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Ponte Vecchio Across the Atlantic: Single-Node Benchmarking of Two Intel GPU systems 24

Ponte Vecchio Across the Atlantic: Single-Node Benchmarking ...

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

作者： Thomas Applencourt Aditya Sadawarte Servesh Muralidharan Colleen Bertoni JaeHyuk Kwack Ye Luo Esteban Rangel John Tramm Yasaman Ghadar Arjen Tamerus Chris Edsall Tom Deakin Argonne Leadership Computing Facility Argonne National Laboratory Lemont IL USA School of Computer Science University of Bristol Bristol UK University of Cambridge Cambridge UK

ISBN: (纸本)9798350355543

Intel Data Center GPU Max 1550, known as Ponte Vecchio (PVC), is a new Intel GPU architecture for high-performance computing. It is the basis of two systems on the June 2024 Top 500 list, Dawn (#51) and Aurora (#2).This work provides micro-benchmarking data on PVCs from which application developers may benefit, shows how the micro-benchmarking results are indicative of mini-app performance on PVC, and demonstrates real applications on large-scale Intel GPU *** quantify the obtainable performance from PVC systems through micro-benchmarking fundamental architectural properties. We evaluate the performance of four mini-apps with known performance characteristics, and two full applications, comparing performance on a node of Aurora and Dawn with a node of NVIDIA H100 GPUs and a node of AMD MI250 GPUs. We show the figure-of-merit of the mini-apps on a single PVC ranges from 0.6-1.8X the performance of an H100, and 0.8-7.5X of a MI250.

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Research on railroad line spatial information management technology

Research on railroad line spatial information management tec...

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2023 International Conference on Remote Sensing, Mapping, and Geographic systems, RSMG 2023

作者： Li, Congxu Xu, Xiaolei Feng, Boqing Zhang, Maolin Institute of Computing Technologies China Academy of Railway Sciences Corporation Limited China Beijing University of Civil Engineering and Architecture School of Geomatics and Urban Spatial Informatics Beijing100044 China

ISBN: (纸本)9781510668898

With the completion of railroad line resurvey work, the measurement data are scattered in various units and lack unified storage and management, which brings many challenges and problems to railroad operation and maintenance. To solve this problem, this study proposes a multi-version, multi-temporal spatial data storage technology, as well as a distributed fast storage technology and a spatial service engine by studying the data management work, aiming to realize unified and fast storage, management and sharing of railroad survey data to improve the reliability, consistency and validity of data. First, this study focuses on the multi-version and multi-temporal spatial data storage technology. By adopting this technology, a large amount of spatial data generated by railroad line resurvey can be effectively handled, and the storage and management of different versions and temporal phases of data can be realized. Meanwhile, distributed fast storage technology is introduced to provide high-speed data access and processing capability to cope with the storage and processing demand of massive data. In addition, using the spatial service engine, fast query and analysis of measurement data can be realized. Second, this study also conducts an in-depth research on the railroad line measurement data management system. First, the overall architecture of the system is designed to ensure the scalability and flexibility of the system. Then, the data structure of the system was optimized to accommodate the storage and query requirements of multi-version and multi-temporal data. In terms of technical architecture, distributed storage technology is adopted to realize fast storage and retrieval of remote sensing, 2D vector and 3D model data. In addition, the sharing and exchange of measurement data is realized through information sharing and information interface. The innovation of this study is to propose a multi-version and multi-temporal spatial data storage technology, and to reali

关键词： Information management

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Platform Performance Suite (PPS): A Framework for Performance analysis & Diagnosis of Complex Cyber-physical systems 25

Platform Performance Suite (PPS): A Framework for Performanc...

引用

2025 International Conference on Performance Engineering-ICPE-Annual

作者： Triantafyllidis, Konstantinos Blankenstein, Yuri Niknam, Sobhan Hegge, Jos ASML Eindhoven Netherlands TNO ESI Eindhoven Netherlands

ISBN: (纸本)9798400710735

The performance of cyber-physical systems (CPS) is a determining factor for their success and often needs to be guaranteed. When performance issues occur, their analysis and the identification of the root-cause should be fast. Typically, the analysis requires the association of system observations (i.e., tracing) to design and implementation artifacts. For this association, multidisciplinary domain knowledge is required, which typically resigns in the workforce but is unmanageable due to the immense and continuously increasing system complexity. This paper proposes a model-based method, the Platform Performance Suite (PPS), supported by corresponding tooling. PPS enables automated analysis for the identification of the root-cause when performance issues, like system throughput loss, occur. The cornerstone of the approach is the Lifecycle Software Architecture model which captures the domain knowledge in models by modeling all the relationships among the system artifacts from specification up to the runtime phase. The performance analysis at runtime phase is carried out on TMSC models guaranteeing the generic applicability of the method, while the connection to the other lifecycle phases enables the automated identification of the root-cause by pinpointing specific artifacts. The evaluation of the method has been carried out on a world-leading complex and high-performing CPS, the ASML TWINSCAN system, where a throughput loss needs to be addressed promptly. PPS has shown remarkable speed-up in the identification of the root-cause compared to the state-of-practice method.

关键词： performance throughput analysis CPS model-based tracing

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Memory Access Scheduling to Reduce Thread Migrations 2022

Memory Access Scheduling to Reduce Thread Migrations

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31st acm SIGPLAN International Conference on Compiler Construction (CC)

作者： Damani, Sana Barua, Prithayan Sarkar, Vivek Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9781450391832

It has been widely observed that data movement is emerging as the primary bottleneck to scalability and energy efficiency in future hardware, especially for applications and algorithms that are not cache-friendly and achieve below 1% of peak performance on today's systems. The idea of "moving compute to data" has been suggested as one approach to address this challenge. While there are approaches that can achieve this migration in software, hardware support is a promising direction from the perspectives of lower overheads and programmer productivity. Migratory thread architectures migrate lightweight hardware thread contexts to the location of the data instead of transferring data to the requesting processor. However, while transporting thread contexts is cheaper than moving data, thread migrations still incur energy and bandwidth overheads and can be particularly expensive if threads frequently migrate in a ping-pong manner between processors due to poor locality of access. In this paper, we propose Memory Access Scheduling, a new compiler optimization that aims to reduce the number of overall thread migrations when executing a program on migratory thread architectures. Our experiments show performance improvements with a geometric mean speedup of 1.23x for a set of 7 explicitly-parallelized kernels, and of 1.10x for a set of 15 automatically-parallelized kernels. We believe that memory access scheduling will also be an important optimization for other locality-centric architectures that benefit from software thread migrations, such as multi-threaded NUMA architectures.

关键词： Compilers Emu Architecture Instruction Scheduling Integer Linear Programming (ILP) Sequential Ordering Problem Dataflow analysis Thread Migration

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Investigating measurement Scheduling Strategies in Low Resource Networks (Poster) 21

Investigating Measurement Scheduling Strategies in Low Resou...

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4th acm SIGCAS Conference on computing and Sustainable Societies, COMPASS 2021

作者： Sharma, Taveesh Chavula, Josiah University of Cape Town Cape Town South Africa

ISBN: (纸本)9781450384537

Community networks have been proposed by many networking experts and researchers as a way to bridge connectivity gaps in rural and remote areas of the world. Many community networks are built with low-capacity computing devices and low-capacity links. Such community networks are examples of low resource networks. The design and implementation of computer networks using limited hardware and software resources has been studied extensively in the past, but scheduling strategies for conducting measurements on these networks remains an important area to be explored. In this study, the design of a Quality of Service monitoring system is proposed, focusing on performance of scheduling of network measurement jobs in a low-resource network. In this paper, we present a testbed for conducting performance evaluation of two measurement scheduling algorithms and present an analysis of trends in their performance with varying experiment profiles. © 2021 Owner/Author.

关键词： Quality of service

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12th Annual acm Symposium on Cloud computing, SoCC 2021

12th Annual ACM Symposium on Cloud Computing, SoCC 2021

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12th Annual acm Symposium on Cloud computing, SoCC 2021

ISBN: (纸本)9781450386388

The proceedings contain 46 papers. The topics discussed include: Lorien: efficient deep learning workloads delivery;elastic hyperparameter tuning on the cloud;siren: byzantine-robust federated learning via proactive alarming;automating instrumentation choices for performance problems in distributed applications with VAIF;tprof: performance profiling via structural aggregation and automated analysis of distributed systems traces;cloud-scale runtime verification of serverless applications;building reliable cloud services using coyote actors;atoll: a scalable low-latency serverless platform;kraken : adaptive container provisioning for deploying dynamic DAG applications in serverless platforms;and Mu: an efficient, fair and responsive serverless framework for resource-constrained edge clouds.

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StencilFlow: Mapping Large Stencil Programs to Distributed Spatial computing systems 21

StencilFlow: Mapping Large Stencil Programs to Distributed S...

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19th IEEE/acm International Symposium on Code Generation and Optimization (CGO)

作者： Licht, Johannes de Fine Kuster, Andreas De Matteis, Tiziano Ben-Nun, Tal Hofer, Dominic Hoefler, Torsten Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland MeteoSwiss Zurich Switzerland

ISBN: (纸本)9781728186139

Spatial computing devices have been shown to significantly accelerate stencil computations, but have so far relied on unrolling the iterative dimension of a single stencil operation to increase temporal locality. This work considers the general case of mapping directed acyclic graphs of heterogeneous stencil computations to spatial computing systems, assuming large input programs without an iterative component. StencilFlow maximizes temporal locality and ensures deadlock freedom in this setting, providing end-to-end analysis and mapping from a high-level program description to distributed hardware. We evaluate our generated architectures on a Stratix 10 FPGA testbed, yielding 1.31 TOp /s and 4.18 TOp/s on single-device and multi-device, respectively, demonstrating the highest performance recorded for stencil programs on FPGAs to date. We then leverage the framework to study a complex stencil program from a production weather simulation application. Our work enables productively targeting distributed spatial computing systems with large stencil programs, and offers insight into architecture characteristics required for their efficient execution in practice.

关键词： Mapping

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An Integrated CNN-RNN-SVM Framework for Classification of MRI Brain Tumor Images

An Integrated CNN-RNN-SVM Framework for Classification of MR...

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2024 IEEE International Conference on Imaging systems and Techniques, IST 2024

作者： Jha, Abhinandan Singh, Onkar Singh, Koushlendra Kumar Akbari, Akbar Sheikh Takei, Masahiro Srm Institute of Science and Technology Dept.of Cse Chennai603203 India Manipal University Dept.of Cse Rajasthan Jaipur303007 India National Institute of Technology Jamshedpur Machine Vision and Intelligence Lab Dept.of Cse Jamshedpur831014 India School of Built Environment Engineering and Computing Leeds Beckett University United Kingdom Chiba University Department of Mechanical Engineering Chiba Japan

ISBN: (纸本)9798350378214

The early identification and categorization of brain tumor through MRI scans are pivotal for effective medical intervention. The present article encompasses a novel integrated framework that combines traditional machine learning and deep learning methods to categorize images of brain tumors. Utilizing the VGG-19 model pre-trained on ImageNet, we extract high-level features from MRI images, which are further processed by a Long Short-Term Memory (LSTM) framework to extract spatial and temporal dependencies within the data. To manage the high-dimensional feature space effectively, we employ Principal Component analysis (PCA) for dimensionality reduction, followed by a Support Vector Machine (SVM) for the final classification task. We utilized a variety of data augmentation approach to enhance the capability of the architecture to generalize. Additionally, we fine-tuned the training parameters by employing the Adam optimizer along with early stopping and learning rate decay strategies. The model demonstrated exceptional precision, recall, and Fl-score metrics, with an accuracy of 97.86%. This study not only validates the effectiveness of integrating CNNs, RNNs, and SVMs but also opens avenues for future research in medical image analysis using hybrid deep learning frameworks. Experimental outcomes demonstrate that the proposed model significantly improves the accuracy of brain tumor classification compared to previous methods, offering a promising tool for aiding radiologists in the rapid and accurate diagnosis of brain tumors. © 2024 IEEE.

关键词： Dimensionality reduction

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Secure Reboots for Real-Time Cyber-Physical systems 4

Secure Reboots for Real-Time Cyber-Physical Systems

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4th Joint Workshop on CPS and IoT Security and Privacy (CPSIoTSec)

作者： Banerjee, Vijay Hounsinou, Sena Olufowobi, Habeeb Hasan, Monowar Bloom, Gedare Univ Colorado Colorado Springs CO 80907 USA Univ Texas Arlington Arlington TX 76019 USA Wichita State Univ Kansas City KS USA

ISBN: (纸本)9781450398763

Cyber-Physical systems (CPS) such as industrial control systems, automobiles, and medical devices often consist of applications with real-time properties. Due to the safety-critical nature of the application domain, multiple security and fault tolerance approaches have been studied and used in safety-critical CPS. One of the popular approaches for CPS safety is the Simplex architecture, which has also been used recently to strengthen the security of the CPS. The simplex architecture supports the integration of safe controllers for dependable systems, and when combined with periodic restarts, the architecture can reset the CPS into a safe state after each restart. However, these restart-based systems do not protect the system against attacks that persist beyond a restart. Such attacks can be mitigated using secure boot, which is a widely used approach for securing general computing systems but is not used in real-time systems due to the overhead of the boot process. This paper presents an analytical framework and derives feasibility conditions to enable secure reboots in real-time applications. The schedulability conditions presented can be used to design and integrate secure reboot into Simplex-based CPS. Our analysis shows that secure boot adds a deterministic and low-performance overhead, which can be as low as 0.08%.

关键词： Secure Boot Real-Time systems Cyber-Physical systems

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A Novel Transfer Learning Approach for Detecting Partial Shading in Photovoltaic systems

A Novel Transfer Learning Approach for Detecting Partial Sha...

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IEEE/acm International Symposium on Big Data computing (BDC)

作者： Ali Teta Maissa Medkour Ahmed Chennana Ammar Chouchane Yassine Himeur Shadi Atalla Wathiq Mansoor El Ouanas Belabbaci Applied Automation and Industrial Diagnostics Laboratory Department of Electrical Engineering University of Djelfa Djelfa Algeria LI3C Laboratory University of Biskra Biskra Algeria University Center of Barika Batna Algeria College of Engineering and Information Technology University of Dubai Dubai UAE Laboratory of Medical Informatics (LIMED) Faculty of Technology University of Bejaia Bejaia Algeria

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

In Photovoltaic systems, detecting partial shading is critical for optimizing energy output and ensuring system reliability. This paper presents a novel method for partial shading detection in photovoltaic systems, leveraging transfer learning to improve accuracy and efficiency. By utilizing a pre-trained InceptionV3 model, discriminative features are extracted from time series signals. To align with the architectural requirements of InceptionV3, these time series signals are transformed into 2D pixel-mapped images. The proposed model is rigorously validated using both balanced and unbalanced scenarios on the Grid-connected PV System Faults (GPVS-Faults) dataset, achieving remarkable accuracies of 96.73% and 94.59% in the respective scenarios. This approach represents a significant advancement in accurately identifying partial shading, thus enhancing the performance and reliability of solar energy systems.

关键词： Photovoltaic systems Accuracy Scalability Transfer learning Time series analysis Solar energy Big Data Feature extraction Distance measurement Reliability

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