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Assessing the GPU Offload Threshold of GEMM and GEMV Kernels on Modern Heterogeneous HPC systems 24

Assessing the GPU Offload Threshold of GEMM and GEMV Kernels...

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proceedings of the SC '24 Workshops of the International Conference on High Performance computing, Network, Storage, and analysis

作者： Finn Wilkinson Alex Cockrean Wei-Chen Lin Simon McIntosh-Smith Tom Deakin High-Performance Computing Group University of Bristol Bristol United Kingdom

ISBN: (纸本)9798350355543

With an ever-growing compute advantage over CPUs, GPUs are often used in workloads with ample BLAS computation to improve performance. However, several factors including data-to-compute ratio, amount of data re-use, and data structure shape can all impact performance. Hence, using a GPU is not a guarantee of better BLAS performance. In this work, we introduce the GPU BLAS Offload Benchmark (GPU-BLOB), a novel and portable benchmark that measures CPU and GPU compute performance of different BLAS kernels and problem configurations. From the GPU offload threshold (a BLAS kernel's minimum dimensions for a certain configuration where using a GPU is guaranteed to yield improved performance), we evaluate the per-node performance of three, in-production, HPC systems. We show that the offload threshold for GEMM is highly dependant on problem shape and number of consecutive BLAS calls, and that, contrary to conventional wisdom, GEMV can benefit from GPU acceleration, especially on SoC-based systems.

关键词： AMD MI250X

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Implementing Asynchronous Jacobi Iteration on GPUs 13

Implementing Asynchronous Jacobi Iteration on GPUs

引用

13th IEEE/acm Workshop on Latest Advances in Scalable Algorithms for Large-Scale Heterogeneous systems, ScalAH 2022

作者： Tsai, Yu-Hsiang Mike Nayak, Pratik Chow, Edmond Anzt, Hartwig Karlsruhe Institute of Technology Steinbuch Centre for Computing Germany Georgia Institute of Technology School of Computational Science and Engineering United States University of Tennessee Innovative Computing Laboratory United States

ISBN: (纸本)9781665475709

Computation on architectures that feature fine-grained parallelism requires algorithms that overcome load imbalance, inefficient memory access, serialization, and excessive synchronization. In this paper, we explore an algorithm for iteratively solving systems of linear equations that allows for asynchronous updates by different execution units and completely removes the need for synchronization. Methods of this type have been identified as potentially competitive for computations on Exascale machines, but practical implementations for GPU platforms have scarcely been studied. We present an asynchronous Jacobi iteration optimized for high-end GPUs, demonstrate the superiority of the algorithm over a highly tuned synchronous Jacobi iteration, and deploy the algorithm as production-ready implementation in the Ginkgo open source library. The ideas presented here on the algorithm design, implementation and performance can help guide the design of other asynchronous iterative methods on GPUs. © 2022 IEEE.

关键词： Iterative methods

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CloudRCA: A Root Cause analysis Framework for Cloud computing Platforms 21

CloudRCA: A Root Cause Analysis Framework for Cloud Computin...

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30th acm International Conference on Information and Knowledge Management (CIKM)

作者： Zhang, Yingying Guan, Zhengxiong Qian, Huajie Xu, Leili Liu, Hengbo Wen, Qingsong Sun, Liang Jiang, Junwei Fan, Lunting Ke, Min Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9781450384469

As business of Alibaba expands across the world among various industries, higher standards are imposed on the service quality and reliability of big data cloud computing platforms which constitute the infrastructure of Alibaba Cloud. However, root cause analysis in these platforms is non-trivial due to the complicated system architecture. In this paper, we propose a root cause analysis framework called CloudRCA which makes use of heterogeneous multi-source data including Key Performance Indicators (KPIs), logs, as well as topology, and extracts important features via state-of-the-art anomaly detection and log analysis techniques. The engineered features are then utilized in a Knowledge-informed Hierarchical Bayesian Network (KHBN) model to infer root causes with high accuracy and efficiency. Ablation study and comprehensive experimental comparisons demonstrate that, compared to existing frameworks, CloudRCA 1) consistently outperforms existing approaches in f1-score across different cloud systems;2) can handle novel types of root causes thanks to the hierarchical structure of KHBN;3) performs more robustly with respect to algorithmic configurations;and 4) scales more favorably in the data and feature sizes. Experiments also show that a cross-platform transfer learning mechanism can be adopted to further improve the accuracy by more than 10%. CloudRCA has been integrated into the diagnosis system of Alibaba Cloud and employed in three typical cloud computing platforms including MaxCompute, Realtime Compute and Hologres. It saves Site Reliability Engineers (SREs) more than 20% in the time spent on resolving failures in the past twelve months and improves service reliability significantly.

关键词： Root Cause analysis AIOps Cloud computing Platform Alibaba Cloud

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Energy-Efficient Resource Management for Real-Time Applications in FaaS Edge computing Platforms 23

Energy-Efficient Resource Management for Real-Time Applicati...

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proceedings of the IEEE/acm 16th International Conference on Utility and Cloud computing

作者： Shahrokh Vahabi Francesca Righetti Carlo Vallati Nicola Tonellotto Department of Information Engineering University of Pisa Pisa Pisa IT Department of Information Engineering University of Pisa Pisa Pisa Italy

ISBN: (纸本)9798400702341

Edge computing and Function-as-a-Service are two emerging paradigms that enable a timed analysis of data directly in the proximity of cyber-physical systems and users. Function-as-a-service platforms deployed at the edge require mechanisms for resource management and allocation to schedule function execution and to scale the available resources in order to ensure the proper quality of service to applications. Large-scale deployments will also require mechanisms to control the energy consumption of the overall system, to ensure long-term sustainability. In this paper, we propose a technique to schedule function invocations on Edge resources by powering down idle edge nodes during period of low demands. In doing so, our technique aims at reducing the overall energy consumption without incurring in service level agreements violations. Experimental evaluations demonstrate that the proposed approach reduces service level agreement violations by at least 78.1% and energy consumption by at least 62.5% on average using synthetic and real-world datasets w.r.t. different baselines.

关键词： edge computing

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Power systems Frequency Response using Frequency Dependent Network Equivalent based on a Rational Function in the z-domain 25

Power Systems Frequency Response using Frequency Dependent N...

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25th Autumn Meeting on Power, Electronics and computing, ROPEC 2023

作者： Verduzco-Duran, Juan Ramos-Paz, Antonio Medina-Rios, Aurelio Cisneros-Magana, Rafael Universidad Michoacana de San Nicolás de Hidalgo División de Estudios de Posgrado de la Facultad de Ingeniería Eléctrica Ciudad Universitaria Michoacán Morelia58030 Mexico

ISBN: (纸本)9798350336887

This article deals with the problem of harmonic resonance in power systems, which causes harmonic amplification and can worsen existing harmonic problems in the system. Therefore, performing a frequency response analysis is essential to identify series and parallel resonances in the system within a frequency range of interest. To address this issue, it is proposed to use a Frequency-Dependent Network Equivalent (FDNE) based on a rational function in the z-domain. This approach is based on acquiring measurement data and implementing parameter identification techniques using the recursive least squares method to evaluate the frequency response. To validate the proposed method, its implementation is carried out in different case studies. © 2023 IEEE.

关键词： Frequency response

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TCPS: A Task and Cache-Aware Partitioned Scheduler for Hard Real-Time Multi-core systems 2022

TCPS: A Task and Cache-Aware Partitioned Scheduler for Hard ...

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23rd International Conference on Languages, Compilers, and Tools for Embedded System (LCTES)

作者： Shen, Yixian Xiao, Jun Pimentel, Andy D. Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9781450392662

Shared caches in multi-core processors seriously complicate the timing verification of real-time software tasks due to the task interference occurring in the shared caches. Explicitly calculating the amount of cache interference among tasks and cache partitioning are two major approaches to enhance the schedulability performance in the context of multi-core processors with shared caches. The former approach suffers from pessimistic cache interference estimations that subsequently result in suboptimal schedulability performance, whereas the latter approach may increase the execution time of tasks due to a lower cache usage, also degrading the schedulability performance. In this paper, we propose a heuristic partitioned scheduler, called TCPS, for real-time non-preemptive multi-core systems with partitioned caches. To achieve a high degree of schedulability, TCPS combines the benefits of partitioned scheduling, relieving the computing resources from contention, and cache partitioning, mitigating cache interference, in conjunction with exploiting task characteristics. A series of comprehensive experiments were performed to evaluate the schedulability performance of TCPS and compare it against a variety of global and partitioned scheduling approaches. Our results show that TCPS outperforms all of these scheduling techniques in terms of schedulability, and yields a more effective cache usage and more stable load balancing.

关键词： Cache partitioning Partitioned scheduling Schedulability analysis Real-time systems

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Designing a Wearable Wireless System for Real-time Bioimpedance Spectroscopy of Body Fluid

Designing a Wearable Wireless System for Real-time Bioimpeda...

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2023 IEEE Biomedical Circuits and systems Conference, BioCAS 2023

作者： Bandur, Antonio Sadatamin, Delaram Piper, Bryan Culjak, Ivana Dzapo, Hrvoje Yadollahi, Azadeh University Health Network KITE-Toronto Rehabilitation Institute Toronto Canada University of Toronto Institute of Biomedical Engineering Toronto Canada University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (纸本)9798350300260

This paper introduces a wearable system for monitoring body fluid dynamics using a wireless technology. The system utilizes Bluetooth Low Energy (BLE) and the AFE4300 integrated circuit for bioimpedance spectroscopy. It combines the STM32 microcontroller and BLE connectivity with a custom-designed hardware platform, allowing real-time acquisition, processing, and analysis of bioimpedance data. The system offers a user-friendly solution for non-invasive monitoring of body fluid, enabling personalized healthcare approaches. The study involved six participants and compared the system's performance to the SFB7 ImpediMed system, as a gold-standard for bioimpedance measurement using gel-based and textile electrodes. The system demonstrated comparable performance to the SFB7 ImpediMed system for assessing leg and total body water. The findings underscore the potential of the system to enable real-time bioimpedance spectroscopy in a wearable and wireless environment, facilitating advancements in personalized healthcare. This holds particular significance for individuals with heart failure, as frequent monitoring of body fluid levels is critical in managing their condition. © 2023 IEEE.

关键词： Bioimpedance Spectroscopy Bluetooth Low Energy Fluid measurement Smart Textile Wearable System Wireless Technology

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Predicting Students' Performance Using Machine Learning Algorithms 6

Predicting Students' Performance Using Machine Learning Algo...

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6th International Conference on Algorithms, computing and systems, ICACS 2022

作者： Dervenis, Charalampos Kyriatzis, Vasileios Stoufis, Spyros Fitsilis, Panos Department of Business Administration School of Economics and Business Administration University of Thessaly Larisa Greece Department of Digital Systems School of Technology University of Thessaly Larisa Greece Computer Science Department School of Science & Technology Hellenic Open University Patras Greece

ISBN: (纸本)9781450397407

Learning analytics (LA), defined as "the measurement, collection, analysis and reporting of data about learners and their contexts for the purposes of understanding and optimizing learning and the environments in which it occurs", is a research topic that has gained significant importance and visibility among researchers during the past few decades. It is a research domain where the use of modern machine-learning (ML) algorithms and big data management provide timely and actionable information that can transform the overall learning experience for both students and educational institutions. In this paper we use ML algorithms in order to predict the performance of students, taking into account both past semester grades and socioeconomic factors. We run two models;a 2-class one predicting a "pass"or "fail"result and then we expanded this to a 5-class model, where we predict in which grading group the student will fall in the next semester. The results acquired indicate that it is possible to accurately predict the student's performance in both cases, with the 2-class model performing better than the 5-class one, which of course opts in providing more fine grain results. © 2022 acm.

关键词： Students

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Evaluation of Automotive Radar Simulation Tools for Adverse Weather Conditions: A Comparative analysis of Real measurement, HFSS and Monte-Carlo GO Methods

Evaluation of Automotive Radar Simulation Tools for Adverse ...

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IEEE Radar Conference (RadarConf)

作者： Wachtel, Diogo Schuessler, Christian von dem Bussche, Tetmar Rothmeier, Thomas Vossiek, Martin Huber, Werner TH Ingolstadt CARISSMA IAD Ingolstadt Germany Friedrich Alexander Univ FAU Inst Microwaves & Photon LHFT Erlangen Germany

ISBN: (纸本)9798350329216;9798350329209

Weather-related crashes continue to be a significant cause of fatalities and injuries each year in the automotive field, with adverse weather conditions affecting such properties as the radar detection range, vehicle speed, and detection accuracy. Advanced driver assistance systems (ADAS) rely on clear and accurate environmental perception;automotive radar sensors play a crucial role in these systems, especially in challenging weather. Simulation has emerged as a valuable tool to validate driving functions and sensors in a virtual environment, providing the ability to simulate critical scenarios and adjust weather conditions reproducibly. However, the simulation tool requires validation against real-world scenarios to ensure reliability. Creating a near-to-reality radar model is complex because of the various factors influencing electromagnetic wave propagation, such as surface roughness, temperature, material composition, and thickness. These factors contribute to the complexity of multi-path propagation in radar simulations. This study conducted real measurements and simulations with a high-frequency structure simulator (HFSS) and a Monte-Carlo geometrical optics (Monte-Carlo GO) implementation on various dry and wet samples. The results were calibrated and compared, and they revealed that HFSS moderately outperformed the Monte-Carlo GO method's overall accuracy;nevertheless, the latter requires less computing capability and fewer triangles compared with HFSS;therefore, it may still be a vital choice for specific applications that require a lot of simulation data. Furthermore, the results showed that some materials change reflectivity because of humidity, and they must be adjusted in the simulations.

关键词： Material Properties FMCW Radar

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Performance analysis of Runtime Handling of Zero-Copy for OpenMP Programs on MI300A APUs 24

Performance Analysis of Runtime Handling of Zero-Copy for Op...

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proceedings of the SC '24 Workshops of the International Conference on High Performance computing, Network, Storage, and analysis

作者： Carlo Bertolli Thorsten Blass Lynd Stringer Nicole Aschenbrenner Jan-Patrick Lehr Doru Bercea Dhruva Chakrabarti Lawrence Meadows Ron Lieberman Advanced Micro Devices (AMD) Austin USA Advanced Micro Devices (AMD) Munich Germany Advanced Micro Devices (AMD) Santa Clara USA

ISBN: (纸本)9798350355543

In current discrete GPU systems, the penalty of data movement between host and device memory is inevitable, forcing many large-scale applications to include optimizations that amortize this cost. On systems like the AMD Instinct™ MI300A series accelerators, based on the accelerated processing unit (APU) architecture, host and device memories are unified into a single physical storage. On an APU, the GPU can access memory in the same way the CPU does, thus avoiding the need for additional data movement (zero-copy). To inform developers of MI300A on expected advantages and potential overheads, we follow an experimental approach to study our OpenMP implementation that leverages MI300A zero-copy. Performance results show that zero-copy is faster than the legacy "copy" implementation by a ratio of 1.2X-2.3X for a production-ready application, but that incurs up to 11% penalty for one SPECaccel 2023 benchmark.

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