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检索条件"任意字段=13th IEEE International Symposium on Parallel and Distributed Processing with Applications"

共 2570 条记录，以下是21-30 订阅

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Evaluating Asynchronous parallel I/O on HPC Systems 37

Evaluating Asynchronous Parallel I/O on HPC Systems

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37th ieee international parallel and distributed processing symposium (IPDPS)

作者： Ravi, John Byna, Suren Koziol, Quincey Tang, Houjun Becchi, Michela N Carolina State Univ Raleigh NC 27695 USA Lawrence Berkeley Natl Lab Berkeley CA 94720 USA

ISBN: (纸本)9798350337662

parallel I/O is an effective method to optimize data movement between memory and storage for many scientific applications. Poor performance of traditional disk-based file systems has led to the design of I/O libraries which take advantage of faster memory layers, such as on-node memory, present in high-performance computing (HPC) systems. By allowing caching and prefetching of data for applications alternating computation and I/O phases, a faster memory layer also provides opportunities for hiding the latency of I/O phases by overlapping them with computation phases, a technique called asynchronous I/O. Since asynchronous parallel I/O in HPC systems is still in the initial stages of development, there hasn't been a systematic study of the factors affecting its performance. In this paper, we perform a systematic study of various factors affecting the performance and efficacy of asynchronous I/O, we develop a performance model to estimate the aggregate I/O bandwidth achievable by iterative applications using synchronous and asynchronous I/O based on past observations, and we evaluate the performance of the recently developed asynchronous I/O feature of a parallel I/O library (HDF5) using benchmarks and real-world science applications. Our study covers parallel file systems on two large-scale HPC systems: Summit and Cori, the former with a GPFS storage and the latter with a Lustre parallel file system.

关键词： Performance Evaluation Modeling Asynchronous I/O parallel I/O

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A Task Based Approach for Co-Scheduling Ensemble Workloads on Heterogeneous Nodes

A Task Based Approach for Co-Scheduling Ensemble Workloads o...

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37th ieee international parallel and distributed processing symposium (IPDPS)

作者： Kamatar, Alok Friese, Ryan Gioiosa, Roberto Univ Chicago Dept Comp Sci Chicago IL 60637 USA Pacific Northwest Natl Lab HPC Grp Richland WA USA

ISBN: (纸本)9798350311990

Scientific workflows consist of multiple, connected applications, with data and results flowing from one to another in a pipeline. Traditionally, such workflows are executed in sequential order, storing intermediate data in storage disks. Co-scheduling application workflows concurrently on the same compute nodes would greatly reduce the cost of moving data to/from storage and allow real-time analysis of intermediate results. Nevertheless, most parallel programming runtimes do not allow seamless integration of various applications in a scientific workflow, in part due to the complexity of managing data and resources. the situation is even more complicated for heterogeneous systems. In this work we extend the Minos Computing Library (MCL) runtime to accelerate pipe-lined and parallel workloads where multiple applications are running in the same system. MCL's asynchronous task library and runtime dynamically manages resources to allow co-scheduling of multiple processes sharing heterogeneous resources. In addition, we design a custom extension of the Open Compute Language (OpenCL) to enable multiple processes to share device memory. We enable MCL to coordinate these shared buffers to allow for easy, fast data sharing between applications. Using malleable micro-benchmarks and two application workflows that combine scientific simulation and AI-based analysis, we show that our method outperforms traditional approaches.

关键词： parallel systems Scheduling and Task Partitioning Heterogeneous Systems Workflow management

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Remote Execution of OpenCL and SYCL applications via rOpenCL

Remote Execution of OpenCL and SYCL Applications via rOpenCL

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37th ieee international parallel and distributed processing symposium (IPDPS)

作者： Alves, Rui Rutin, Jose Inst Politecn Braganca Campus Santa Apolonia P-5300253 Braganca Portugal Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Lab Sustentabil & Tecnol Reg Montanha SusTEC Campus Santa Apolonia P-5300253 Braganca Portugal

ISBN: (纸本)9798350311990

With the increasing computational demands of modern applications, heterogeneous systems continue to have an important role in accelerating computationally intensive tasks, a trend confirmed by the most recent HPC architectures. Efficiently exploiting these systems implies the use of specific programming paradigms, such as the classic OpenCL model, or modern single-source alternatives, like SYCL. However, the original execution model of these approaches does not provision for the use of coprocessors other than those directly attached to the host system where the heterogeneous application starts. Over time, several solutions emerged to cope with this limitation, both at the hardware and software level, allowing to exploit remote/distributed co-processors. In this paper, a representative set of seminal OpenCL API Forwarders is revisited and their performance compared with rOpenCL (a recently introduced platform of the same kind), using the classical matrix multiplication case study. In addition, given the importance of SYCL, which has been steadily gaining traction, this paper also exploits the potential of rOpenCL in supporting SYCL applications that use remote accelerators. To that end, another set of benchmarks is used, with both OpenCL and SYCL implementations, allowing not only to gather insight into the performance trade-offs of local versus remote (via rOpenCL) execution, but also about the current performance differential between the two programming models.

关键词： HPC Heterogeneous Computing API Forwarders OpenCL SYCL

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Cost-Effective Dynamic Alliance Pricing Mechanism Based on distributed Edge-Intelligence 21

Cost-Effective Dynamic Alliance Pricing Mechanism Based on D...

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21st ieee international symposium on parallel and distributed processing with applications, 13th ieee international Conference on Big Data and Cloud Computing, 16th ieee international Conference on Social Computing and Networking and 13th international Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Cao, Zhihan Zheng, Xi Guo, Jianxiong Jia, Weijia Xu, Yang Wang, Tian Beijing Normal University Zhuhai China Fudan University Shanghai China Macquarie University Australia Hunan University Changsha China

ISBN: (纸本)9798350329223

In Internet of thing (ToT) system based on edge intelligence, pay-for-use demand has become a consensus, and the pricing of ToT services has attracted the attention of academia and industry. the pricing method based on non-cooperative game allows edge service providers to compete fairly, effectively preventing edge nodes from malicious bidding. However, in this scenario, a task can only be assigned to one edge vendor. Because only one winner can profit each time, it is easy to create a large number of edge service providers who cannot profit for a long time due to insufficient incentives. To this end, a dynamic alliance pricing mechanism based on distributed edge-intelligence is proposed. Edge service providers can freely choose to form an edge dynamic alliance, which not only retains the independence of edge nodes, but also makes full use of the advantages of mutual cooperation between nodes. Tn order to prevent abnormal behaviors of edge nodes, we set up necessary restrictive rules. At the same time, we designed a privacy-enhanced joint pricing prediction algorithm to screen the alliance's candidate solutions to improve pricing efficiency and edge benefit. the experimental results show that, compared with the traditional game method, the performance of the mechanism we proposed improves the utilization rate of edge resources by 32.76%-61.37%. Meanwhile, the prediction accuracy was improved by 16.47%38.86%, and the average prediction time was reduced by 42.81 %-65.57%. © 2023 ieee.

关键词： Profitability

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Cross-subject Drowsiness Recognition Model Based on the Multi-scale parallel Convolution of EEG Channels 21

Cross-subject Drowsiness Recognition Model Based on the Mult...

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作者： Li, Songquan Zhu, Rongbo Yuan, Jun Wan, Shaohua Huazhong Agricultural University College of Informatics Wuhan430070 China Huazhong Agricultural University Shenzhen Institute of Nutrition and Health Wuhan430070 China Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen Branch Guangdong Laboratory for Lingnan Modern Agriculture Genome Analysis Laboratory Ministry of Agriculture Shenzhen518000 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518000 China

ISBN: (纸本)9798350329223

Cross-subject electroencephalogram (EEG) drowsiness recognition is currently one of the most efficient methods. However, the traditional cross-subject approaches overlook the correlation between channel sub-features and effectively utilize the potential connection between frequency features and drowsiness, making it difficult to extract shared features from differentiated subjects. this paper proposes a novel deep learning model called CPCNN to address the challenging of cross-subject drowsiness recognition without calibration and improve accuracy. CPCNN obtains delta frequency band features related to drowsiness from sub-features in the channel dimension, effectively combining the advantages of delta frequency bands in EEG with the richness of channel features. To capture the common features of drowsiness, a channel slicing module is proposed, which divides the spatial and channel features extracted by the deep separable convolution module into multiple sub-features along the channel dimension. In addition, to further improve cross-subject recognition performance, a multi-scale parallel group convolution is designed to capture four key frequency sub-feature groups within the delta frequency band. the experimental results show that CPCNN boosts the recognition performance, significantly outperforms all strong baselines. On balanced and unbalanced public datasets, the average accuracy of CPCNN is 81.49% and 78.26%, respectively, surpassing the excellent baseline method by 3.14% and 0.56%. © 2023 ieee.

关键词： Electroencephalography

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Hadar: Heterogeneity-Aware Optimization-Based Online Scheduling for Deep Learning Cluster 38

Hadar: Heterogeneity-Aware Optimization-Based Online Schedul...

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international parallel and distributed processing symposium (IPDPS)

作者： Sultana, Abeda Xu, Fei Yuan, Xu Chen, Li Tzeng, Nian-Feng Univ Louisiana Lafayette Sch Comp & Informat Lafayette LA USA East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China Univ Delaware Dept Comp & Informat Sci Newark DE USA

ISBN: (纸本)9798350387117;9798350387124

With the wide adoption of deep neural network (DNN) models for various applications, enterprises, and cloud providers have built deep learning clusters and increasingly deployed specialized accelerators, such as GPUs and TPUs, for DNN training jobs. To arbitrate cluster resources among multi-user jobs, existing schedulers fall short, either lacking fine-grained heterogeneity awareness or hardly generalizable to various scheduling policies. To fill this gap, we propose a novel design of a task-level heterogeneity-aware scheduler, Hadar, based on an online optimization framework that can express other scheduling algorithms. Hadar leverages the performance traits of DNN jobs on a heterogeneous cluster, characterizes the task-level performance heterogeneity in the optimization problem, and makes scheduling decisions across both spatial and temporal dimensions. the primal-dual framework is employed, with our design of a dual subroutine, to solve the optimization problem and guide the scheduling design. Extensive trace-driven simulations with representative DNN models have been conducted to demonstrate that Hadar improves the average job completion time (JCT) by 3x over an Apache YARN-based resource manager used in production. Moreover, Hadar outperforms Gavel[1], the state-of-the-art heterogeneity-aware scheduler, by 2.5x for the average JCT, shortens the queuing delay by 13%, and improves FTF (Finish-Time-Fairness) by 1.5%.

关键词： distributed deep learning scheduling optimization

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parallel Simulation of Power Systems with High Penetration of distributed Generation Using GPUs and OpenCL 13

Parallel Simulation of Power Systems with High Penetration o...

引用

13th international symposium on Power Electronics for distributed Generation Systems (PEDG)

作者： Zhang, Junjie Mittenbuehler, Marcel Razik, Lukas Benigni, Andrea Forschungszentrum Julich IEK 10 Energy Syst Engn D-52428 Julich Germany Rhein Westfal TH Aachen D-52056 Aachen Germany Raz InformING D-52070 Aachen Germany JARA Energy D-52425 Julich Germany

ISBN: (数字)9781665466189

ISBN: (纸本)9781665466189

In this paper, we present the use of parallel-in-space simulation approach to accelerate the dynamic simulation of power systems with high penetration of distributed generation, i. e. high number of power electronic devices. the approach is implemented using the OpenCL framework and executed on a graphic processing unit (GPU). We benchmark our prototype implementation using a distribution network with increasing number of distributed generator which is modeled by voltage source inverter. Results show that the computation time for the distributed generators solution stays nearly constant with the increasing number of the distributed generators in the network.

关键词： parallel programming power system simulation power electronics

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Efficient Large Scale Reverse-time Migration Imaging Computation based on distributed Spark Cluster with GPUs 21

Efficient Large Scale Reverse-time Migration Imaging Computa...

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作者： Wangzhang, Suhui Yang, Ruizhang Gu, Rong Li, Bo Liu, Dingjin Wang, Zhaokang Nanjing University State Key Laboratory for Novel Software Technology China Sinopec Geophysical Research Institute China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China

ISBN: (纸本)9798350329223

the reverse-time migration (RTM) imaging algorithm, which is used for complex underground structure analysis, is known as one of the current mainstream method of high-precision seismic imaging. Nowadays, as the fast development of seismic data acquisition technology, there exists large amount of seismic data. thus, the requirement for the efficient computation of the reverse-time migration (RTM) imaging algorithms over large-scale data have been increased greatly. To address this problem, in this paper, we propose a distributed reverse-time migration imaging algorithm based on the widely-used Spark platform first. Based on that, we design and implement a RTM computation solution based on Spark and GPU with several optimizations, including pre-shuffle&caching and load balancing. We also analyze the fault tolerance capability of the proposed distributed algorithm. We evaluate the proposed methods on the typical three-dimensional wavefild benchmark dataset. Experimental results show that the performance of the Spark based reverse-time migration imaging algorithm can scale up near linearly with computing nodes. In addition, the GPU+Spark based version has significant performance improvement on execution efficiency over the Spark-based algorithm. Moreover, the proposed optimization strategies also take effect. In addition, the method proposed in this paper has been applied in the real-world environment of one of the top oil exploration companies world wide, and has achieved significant improvement under the premise of the algorithm correctness guarantee. © 2023 ieee.

关键词： Graphics processing unit

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Proceedings - 2024 ieee international parallel and distributed processing symposium, IPDPS 2024

Proceedings - 2024 IEEE International Parallel and Distribut...

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38th ieee international parallel and distributed processing symposium, IPDPS 2024

ISBN: (纸本)9798350337662

the proceedings contain 88 papers. the topics discussed include: HINT: designing cache-efficient MPI_Alltoall using hybrid memory copy ordering and non-temporal instructions;graph analytics on jellyfish topology;QSync: quantization-minimized synchronous distributed training across hybrid devices;two-stage block orthogonalization to improve performance of s-step GMRES;CloverLeaf on intel multi-core CPUs: a case study in write-allocate evasion;the self-adaptive and topology-aware MPI Bcast leveraging collective offload on Tianhe express interconnect;Picasso: memory-efficient graph coloring using palettes with applications in quantum computing;exploiting long vectors with a CFD code: a co-design show case;and to store or not to store: a graph theoretical approach for dataset versioning.

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Contrastive Learning-Based Generic Audio-to-Lyrics Alignment 21

Contrastive Learning-Based Generic Audio-to-Lyrics Alignment

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作者： Jin, Zhenkun Huang, Shuai Nie, Xin Zhou, Xinlei Yi, Yuanyuan Zhou, Gefei Wuhan Business UIniversity Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Hubei Key Laboratory of Distributed System Security Wuhan China Hubei Engineering Research Center on BigData Security Wuhan China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350329223

In this research, we tackled the complex task of aligning audio and lyrics content automatically. this task entails the precise matching of lyrics with the corresponding audio segments in songs, necessitating the coordination of two distinct modalities: text and audio. To surmount this challenge, we employed a contrastive learning approach that generates cross-modal embeddings capable of establishing connections between the audio and text domains. Furthermore, we adopted a hierarchical and cascading design. Initially, the system predicts synchronization at the sentence level and subsequently refines the alignment at the word level. this design equips the system to handle extended sequences effectively, as cross-correlations leverage secondary memory that corresponds to sequence length. through this innovative approach, we developed a trainable end-to-end system that can harness weakly annotated training data, concurrently learning a robust text model optimized for alignment tasks. Our experiments demonstrated substantial enhancements in average alignment accuracy when utilizing our proposed system, underscoring its robustness compared to previous state-of-the-art models. © 2023 ieee.

关键词： Alignment

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