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3rd international symposium on New Energy and Electrical Technology, ISNEET 2022

3rd International Symposium on New Energy and Electrical Tec...

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3rd international symposium on New Energy and Electrical Technology, ISNEET 2022

ISBN: (纸本)9789819905522

The proceedings contain 102 papers. The special focus in this conference is on New Energy and Electrical Technology. The topics include: Novel Design Technique of Fuzzy Adaptive PI Regulator for Permanent Magnet Synchronous Motor;multi-port Energy Router for Virtual Motor Control;bidirectional Communication Between parallel Buck Converters;identification of Key State Information of Substation Equipment Based on Text Mining and Semantic Analysis Technology;a Simulation Method for the Supply-Demand Conditions of Credits Under Corporate Average Fuel Consumption and New Energy Vehicles Credit Policy;Price Discovery and Volatility Modelling in the EU ETS: Evidence from Phase III;research on Intelligent Energy Management System for Differential Pressure Power Generation;distributed Cooperative Control for DC Microgrid Clusters Interconnected by Multi-port Converter;Quantitative Analysis and Diagnosis of High Resistance Contact Fault Based on ANN Neural Network;real-Time Electromagnetic Transient Simulation for Regional Power Grid Based on Cloudpss;IP Core Design of Phase-Locked Loop for Grid-Connected Photovoltaic Inverter Based on FPGA;Prediction of Minimum Miscibility Pressure (MMP) of CO2-Crude Oil System Based on GWO-RBF Neural Network;Research on Operation Optimization of Crude Oil Pipeline Based on PPSO Algorithm;research on Life Prediction Method of Motor Bearings;research and Application of Power Distribution Monitoring System Based on Edge computing;research on Transformer Inter-turn Fault Protection Method Based on Innovation Graph;research on Relay Protection of Active Distribution Network Based on Innovation Graph;optimal Configuration of Charging Station Based on Multi-objective Genetic Algorithm;Decoupling Control Strategy for Multi-active Bridge DC/DC Converters Based on Dichotomy;preface.

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CloverLeaf on Intel Multi-Core CPUs: A Case Study in Write-Allocate Evasion

CloverLeaf on Intel Multi-Core CPUs: A Case Study in Write-A...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Jan Laukemann Thomas Gruber Georg Hager Dossay Oryspayev Gerhard Wellein Erlangen National High Performance Computing Center Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Programming Models and Compilers Group Computational Science Initiative Brookhaven National Laboratory Upton NY USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

In this paper we analyze the MPI-only version of the CloverLeaf code from the SPEChpc 2021 benchmark suite on recent Intel Xeon "Ice Lake" and "Sapphire Rapids" server CPUs. We observe peculiar breakdowns in performance when the number of processes is prime. Investigating this effect, we create first-principles data traffic models for each of the stencil-like hotspot loops. With application measurements and microbenchmarks to study memory data traffic behavior, we can connect the breakdowns to SpecI2M, a new write-allocate evasion feature in current Intel CPUs. For serial and full-node cases we are able to predict the memory data volume analytically with an error of a few percent. We find that if the number of processes is prime, SpecI2M fails to work properly, which we can attribute to short inner loops emerging from the one-dimensional domain decomposition in this case. We can also rule out other possible causes of the prime number effect, such as breaking layer conditions, MPI communication overhead, and load imbalance.

关键词： distributed processing Codes Electric breakdown Current measurement Lakes Benchmark testing Ice

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A GMM-CGABC Based Energy Replenishment Scheme for On-Demand Wireless Rechargeable Sensor Networks

A GMM-CGABC Based Energy Replenishment Scheme for On-Demand ...

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parallel computing and distributed Systems (PCDS), international symposium on

作者： S.Sudhamsu Mouli T. Veeraiah K. Mahesh M.P. Singh Department of Computer Science and Engineering Ecole Centrale School of Engineering Mahindra University Hyderabad India Department of Computer Science and Engineering National Institute of Technology Patna India

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

Wireless Sensor Networks (WSNs) are constrained by the limited energy capacity of Sensor Nodes (SNs), which hinders their perpetual operation. The advent of Wireless Energy Transfer (WET) technology has emerged as a promising solution to this challenge, enabling the creation of Wireless Rechargeable Sensor Networks (WRSNs). Recent research has shown growing interest in integrating Wireless Mobile Chargers (WMCs) into WRSNs to replenish the energy reserves of critical SNs through WET. A key aspect of WET involves using one or more WMCs to recharge SNs, with optimizing the charging schedule (i.e., the sequence in which SNs are charged) crucial in on-demand WRSNs. In addition, spatiotemporal constraints imposed by the requesting SNs must be considered when devising the WMC's charging schedule. To address these challenges, this work proposes the integration of a Gaussian Mixture Model with a Chaotic Gaussian Artificial Bee Colony (GMM-CGABC) to develop an efficient recharge schedule that revitalizes SNs. This model is designed to optimize SN deployment, maximize network coverage, minimize energy consumption, and extend network longevity. The proposed method determines the optimal charging path for WMCs within each cluster to minimize Average Charging Latency (ACL) while incorporating a novel strategy for fitness function evaluation. Extensive simulations demonstrate that the proposed system outperforms state-of-the-art algorithms, including First Come, First Served (FCFS), Nearest Job Next with Preemption (NJNP), and the Gravitational Search Algorithm (GSA), in terms of charging latency.

关键词： Wireless communication Wireless sensor networks Schedules Energy consumption Clustering algorithms Mixture models parallel processing Spatiotemporal phenomena Optimization Gaussian mixture model

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Cross-System Analysis of Job Characterization and Scheduling in Large-Scale computing Clusters

Cross-System Analysis of Job Characterization and Scheduling...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Di Zhang Monish Soundar Raj Bing Xie Sheng Di Dong Dai University of North Carolina at Charlotte USA Microsoft USA Argonne National Laboratory USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Amid the growing prevalence of artificial intelligence (AI) and deep learning (DL) across industries and science disciplines, high-performance computing (HPC) clusters are increasingly used for DL tasks, in addition to their traditional role in numerical simulations. This shared use of HPC systems for both DL tasks and numerical codes is altering the characteristics of their workloads, leaving many previously observed and well-accepted workload characteristics unchecked, potentially outdated and imprecise, for these new mixed workloads. Thus, to understand these changes and their implications for job scheduling, we conduct a cross-system analysis of job characterization and its scheduling across a range of representative clusters, including two classic HPC clusters (Mira, Theta), two classic DL clusters (Philly, Helios), and a hybrid cluster (Blue Waters).Our cross-system analyses focus on three key aspects: 1) job geometries (job size, run time, arrival interval) and their impacts on scheduling results, 2) job failure patterns and their correlations to job geometries, 3) per-user behaviors and their indication on job scheduling. Through these comparisons, we confirm notable disparity and similarity among different systems (summarized as 8 takeaways), which would help design more efficient job schedulers for the future HPC systems. We further introduce two use case studies (job runtime prediction and adaptive relaxed backfilling) that leverage these new observations and show the improved job scheduling results. In summary, we expect our observations, insights, and systematic analysis approach can be useful for the community in building efficient HPC scheduling for the upcoming hybrid workloads.

关键词： Geometry Deep learning Job shop scheduling Systematics Runtime Processor scheduling Numerical simulation

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Efficient Data-parallel Continual Learning with Asynchronous distributed Rehearsal Buffers

Efficient Data-Parallel Continual Learning with Asynchronous...

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IEEE/ACM international symposium on Cluster computing and the Grid (CCGRID)

作者： Thomas Bouvier Bogdan Nicolae Hugo Chaugier Alexandru Costan Ian Foster Gabriel Antoniu Inria CNRS IRISA University of Rennes Argonne National Laboratory

ISBN: (数字)9798350395662

ISBN: (纸本)9798350395679

Deep learning has emerged as a powerful method for extracting valuable information from large volumes of data. However, when new training data arrives continuously (i.e., is not fully available from the beginning), incremental training suffers from catastrophic forgetting (i.e., new patterns are reinforced at the expense of previously acquired knowledge). Training from scratch each time new training data becomes available would result in extremely long training times and massive data accumulation. Rehearsal-based continual learning has shown promise for addressing the catastrophic forgetting challenge, but research to date has not addressed performance and scalability. To fill this gap, we propose an approach based on a distributed rehearsal buffer that efficiently complements data-parallel training on multiple GPUs, allowing us to achieve short runtime and scalability while retaining high accuracy. It leverages a set of buffers (local to each GPU) and uses several asynchronous techniques for updating these local buffers in an embarrassingly parallel fashion, all while handling the communication overheads necessary to augment input mini-batches (groups of training samples fed to the model) using unbiased, global sampling. In this paper we explore the benefits of this approach for classification models. We run extensive experiments on up to 128 GPUs of the ThetaGPU supercomputer to compare our approach with baselines representative of training-from-scratch (the upper bound in terms of accuracy) and incremental training (the lower bound). Results show that rehearsal-based continual learning achieves a top-5 classification accuracy close to the upper bound, while simultaneously exhibiting a runtime close to the lower bound.

关键词： Training Continuing education Deep learning Accuracy Runtime Upper bound Scalability Training data Graphics processing units Supercomputers

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Combining Static and Dynamic Analysis to Query Characteristics of HPC Applications

Combining Static and Dynamic Analysis to Query Characteristi...

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Welch, Aaron Hernandez, Oscar Chapman, Barbara Univ Houston Houston TX 77004 USA Oak Ridge Natl Lab Oak Ridge TN USA SUNY Stony Brook Stony Brook NY 11794 USA

ISBN: (纸本)9781665435772

Emerging HPC platforms are becoming more difficult to program as a result of systems with different node architectures, some with a small number of "fat" heterogenous nodes (consisting of multiple accelerators) and others with a large number of "thin" homogenous nodes consisting of multi-core CPUs connected with high speed interconnects. New programming models are emerging to address performance portability of the applications as well as a set of scientific libraries that applications can use to exploit these architectures efficiently. To port applications to new architectures, developers need information about their source code characteristics including static and dynamic (e.g. performance) information to refactor the code, understand their data and code structure, and library usage as well as program information to direct their optimisation efforts and make key decisions. In this paper, we describe a tool that combines compiler and profiler information to query program characteristics in a given programming environment. Static and dynamic data about applications is collected and stored together in an SQL database that can be later queried to study application characteristics and patterns. We will demonstrate the capabilities of this tool with an application-driven case study that aims at understanding application code and its use of scientific libraries via a real world example from the molecular simulation application CP2K.

关键词： distributed processing Program processors Databases Conferences Tools Programming Libraries

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Druto: Upper-Bounding Silent Data Corruption Vulnerability in GPU Applications

Druto: Upper-Bounding Silent Data Corruption Vulnerability i...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Md Hasanur Rahman Sheng Di Shengjian Guo Xiaoyi Lu Guanpeng Li Franck Cappello University of Iowa IA USA Argonne National Laboratory IL USA Amazon Web Services CA USA University of California Merced CA USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Due to the increasing scale of high-performance computing (HPC) systems, transient hardware faults have become a major reliability concern. Consequently, Silent Data Corruptions (SDCs) due to these faults have been a common insidious consequence in GPU applications. Developers often measure the application resilience with a set of program test inputs available in the benchmark suite, assuming the resilience would not fluctuate much among different inputs. However, we observe that this assumption often results in an over-optimistic evaluation for GPU applications. As a result, the subsequent SDC protection following the evaluation can hardly meet the expected reliability bar in the production environment, where applications would run with potentially arbitrary input values. To this end, we propose Druto – a compiler-based automated technique that searches for inputs to incrementally approach the upper bound of a GPU application’s SDC probability. We develop Druto based on the property that the resilience profiles of a small group of representative threads in a GPU kernel can approximately rank various inputs in terms of the overall SDC probability. Therefore, Druto strategically steers the search towards new program inputs that efficiently portray the overall SDC probability. Evaluation shows that the SDC probability derived from Druto’s input generation is as much as 74× higher than that from existing techniques. Moreover, existing techniques cannot find our generated inputs even given 5× more search time.

关键词： Upper bound High performance computing Graphics processing units Production Hardware Reliability Kernel

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LaSS: Running Latency Sensitive Serverless Computations at the Edge 21

LaSS: Running Latency Sensitive Serverless Computations at t...

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30th international symposium on High-Performance parallel and distributed computing (HPDC)

作者： Wang, Bin Ali-Eldin, Ahmed Shenoy, Prashant Univ Massachusetts Amherst Amherst MA 01003 USA Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9781450382175

Serverless computing has emerged as a new paradigm for running short-lived computations in the cloud. Due to its ability to handle IoT workloads, there has been considerable interest in running serverless functions at the edge. However, the constrained nature of the edge and the latency sensitive nature of workloads result in many challenges for serverless platforms. In this paper, we present LaSS, a platform that uses model-driven approaches for running latency-sensitive serverless computations on edge resources. LaSS uses principled queuing-based methods to determine an appropriate allocation for each hosted function and auto-scales the allocated resources in response to workload dynamics. LaSS uses a fair-share allocation approach to guarantee a minimum of allocated resources to each function in the presence of overload. In addition, it utilizes resource reclamation methods based on container deflation and termination to reassign resources from over-provisioned functions to under-provisioned ones. We implement a prototype of our approach on an OpenWhisk serverless edge cluster and conduct a detailed experimental evaluation. Our results show that LaSS can accurately predict the resources needed for serverless functions in the presence of highly dynamic workloads, and reprovision container capacity within hundreds of milliseconds while maintaining fair share allocation guarantees.

关键词： cloud computing edge computing serverless computing Functionas-a-Service (FaaS) service-level agreement (SLA) queueing theory

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Proceedings - 2013 IEEE 12th international symposium on parallel and distributed computing, ISPDC 2013

Proceedings - 2013 IEEE 12th International Symposium on Para...

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2013 IEEE 12th international symposium on parallel and distributed computing, ISPDC 2013

ISBN: (纸本)9780769550183

The proceedings contain 28 papers. The topics discussed include: predicting the flexibility of dynamic loop scheduling using an artificial neural network;dynamic load balancing based on applications global states monitoring;Monext: an accounting framework for infrastructure clouds;development of polish infrastructure for advanced scientific research - status and current achievements;analyzing the robustness of scheduling algorithms using divisible load theory on heterogeneous systems;distributed methodologies for imbalanced classification problems: parameter analysis and tuning;efficient linearizable write operations using bounded global time uncertainty;study of techniques and applications for fast data transfers in HBDP networks;topology-aware process mapping on clusters featuring NUMA and hierarchical network;user experience of mobile cloud applications - current state and future directions;and distributed data storage in support for context-aware applications.

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parallel Inference of Phylogenetic Stands with Gentrius

Parallel Inference of Phylogenetic Stands with Gentrius

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Anastasis Togkousidis Olga Chernomor Alexandros Stamatakis Computational Molecular Evolution Group Heidelberg Institute for Theoretical Studies Institute for Theoretical Informatics Karlsruhe Institute of Technology Center for Integrative Bioinformatics Vienna (CIBIV) Max Perutz Laboratories University of Vienna and Medical University of Vienna Vienna Bio Center (VBC) Vienna Austria Biodiversity Computing Group Institute of Computer Science Foundation for Research and Technology - Hellas

Multi-locus datasets are frequently used to infer phylogenies instead of using single locus. Missing data constitute a common challenge in such datasets as they can lead to stands, that is, sets of trees that are compatible with the incomplete per-locus trees. Under many common criteria the trees from one stand have identical score. Hence, identifying stands and determining their sizes is of crucial importance for a robust phylogenetic analysis. Recently, Chernomor et al. published Gentrius, a branch-and-bound algorithm that enumerates all stand trees given a set of unrooted incomplete locus trees. Despite its efficiency, the pattern and proportion of missing data in multi-locus datasets can still induce extremely long execution ***, we introduce the parallel version of the Gentrius algorithm. Our parallelization deploys a thread-pooling mechanism that maintains threads that finish early in busy-wait mode, such that they can contribute to solving long-running tasks. Thereby, we substantially reduce load imbalance and attain high parallel efficiency. Our performance assessment up to 16 cores yields linear parallel speedups on both, simulated, and empirical data. The parallel version of Gentrius is available as open source code under GNU GPL at https://***/togkousa/iqtree2/tree/terragen. All data we used for our analyses, are available for download at https://***/material/***.

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