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检索条件"任意字段=International Parallel and Distributed Processing Symposium"

共 11367 条记录，以下是41-50 订阅

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High-Performance Simulations for Urban Planning: Implementing parallel distributed Multi-Agent Systems in MATSim 23

High-Performance Simulations for Urban Planning: Implementin...

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23rd international symposium on parallel and distributed Computing (ISPDC)

作者： Laudan, Janek Heinrich, Paul Nagel, Kai Tech Univ Berlin Transport Syst Planning Berlin Germany

ISBN: (纸本)9798350369205;9798350369199

As semiconductor design approaches physical limits, computer processing speeds are stagnating. This poses significant challenges for traffic simulations, which are becoming more and more computationally demanding. To maintain fast execution times while accommodating more complex simulations, it is essential to utilize the parallel computing capabilities of modern hardware. This paper discusses the need for an updated architectural design in the MATSim traffic simulation framework to take advantage of parallel computing infrastructures. We introduce a prototype that adapts the existing traffic simulation logic to a distributed parallel algorithm. Extensive benchmarks have been conducted to evaluate the prototype's performance and identify its limitations. The results demonstrate that the prototype performs up to 100 times faster than the current implementation. Based on these findings, we advocate for the integration of a distributed traffic simulation within the MATSim framework and outline necessary steps to enhance the prototype.

关键词： Mulit-Agent Transport Simulation distributed Computing MPI parallel Computing

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Predicting Cross-Architecture Performance of parallel Programs 38

Predicting Cross-Architecture Performance of Parallel Progra...

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international parallel and distributed processing symposium (IPDPS)

作者： Nichols, Daniel Movsesyan, Alexander Yeom, Jae-Seung Sarkar, Abhik Milroy, Daniel Patki, Tapasya Bhatele, Abhinav Univ Maryland Dept Comp Sci College Pk MD 20742 USA Lawrence Livermore Natl Lab Ctr Appl Sci Comp Lawrence KS USA

ISBN: (纸本)9798350387117;9798350387124

A variety of hardware architectures, both CPUs and GPUs, are used today to build supercomputers and parallel clusters. Often times, users can choose which hardware platform they want to run on. Modern scientific workflows have multiple computational tasks, and each task may be better suited for a different architecture in terms of performance. Deciding where to run an application or workflow task is not straightforward because of the complexity of applications, and hardware architectures, which makes performance predictions challenging. Hence, modeling the performance of scientific applications across a variety of architectures is important for achieving the best performance. In this paper, we present a machine learning based methodology to model the relative performance of applications across multiple architectures using hardware performance counters. Our machine learning model can predict the relative performance of an application with a mean absolute error of 0.11, and can be used effectively to make performance-aware and multi-architecture scheduling decisions, reducing makespan by up to 20%.

关键词： performance modeling architectures machine learning multi-cluster scheduling

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An O(N) distributed-memory parallel direct solver for planar integral equations 38

An O(N) distributed-memory parallel direct solver for planar...

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international parallel and distributed processing symposium (IPDPS)

作者： Liang, Tianyu Chen, Chao Martinsson, Per-Gunnar Biros, George Univ Calif Berkeley Berkeley CA 94720 USA North Carolina State Univ Raleigh NC USA Univ Texas Austin Austin TX USA

ISBN: (纸本)9798350387117;9798350387124

Boundary value problems involving elliptic PDEs such as the Laplace and the Helmholtz equations are ubiquitous in mathematical physics and engineering. Many such problems can be alternatively formulated as integral equations that are mathematically more tractable. However, an integral-equation formulation poses a significant computational challenge: solving large dense linear systems that arise upon discretization. In cases where iterative methods converge rapidly, existing methods that draw on fast summation schemes such as the Fast Multipole Method are highly efficient and well-established. More recently, linear complexity direct solvers that sidestep convergence issues by directly computing an invertible factorization have been developed. However, storage and computation costs are high, which limits their ability to solve large-scale problems in practice. In this work, we introduce a distributed-memory parallel algorithm based on an existing direct solver named "strong recursive skeletonization factorization [1]." Specifically, we apply low-rank compression to certain off-diagonal matrix blocks in a way that minimizes computation and data movement. Compared to iterative algorithms, our method is particularly suitable for problems involving ill-conditioned matrices or multiple right-hand sides. Large-scale numerical experiments are presented to show the performance of our Julia implementation.

关键词： distributed memory fast direct solver fast multipole method integral equation Julia matrix factorization O(N) algorithm parallel algorithm

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Towards End-to-End Compression in Lustre 23

Towards End-to-End Compression in Lustre

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23rd international symposium on parallel and distributed Computing (ISPDC)

作者： Fuchs, Anna Squar, Jannek Kuhn, Michael Univ Hamburg Hamburg Germany Otto von Guericke Univ Magdeburg Germany

ISBN: (纸本)9798350369205;9798350369199

Scientific applications generate massive amounts of data, posing storage limitations and network traffic challenges. While scientists struggle with the usage of application-side compression and parallel I/O, we design a transparent feature. Our Lustre-based prototype automatically applies lossless compression, offering flexibility in compression-related decisions to minimize computational costs and optimize application performance. We outline the challenges posed by our prototype and illustrate through a comprehensive assessment how integrating Lustre and ZFS as backend solutions provides the essential elements for performance and scalability: specifically, asynchronous operations and parallel processing of compression. In our evaluation, we illustrate the interaction of different buffer levels within a distributed system. Additionally, we showcase how the I/O pattern, hardware setup, and various system software optimizations can impact overall performance and influence the choice of compression strategy.

关键词： Compression Lustre parallel File System Access Patterns I/O

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UniFaaS: Programming across distributed Cyberinfrastructure with Federated Function Serving 38

UniFaaS: Programming across Distributed Cyberinfrastructure ...

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international parallel and distributed processing symposium (IPDPS)

作者： Li, Yifei Chard, Ryan Babuji, Yadu Chard, Kyle Foster, Ian Li, Zhuozhao Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China Argonne Natl Lab Data Sci & Learning Div Lemont IL USA Univ Chicago Dept Comp Sci Chicago IL USA

ISBN: (纸本)9798350387117;9798350387124

Modern scientific applications are increasingly decomposable into individual functions that may be deployed across distributed and diverse cyberinfrastructure such as supercomputers, clouds, and accelerators. Such applications call for new approaches to programming, distributed execution, and function-level management. We present UniFaaS, a parallel programming framework that relies on a federated function-as-a-service (FaaS) model to enable composition of distributed, scalable, and high-performance scientific workflows, and to support fine-grained function-level management. UniFaaS provides a unified programming interface to compose dynamic task graphs with transparent wide-area data management. UniFaaS exploits an observe-predict-decide approach to efficiently map workflow tasks to target heterogeneous and dynamic resources. We propose a dynamic heterogeneity-aware scheduling algorithm that employs a delay mechanism and a re-scheduling mechanism to accommodate dynamic resource capacity. Our experiments show that UniFaaS can efficiently execute workflows across computing resources with minimal scheduling overhead. We show that UniFaaS can improve the performance of a real-world drug screening workflow by as much as 22.99% when employing an additional 19.48% of resources and a montage workflow by 54.41% when employing an additional 47.83% of resources across multiple distributed clusters, in contrast to using a single cluster.

关键词： federated cyberinfrastructure federated function serving

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QSync: Quantization-Minimized Synchronous distributed Training Across Hybrid Devices 38

QSync: Quantization-Minimized Synchronous Distributed Traini...

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international parallel and distributed processing symposium (IPDPS)

作者： Zhao, Juntao Wan, Borui Peng, Yanghua Lin, Haibin Zhu, Yibo Wu, Chuan Univ Hong Kong Hong Kong Peoples R China ByteDance Inc San Jose CA USA

ISBN: (纸本)9798350387117;9798350387124

A number of production deep learning clusters have attempted to explore inference hardware for DNN training, at the off-peak serving hours with many inference GPUs idling. Conducting DNN training with a combination of heterogeneous training and inference GPUs, known as hybrid device training, presents considerable challenges due to disparities in compute capability and significant differences in memory capacity. We propose QSync, a training system that enables efficient synchronous data-parallel DNN training over hybrid devices by strategically exploiting quantized operators. According to each device's available resource capacity, QSync selects a quantization-minimized setting for operators in the distributed DNN training graph, minimizing model accuracy degradation but keeping the training efficiency brought by quantization. We carefully design a predictor with a bi-directional mixed-precision indicator to reflect the sensitivity of DNN layers on fixed-point and floating-point low-precision operators, a replayer with a neighborhood-aware cost mapper to accurately estimate the latency of distributed hybrid mixed-precision training, and then an allocator that efficiently synchronizes workers with minimized model accuracy degradation. QSync bridges the computational graph on PyTorch to an optimized backend for quantization kernel performance and flexible support for various GPU architectures. Extensive experiments show that QSync's predictor can accurately simulate distributed mixed-precision training with < 5% error, with a consistent 0.27 - 1.03% accuracy improvement over the from-scratch training tasks compared to uniform precision.

关键词： Heterogenous Cluster Quantization Training

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A Scheduling Algorithm for Hyperledger Fabric Based on Transaction Batch processing 23

A Scheduling Algorithm for Hyperledger Fabric Based on Trans...

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23rd international symposium on parallel and distributed Computing (ISPDC)

作者： Jial, Junyu Wu, Ou Li, Shanshan Mao, Rufei Zhang, He Anhui Normal Univ Wuhu Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Nanjing Univ Software Inst Nanjing Peoples R China PLA Army Engn Univ Dept Basic Courses Beijing Peoples R China

ISBN: (纸本)9798350369205;9798350369199

Hyperledger Fabric (Fabric for short), is a consortium blockchain platform that adopts the smart contract paradigm and provides complete operational functions. Although it has become the system with the highest throughput among open source blockchain systems, its performance cannot meet the needs of industrial-grade application scenarios. To further expand the application scenarios of blockchain, this paper proposes a Transaction Batch processing Scheduling (TBPS) algorithm for multi-channel Fabric networks based on Lyapunov optimization theory. The algorithm maximizes the consensus efficiency of the system while ensuring the minimum transaction accumulation, and provides stability conditions and optimal performance for the system under transaction batch processing. Finally, we built a blockchain network of Fabric's latest stable version v2.0 via the cloud platform, providing an order of magnitude of algorithmic parameters by testing transaction processing rates. To simulate the distribution of performance indicators such as transaction delay, system transaction accumulation and average transaction processing rate under different impact factors, and verify the effectiveness of the proposed TBPS algorithm.

关键词： Blockchain Hyperledger Fabric Optimization Scheduling Algorithm

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Introducing parallel and distributed Computing concepts through the use of Flashcards and a Card Game

Introducing Parallel and Distributed Computing concepts thro...

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

作者： Smith, Mary L. Srivastava, Srishti Hawaii Pacific Univ Comp Sci Honolulu HI 96813 USA Univ Southern Indiana Comp Sci Evansville IN USA

ISBN: (纸本)9798350311990

This paper describes two active learning strategies to teach and review fundamental PDC concepts in early computer science courses. Questions were created based on eight PDC concept categories. In the first phase, flashcards were created for students to review the concepts. In the second phase, a card game called PDC Quest was created to allow groups of students to engage collaboratively in learning and reviewing the concepts.

关键词： parallel and distributed Computing Student Engagement Active Learning Early Computer Science Undergraduate Curriculum Flash Cards Game

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Low-Depth Spatial Tree Algorithms 38

Low-Depth Spatial Tree Algorithms

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international parallel and distributed processing symposium (IPDPS)

作者： Baumann, Yves Ben-Nun, Tal Besta, Maciej Gianinazzi, Lukas Hoefler, Torsten Luczynski, Piotr Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9798350387117;9798350387124

Contemporary accelerator designs exhibit a high degree of spatial localization, wherein two-dimensional physical distance determines communication costs between processing elements. This situation presents considerable algorithmic challenges, particularly when managing sparse data, a pivotal component in progressing data science. The spatial computer model quantifies communication locality by weighting processor communication costs by distance, introducing a term named energy. Moreover, it integrates depth, a widely-utilized metric, to promote high parallelism. We propose and analyze a framework for efficient spatial tree algorithms within the spatial computer model. Our primary method constructs a spatial tree layout that optimizes the locality of the neighbors in the compute grid. This approach thereby enables locality-optimized messaging within the tree. Our layout achieves a polynomial factor improvement in energy compared to utilizing a PRAM approach. Using this layout, we develop energy-efficient treefix sum and lowest common ancestor algorithms, which are both fundamental building blocks for other graph algorithms. With high probability, our algorithms exhibit near-linear energy and poly-logarithmic depth. Our contributions augment a growing body of work demonstrating that computations can have both high spatial locality and low depth. Moreover, our work constitutes an advancement in the spatial layout of irregular and sparse computations.

关键词： Graph algorithms parallel algorithms Trees Routing and layout distributed data structures Dataflow architectures Adaptable architectures

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nOS-V: Co-Executing HPC Applications Using System-Wide Task Scheduling 38

nOS-V: Co-Executing HPC Applications Using System-Wide Task ...

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international parallel and distributed processing symposium (IPDPS)

作者： Alvarez, David Sala, Kevin Beltran, Vicenc Barcelona Supercomp Ctr Barcelona Spain

ISBN: (纸本)9798350387117;9798350387124

Future Exascale systems will feature massive parallelism, many-core processors and heterogeneous architectures. In this scenario, it is increasingly difficult for HPC applications to fully and efficiently utilize the resources in system nodes. Moreover, the increased parallelism exacerbates the effects of existing inefficiencies in current applications. Research has shown that co-scheduling applications to share system nodes instead of executing each application exclusively can increase resource utilization and efficiency. Nevertheless, the current oversubscription and co-location techniques to share nodes have several drawbacks which limit their applicability and make them very application-dependent. This paper presents co-execution through system-wide scheduling. Co-execution is a novel fine-grained technique to execute multiple HPC applications simultaneously on the same node, outperforming current state-of-the-art approaches. We implement this technique in nOS-V, a lightweight tasking library that supports co-execution through system-wide task scheduling. Moreover, nOS-V can be easily integrated with existing programming models, requiring no changes to user applications. We showcase how co-execution with nOS-V significantly reduces schedule makespan for several applications on different scenarios, outperforming prior node-sharing techniques.

关键词： HPC parallel programming co-location coexecution task-based programming

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