that should be used to maximize efficiency, according to the selected ionic models' computational weight; 3) the integration of these mechanisms in openCARP, building on prior work that took advantage of LLVM's MLIR framework to generate multiple device-specialized variants of kernels from ionic models expressed in openCARP's high-level DSL; 4) a thorough experimentation of the mechanisms on a comprehensive series of 30 ionic models provided by openCARP. The experiments show that when using the combination of the load-balancing algorithm and the resource dimensioning heuristic to compute each ionic model, the geometric mean of speedup is 9.97× with respect to the original multi-threaded code on an architecture with two A100 GPUs and 2× 32-cores AMD Zen3 CPUs.

关键词： Adaptation models Energy consumption Computational modeling Heuristic algorithms computer architecture Load management Kernel

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LB-UNet: Multi-Scale Fusion Lightweight Network with Boundary Enhancement for Water Segmentation 22

LB-UNet: Multi-Scale Fusion Lightweight Network with Boundar...

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22nd IEEE international symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Li, Ning Wang, Libo Yang, Gaochao Yuan, Baohua Li, Bo Changzhou University Academy of Computing and Artificial Intelligence Changzhou China

ISBN: (纸本)9798331509712

Water body extraction is a key technology in spatial geographic analysis. It plays an important role in disaster warning and surface navigation. At present, the semantic segmentation model of water body images generally has problems such as large scale changes, inaccurate edge description and noise interference. To address these limitations, we propose a new segmentation model, named Lightweight Boundary Enhancement U-Net (LB-UNet). Firstly, a Boundary Enhancement (BE) module enhanced by Butterworth high-pass filtering is constructed to enhance the edge contour features of the image and retain more boundary information. Secondly, a Multi-scale Fusion Connection Bridge (MFCB) module is designed for skip connection. Through multi-scale fusion, the feature information between the deep and shallow layers is fused with the mask, enriching the feature information of the shallow layer and reducing the loss of image feature information during encoder downsampling. In order to reduce the number of model parameters, residual connections and depth-separable convolution modules are introduced to construct a Lightweight Encoder-Decoder structure (LED). The model is trained on the inland waterway dataset and the remote sensing water body dataset. It achieves the best segmentation performance. On the inland waterway dataset, the IoU on the test set reached 95.69%, an increase of 13.33% compared with U-Net. On the remote sensing water body dataset, the IoU on the test set reached 90.78%, an increase of 10.39% compared with U-Net. © 2024 IEEE.

关键词： Semantic Segmentation

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Optimizing Batched Small Matrix Multiplication on Multi-core DSP architecture

Optimizing Batched Small Matrix Multiplication on Multi-core...

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international symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Xiaohan Zuo Chunhua Xiao Qinglin Wang Chen Shi College of Computer Science Chongqing University China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

General Matrix Multiplication (GEMM) is a critical computational operation in scientific computing and machine learning domains. While traditional GEMM performs well on large matrices, it is inefficient in terms of data transfer and computation for small matrices. Many high-performance computing (HPC) tasks can be decomposed into large batches of small matrix multiplication operations. Multi-core Digital Signal Processors (DSPs) are commonly used to accelerate high-performance computing. We present a design for batched fusion small matrix multiplication (BFMM) tailored for multi-core DSP architecture. To address the inefficiencies and redundancy in storage and computational operations associated with batch small matrix multiplications, we designed several strategies. We design a matrix fusion concatenation strategy, an access coordination mechanism, and a mechanism for fragment aggregation. BFMM supports an efficient K-dimension multi-core parallelization strategy. The parameter constraint model makes BFMM highly portable. BFMM also includes a performance evaluation model that facilitates assessment and verification. Experimental results demonstrate that, compared to traditional GEMM (TGEMM) on multi-core DSP and traditional GEMM with concatenated data access (TGEMM Op), BFMM exhibits superior performance. For large batches of small matrices, our design achieves 1.21x to 18x higher performance than TGEMM Op on single-core DSP, while on multi-core DSP, it outperforms TGEMM Op by 1.14x to 18.1x.

关键词： performance evaluation Scientific computing high performance computing Digital signal processors Redundancy Batch production systems Signal processing algorithms computer architecture Machine learning Matrix decomposition

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To Derive or Not to Derive: I/O Libraries Take Charge of Derived Quantities Computation

To Derive or Not to Derive: I/O Libraries Take Charge of Der...

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international symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Ana Gainaru Norbert Podhorszki Liz Dulac Qian Gong Scott Klasky Greg Eisenhauer Antonios Kougkas Xian-He Sun Jay Lofstead Oak Ridge National Laboratory Georgia Institute of Technology Illinois Institute of Technology Sandia National Laboratory

ISBN: (数字)9798350356168

ISBN: (纸本)9798350356175

The ever-increasing volume of data produced by HPC simulations necessitates scalable methods for data exploration and knowledge extraction. Scientific data analysis often involves complex queries across distributed datasets, requiring manipulation of multiple primary variables and generating derived data that needs to be handled efficiently, creating challenges for applications that need to parse many large datasets. Relying on individual applications to handle all intermediate data generally leads to redundant computations across studies and unnecessary data transfers. In this paper, we investigate the performance of different approaches where applications define derived variables as quantities of interest (QoIs) and offload the computation and transfer of these QoIs to the I/O library. This significantly reduces redundancy and optimizes data movement across the distributed storage and processing infrastructure by allowing control over when and where derived variables are computed. We present a detailed analysis of the performance-storage trade-offs associated with different solutions and showcase results for our study on two large-scale datasets created from climate and combustion simulations.

关键词： Analytical models Solid modeling Computational modeling high performance computing Redundancy Distributed databases Process control Libraries Data models Meteorology

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Efficient Tree-based Parallel Algorithms for N-Body Simulations Using C++ Standard Parallelism

Efficient Tree-based Parallel Algorithms for N-Body Simulati...

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high performance computing, Networking, Storage and Analysis, SC-W: workshops of the international Conference for

作者： Thomas Lane Cassell Tom Deakin Aksel Alpay Vincent Heuveline Gonzalo Brito Gadeschi School of Computer Science University of Bristol Bristol UK Engineering Mathematics and Computing Lab Interdisciplinary Center for Scientific Computing Heidelberg University Heidelberg Germany GPU Architecture NVIDIA Munich Germany

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

The Barnes-Hut approximation for N-body simulations reduces the time complexity of the naive all-pairs approach from O(N 2 ) to O(N log N) by hierarchically aggregating nearby particles into single entities using a tree data structure. This inherently irregular algorithm poses substantial challenges for performance portable implementations on multi-core CPUs and GPUs. We introduce two portable fully-parallel Barnes-Hut implementation strategies that trade-off different levels of GPU support for performance: an unbalanced concurrent octree, and a balanced bounding volume hierarchy sorted by a Hilbert spacefilling curve. We implement these algorithms in portable ISO C++ using parallel algorithms and concurrency primitives like atomics. The results demonstrate competitive performance on a range of CPUs and GPUs. Additionally, they highlight the effectiveness of the par execution policy for highly concurrent irregular algorithms, outperforming par_unseq on CPUs and GPUs with Independent Thread Scheduling.

关键词： performance evaluation ISO Standards Software algorithms Octrees Graphics processing units C++ languages Scheduling Hardware Parallel algorithms Standards

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Dependability Modeling in an Industrial Environment

Dependability Modeling in an Industrial Environment

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IEEE international Conference on Software Reliability Engineering workshops (ISSRE Wksp)

作者： Alberto Avritzer James J. Cusick Andrea Janes Matteo Camilli Barbara Russo Catia Trubiani Andre van Hoom EsulabSolutions Inc. Princeton USA Ritsumeikan University Shiga Japan Free University of Bozen-Bolzano Italy Politecnico di Milano Italy Gran Sasso Science Institute Italy Universität Hamburg Germany

ISBN: (数字)9798350367041

ISBN: (纸本)9798350367058

In this industrial report, we present the application of an architectural risk assessment of a major digital transformation initiative to validate and improve reliability and performance characteristics. The goal of this paper is to describe our experience adapting the well-known architecture Tradeoff Analysis Method (ATAM) methodology to perform an architecture risk assessment of the individual components of this initiative. A high-level back-of-the-envelope model was developed to support the iterative ATAM architecture risk assessment of reliability, performance, and scalability business drivers on the industrial project under analysis each highlighted the dependability of the solution. This performance model was validated using measurements of the existing architecture prototype and demonstrated estimated reliability. This paper contributes to presenting the ATAM adaptation details, the technical approach adopted, the challenges encountered, the summary of results, and the lessons learned from the application of the ATAM methodology to understanding software reliability and performance early in the system life-cycle.

关键词： Adaptation models Digital transformation Scalability Prototypes computer architecture Software reliability Risk management Iterative methods Standards Pragmatics

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