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

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J-Net: A Low-Resolution Lightweight Neural Network for Semantic Segmentation in the Medical Field for Embedded Deployment 19th

J-Net: A Low-Resolution Lightweight Neural Network for Seman...

引用

19th international symposium on Visual computing

作者： Ostrowski, Erik Shafique, Muhammad Tech Univ Wien TU Wien Inst Comp Engn Vienna Austria New York Univ Abu Dhabi Div Engn eBrain Lab Abu Dhabi U Arab Emirates

ISBN: (纸本)9783031773914;9783031773921

When deploying neural networks in real-life situations, the size and computational effort are often the limiting factors. This is especially true in environments where big, expensive hardware is not affordable, like in embedded medical devices, where budgets are often tight. State-of-the-art proposed multiple different lightweight solutions for such use cases, mostly by changing the base model architecture, not taking the input and output resolution into consideration. In this paper, we propose the J-Net architecture that takes advantage of the fact that in hardware-limited environments, we often refrain from using the highest available input resolutions to guarantee a higher throughput. Although using lower-resolution input leads to a significant reduction in computing and memory requirements, it may also incur reduced prediction quality. Our J-Net architecture addresses this problem by exploiting the fact that we can still utilize high-resolution ground-truths in training. The proposed model inputs lower-resolution images and high-resolution ground truths, which can improve the prediction quality by 5.5% while adding less than 200 parameters to the model. We conduct an extensive analysis to illustrate that J-Net enhances existing state-of-the-art frameworks for lightweight semantic segmentation of cancer in MRI images. We also tested the deployment speed of state-of-the-art lightweight networks and J-Net on Nvidia's Jetson Nano to emulate deployment in resource-constrained embedded scenarios. The framework is open-source and accessible online at https://***/ErikOstrowski/J-Net.

关键词： Semantic Segmentation Lightweight Embedded Deployment CAD computer Vision

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FPBOXer: Efficient Input-Generation for Targeting Floating-Point Exceptions in GPU Programs 24

FPBOXer: Efficient Input-Generation for Targeting Floating-P...

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33rd international symposium on high-performance Parallel and Distributed computing (HPDC)

作者： Tran, Anh Laguna, Ignacio Gopalakrishnan, Ganesh Kahlert Sch Comp Salt Lake City UT 84112 USA Ctr Appl Sci Comp Livermore CA USA

ISBN: (纸本)9798400704130

Numerical programs that generate floating-point exceptions, such as NaNs, are inherently unreliable, as these programs can produce meaningless outputs or affect control flow. When these programs run on GPUs, one cannot rely on hardware traps to handle the exceptions, as most GPUs do not support them. Unfortunately, we must also employ black-box testing for many such GPU programs, as they are supplied as binary code only. While previous work has shown that black-box testing for triggering floating-point exceptions can be approached using Bayesian Optimization, their approach cannot handle programs with more than three inputs. We contribute a new tool, FPBOXer, which pushes up the capabilities of BO to handle over 20 inputs-this makes our contribution capable of handling realistic HPC program functions. In addition to delivering an overall 90x speedup over the previous methods, FPBOXer does not suffer from "self-inflicted" exceptions that are caused by the BO algorithm itself-something that previous tools did. This is achieved through parallel deployments of asynchronous BO searches, which has the beneficial side-effect of improving the GPU-utilization. By using FPBOXer, developers can, for the first time, find exception-causing inputs in realistic HPC programs, as we demonstrate when we apply FPBOXer to NAS, Lampps, CFD, ExaMiniMD, HPCCG, MiniFE, and BDCSVD.

关键词： Floating-point Arithmetic Exceptions Bayesian Optimization high-performance computing

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Metrics for Packing Efficiency and Fairness of HPC Cluster Batch Job Scheduling 34

Metrics for Packing Efficiency and Fairness of HPC Cluster B...

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34th IEEE international symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Goponenko, Alexander, V Lamar, Kenneth Peterson, Christina Allan, Benjamin A. Brandt, Jim M. Dechev, Damian Univ Cent Florida Dept Comp Sci 211 Harris CtrBldg 1164000 Cent Florida Blvd Orlando FL 32816 USA Sandia Natl Labs POB 5800MS 0823 Albuquerque NM 87185 USA

ISBN: (数字)9781665451550

ISBN: (纸本)9781665451550

Development of job scheduling algorithms, which directly influence high-performance computing (HPC) clusters performance, is hindered because popular scheduling quality metrics, such as Bounded Slowdown, poorly correlate with global scheduling objectives that include job packing efficiency and fairness. This report proposes Area Weighted Response Time, a metric that offers an unbiased representation of job packing efficiency, and presents a class of new metrics, Priority Weighted Specific Response Time, that assess both packing efficiency and fairness of schedules. The provided examples of simulation of scheduling of real workload traces and analysis of the resulting schedules with the help of these metrics and conventional metrics, demonstrate that although Bounded Slowdown can be readily improved by modifying the standard First Come First Served backfilling algorithm and by using existing techniques of estimating job runtime, these improvements are accompanied by significant degradation of job packing efficiency and fairness. In contrast, improving job packing efficiency and fairness over the standard backfilling algorithm, which is designed to target those objectives, is difficult. It requires further algorithm development and more accurate runtime estimation techniques that reduce frequency of underpredictions.

关键词： high performance computing parallel job scheduling performance metrics schedule quality runtime estimates packing efficiency fairness weighted flow time weighted response time

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A Novel Approach for Virtual Locomotion Gesture Classification: Self-Teaching Vision Transformer for a Carpet-Type Tactile Sensor 31

A Novel Approach for Virtual Locomotion Gesture Classificati...

引用

IEEE Conference on Virtual Reality and 3D User Interfaces (VR)

作者： Lee, Sung-Ha Joo, Ho-Taek Chung, Insik Park, Donghyeok Choi, Yunho Kim, Kyung-Joong Gwangju Inst Sci & Technol Gwangju South Korea

ISBN: (纸本)9798350374025;9798350374032

Locomotion gesture classification in virtual reality (VR) is the process of analyzing and identifying specific user movements in the real world to navigate virtual environments. However, existing methods often necessitate the use of wearable sensors, which present limitations. To address this, we utilize a high-resolution carpet-type tactile sensor as a foot action recognition interface, which was previously unexplored in the context of locomotion gesture classification. This interface can capture the user's foot pressure data in detail to distinguish similar actions. In this paper, to efficiently process captured user's foot tactile data and classify nuanced actions, we utilize a Vision Transformer (ViT) architecture and propose a novel Self-Teaching Vision Transformer (STViT) model integrating elements of the Shifted window Vision Transformer (SwinViT) and Data-efficient image Transformer (DeiT). However, unlike DeiT, our model uses itself from N-steps prior as the teacher model, which is continuously updated. Therefore, improving the ability to classify actions by referencing its own knowledge from previous training stages progressively refines its understanding of similar action gestures. Also, we used the base architecture of SwinViT to utilize patch merging, which improves the ability to differentiate between variations in similar actions by capturing information at different scales. We evaluated seven vision-based methods, demonstrating promising results. Not only did our model outperform ResNet by 19.6%, but it also outperformed each Deit and SwinViT by 3.3% and 2.9%, achieving 92.7% accuracy. To validate our model's real-world applicability, we conducted user preference tests and in-game performance evaluations with 18 participants. As a result, the participants preferred our model to SwinViT and DeiT, backing up the computational results. The video demonstrating the VR testing for STViT can be found in https://***/NJslvanRn18

关键词： Human-centered computing-Human-computer interaction (HCI)-Interaction techniques-Gestural input Human-centered computing-Human-computer interaction (HCI)-Interaction paradigms-Virtual reality

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Efficiently Distributed Federated Learning

Efficiently Distributed Federated Learning

引用

29th international Conference on Parallel and Distributed computing (Euro-Par)

作者： Mittone, Gianluca Birke, Robert Aldinucci, Marco Univ Turin Comp Sci Dept Turin Italy

ISBN: (纸本)9783031488023;9783031488030

Federated Learning (FL) is experiencing a substantial research interest, with many frameworks being developed to allow practitioners to build federations easily and quickly. Most of these efforts do not consider two main aspects that are key to Machine Learning (ML) software: customizability and performance. This research addresses these issues by implementing an open-source FL framework named FastFederatedLearning (FFL). FFL is implemented in C/C++, focusing on code performance, and allows the user to specify any communication graph between clients and servers involved in the federation, ensuring customizability. FFL is tested against Intel OpenFL, achieving consistent speedups over different computational platforms (x86-64, ARM-v8, RISC-V), ranging from 2.5x and 3.69x. We aim to wrap FFL with a Python interface to ease its use and implement a middleware for different communication backends to be used. We aim to build dynamic federations in which relations between clients and servers are not static, giving life to an environment where federations can be seen as long-time evolving structures and exploited as services.

关键词： Federated Learning Distributed computing HPC

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InstAttention: In-Storage Attention Offloading for Cost-Effective Long-Context LLM Inference 31

InstAttention: In-Storage Attention Offloading for Cost-Effe...

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31st IEEE international symposium on high performance computer architecture, HPCA 2025

作者： Pan, Xiurui Li, Endian Li, Qiao Liang, Shengwen Shan, Yizhou Zhou, Ke Luo, Yingwei Wang, Xiaolin Zhang, Jie Peking University China University of Electronic Science and Technology of China China Institute of Computing Technology Chinese Academy of Sciences China Huawei Cloud China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology China

ISBN: (纸本)9798331506476

The widespread of Large Language Models (LLMs) marks a significant milestone in generative AI. Nevertheless, the increasing context length and batch size in offline LLM inference escalate the memory requirement of the key-value (KV) cache, which imposes a huge burden on the GPU VRAM, especially for resource-constrained scenarios (e.g., edge computing). Several cost-effective solutions leverage host memory or SSDs to reduce storage costs for offline inference scenarios and improve the throughput. Nevertheless, they suffer from significant performance penalties imposed by intensive KV cache accesses due to limited PCIe bandwidth. To address these issues, we propose InstAttention, a novel LLM inference system that offloads the most performance-critical computation (i.e., attention in decoding phase) and data (i.e., KV cache) parts to Computational Storage Drives (CSDs), which minimize the enormous KV transfer overheads. InstAttention designs a dedicated flashaware in-storage attention engine with KV cache management mechanisms to exploit the high internal bandwidths of CSDs instead of being limited by the PCIe bandwidth. The optimized P2P transmission between GPU and CSDs further reduces data migration overheads. Experimental results demonstrate that for a 13B model using an NVIDIA A6000 GPU, InstAttention improves throughput for long-sequence inference by up to 11.1 ×, compared to existing SSD-based solutions such as FlexGen. © 2025 IEEE.

关键词： computer graphics equipment

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Optimizing Microservices performance and Resource Utilization through Containerized Grouping: An Experimental Study

Optimizing Microservices Performance and Resource Utilizatio...

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

作者： Fernando H. L. Buzato Alfredo Goldman Department of Computer Science University of São Paulo (USP) São Paulo Brazil

This study investigates microservices grouping within containers, exploring its impact on performance, resource utilization, and availability. Experiments using the Sock-Shop microservice benchmark tool across varying workloads revealed benefits in terms of optimizing performance, computational resources and improving availability in scenarios grouping loosely-coupled microservices. However, this approach can incur operational complexity and costs. Therefore, grouping microservices within containers offers potential advantages, but careful consideration is needed to balance benefits and challenges effectively.

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Integrating interactive performance analysis in Jupyter Notebooks for parallel programming education

Integrating interactive performance analysis in Jupyter Note...

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1st international Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Oden, Lena Noelp, Klaus Brauner, Philipp Univ Hagen Comp Engn Hagen Germany Rhein Westfal TH Aachen Human Comp Interact Ctr Aachen Germany

ISBN: (纸本)9798350364613;9798350364606

Understanding the performance behavior of parallel applications is important in many ways, but doing so is not easy. Most open source analysis tools are written for the command line. We are building on these proven tools to provide an interactive performance analysis experience within Jupyter Notebooks when developing parallel code with MPI, OpenMP, or both. Our solution makes it possible to measure the execution time, perform profiling and tracing, and visualize the results within the notebooks. For ease of use, it provides both a graphical JupyterLab extension and a C++ API. The JupyterLab extension shows a dialog where the user can select the type of analysis and its parameters. Internally, this tool uses Score -P, Scalasca, and Cube to generate profiling and tracing data. This tight integration gives students easy access to profiling tools and helps them better understand concepts such as benchmarking, scalability and performance bottlenecks. In addition to the technical development, the article presents hands-on exercises from our well-established parallel programming course. We conclude with a qualitative and quantitative evaluation with 19 students, which shows a positive effect of the tools on the students' perceived competence.

关键词： Jupyter parallel programming performance analysis interactive programming high perfonnance computing

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An Analysis of Multitask Deep Learning Models for Histopathology 26

An Analysis of Multitask Deep Learning Models for Histopatho...

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26th international symposium on Symbolic and Numeric Algorithms for Scientific computing, SYNASC 2024

作者： Manole, Alexandru Babeş-Bolyai University Faculty of Mathematics and Computer Science Cluj-Napoca Romania

ISBN: (纸本)9798331532833

Artificial Intelligence has the potential to streamline and facilitate numerous processes in the medical field, increasing the quality of life for millions and potentially saving lives. One area which requires a lot of effort when it comes to diagnosing severe diseases is Histopathology. Recently, a modern learning strategy, Multitask learning, was applied in Digital Histopathology in order to obtain relevant medical information from histological images. This paradigm is able to increase performance by learning multiple objectives simultaneously resulting in more general features. The resulting methods reduce overfitting and computational complexity while increasing data efficiency making it a suitable choice for the high-dimensionality, low sample size sets from the medical field. The aim of this work is to present novel multitask approaches with applications in Histopathology, analyse them, showcase their advantages and drawbacks and identify possible future research directions. © 2024 IEEE.

关键词： Contrastive Learning

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Compiler-Driven SWAR Parallelism for high-performance Bitboard Algorithms

Compiler-Driven SWAR Parallelism for High-Performance Bitboa...

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

作者： Florian Fey Sergei Gorlatch University of Muenster Muenster Germany

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Parallel high-performance computing relies on cache-efficient, branch-free algorithms that are often expressed as imperative computations over multidimensional arrays. Numerous problem domains, spanning from image processing to graph analytics, and from state space exploration in combinatorial optimization to computer Chess, require carefully crafted algorithms that capitalize on patterns inherent in the underlying problem structure. A renowned technique, SIMD-Within-A-Register (SWAR), harnesses integer arithmetic to attain significant hardware parallelism. However, this approach typically demands labor-intensive efforts from domain experts with specialized knowledge of the underlying hardware architecture. We therefore present a compiler-driven approach that automates the transformation of conventional array-based C-code into highly tuned integer arithmetic, exploiting SWAR parallelism without the requirement of tedious manual optimization efforts. Our approach achieves substantial performance improvements, exhibiting an average speedup of 30 × compared to conventional array-based implementations.

关键词： Codes Manuals computer architecture Transforms Parallel processing performance gain Hardware

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