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检索条件"任意字段=International Symposium on Databases in Parallel and Distributed Systems"

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Enhancing File Recovery from distributed File systems (DFSs) Using Erasure Coding and Replication

Enhancing File Recovery from Distributed File Systems (DFSs)...

引用

international Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Prince Raj Sapna Sinha Amity University Noida Uttar Pradesh UP India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

Several solutions have been used in distributed file systems to increase data availability and dependability. Historically, replication techniques were the favored method for storing data in such systems. However, there has been a recent movement towards the use of erasure-coding (EC) methods to overcome space efficiency concerns. While EC is more successful than replication approaches in overcoming space efficiency difficulties, it introduces its own performance degradation factors, such as encoding and decoding delays, as well as input and output (I/O). To address these issues, this research proposes a buffering and combining strategy. This solution combines several I/O requests made throughout the encoding process in an EC-based distributed file system into a single operation, resulting in more efficient processing. In addition, the research recommends four recovery strategies to reduce the impact of decoding on disk input/output loads. These measures include disk input/output load distribution, random block layout, multi-thread parallel recovery, and the matrix recycling approach. All of these methods are intended to optimize the distribution of disk input/output loads produced during the decoding process.

关键词： Fault tolerance File systems Scalability Fault tolerant systems distributed databases Computer architecture Throughput

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

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

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international symposium on parallel and distributed Processing (IPDPS)

作者： David Álvarez Kevin Sala Vicenç Beltran Barcelona Supercomputing Center Barcelona Spain

ISBN: (数字)9798350387117

ISBN: (纸本)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 *** 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： Schedules distributed processing Runtime Program processors Programming parallel processing Throughput

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A 3D Tile Generation Method Based on Adaptive Meshing and Octree Segmentation

A 3D Tile Generation Method Based on Adaptive Meshing and Oc...

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Computer Engineering and Intelligent Communications (ISCEIC), international symposium on

作者： Yuan Ji Junfeng Qian Ruman Yang Tangshu Ji Haihua Liao Guizhou Power Grid Co Ltd Guizhou China Southern Power Grid Digital Grid Technology (Guangdong) Co. Ltd Guangzhou China

ISBN: (数字)9798331518677

ISBN: (纸本)9798331518684

As urban informatization advances, traditional geographic information systems (GIS) evolve into the three-dimensional realm. With its realism and high-precision measurement capabilities, three-dimensional geographic information has widespread application in land use management, digital city development, and the construction of smart cities. Addressing the challenges posed by large-scale, unevenly distributed 3D scene data, this study introduces an innovative 3D Tile construction technique based on adaptive meshing and octree structures. This approach enhances the geometric fidelity and detailed data representation and significantly accelerates data loading speeds. Managing massive, heterogeneous 3D model datasets efficiently supports sustainable development and effective management in homeland spatial planning, digital city building, and smart city initiatives.

关键词： Adaptation models Three-dimensional displays Smart cities Tiles Octrees Loading distributed databases Feature extraction Geographic information systems Testing

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Adaptive Spatially Aware I/O for Multiresolution Particle Data Layouts 35

Adaptive Spatially Aware I/O for Multiresolution Particle Da...

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

作者： Usher, Will Huang, Xuan Petruzza, Steve Kumar, Sidharth Slattery, Stuart R. Reeve, Sam T. Wang, Feng Johnson, Chris R. Pascucci, Valerio Univ Utah SCI Inst Salt Lake City UT 84112 USA Utah State Univ Logan UT 84322 USA Univ Alabama Birmingham Birmingham AL USA Oak Ridge Natl Lab Oak Ridge TN USA Lawrence Livermore Natl Lab Livermore CA USA

ISBN: (纸本)9781665440660

Large-scale simulations on nonuniform particle distributions that evolve over time are widely used in cosmology, molecular dynamics, and engineering. Such data are often saved in an unstructured format that neither preserves spatial locality nor provides metadata for accelerating spatial or attribute subset queries, leading to poor performance of visualization tasks. Furthermore, the parallel I/O strategy used typically writes a file per process or a single shared file, neither of which is portable or scalable across different HPC systems. We present a portable technique for scalable, spatially aware adaptive aggregation that preserves spatial locality in the output. We evaluate our approach on two supercomputers, Stampede2 and Summit, and demonstrate that it outperforms prior approaches at scale, achieving up to 2.5v faster writes and reads for nonuniform distributions. Furthermore, the layout written by our method is directly suitable for visual analytics, supporting low-latency reads and attribute-based filtering with little overhead.

关键词： parallel I/O Load Balancing

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Accelerating Message Passing Operation of GDL-Based Constraint Optimization Algorithms Using Multiprocessing 19

Accelerating Message Passing Operation of GDL-Based Constrai...

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19th IEEE international symposium on parallel and distributed Processing with Applications (IEEE ISPA)

作者： Zaoad, Syeed Abrar Tanjim, Tauhid Hasan, Mir Mamun-Or-Rashid, Md Almansour, Ibrahem Abdullah Khan, Md Mosaddek Univ Dhaka Dept Comp Sci & Engn Dhaka 1000 Bangladesh Austin Peay State Univ Dept Comp Sci & Informat Technol Clarksville TN 37040 USA King Abdulaziz City Sci & Technol Dept Comp Sci & Informat Technol Riyadh Saudi Arabia

ISBN: (纸本)9781665435741

This paper develops a new message passing protocol that can be used to speed up the inference process of Generalized Distributive Law (GDL) based constraint optimization algorithms. In particular, we parallelize the inference process of the GDL-based constraint optimization algorithms which have been widely used to solve Constraint Optimization Problems (COPs) in different real world applications, such as wire routing, image analysis, computer-aided gas pipeline operation, and numerical optimization. It is worth noting that the proposed new parallel approach can be applied to accelerate any GDL-based message passing algorithms, such as Max-Sum, Max-Product or Sum-Product. In this work, we make use of the available Central Processing Unit (CPU) cores of a system to minimize the time it requires to execute a given algorithm. Consequently, this reduced computation time will accelerate the GDL-based COP algorithms which can be used in larger systems. However, the main challenge is to maintain the quality of the solution while minimizing the completion time. Our proposed protocol specifically takes this trade-off into account, and our empirical results depict that it is able to produce the same solution quality while accelerating the inference process 2-10 times faster compared to the state-of-theart.

关键词： COP Generalized distributive law parallel computing Speeding up

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CODC-pyParaQC: A design and implementation of parallel quality control for ocean observation big data

CODC-pyParaQC: A design and implementation of parallel quali...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Huifeng Yuan Tianyan Li Zhong Jin Lijing Cheng Zhetao Tan Bin Zhang Yanjun Wang Computer Internet Information Center Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Computer Internet Information Center Chinese Academy of Sciences Beijing China Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China Institute of Oceanography Chinese Academy of Sciences Qingdao China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

High-quality ocean observation is essential for research and applications in ocean exploration and climate change. With moving into the era of big data in recent years, it becomes crucial to process these massive raw observations accurately and efficiently. This paper addressed issues encountered in processing ocean big data within traditional delayed-mode quality control systems, including substantial serial I/O workloads and frequent context switching. A parallel quality control scheme named CODC-pyParaQC was proposed by constructing computing process groups. It retains the advantages of the existed delayed-mode quality control system (e.g. CODC-QC) while improving the efficiency of the quality control procedure, solving the feasibility of a large-scale parallel computation of the quality control scheme and realizing the (near) real-time quality control of massive ocean observation profiles. The results showed that the efficiency of single-node quality control has been improved by about 10 times. Leveraging the computing power of supercomputers and employing multi process groups for cross-node parallel computation, we have developed a fast and efficient (near) real-time quality control procedure. This system processed approximately 22,548,733 temperature profiles from the world ocean database (1940-2023) in about 6.5 hours. Our new quality control scheme can ensure the computing capability necessary for establishing a high-quality ocean observation profile database.

关键词： Climate change Oceans Observers Quality control Big Data Oceanography Control systems Database systems Process control Real-time systems Computational efficiency Supercomputers Ocean temperature

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Efficient parallel Multigrid Methods on Manycore Clusters with Double/Single Precision Computing

Efficient Parallel Multigrid Methods on Manycore Clusters wi...

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

作者： Nakajima, Kengo Ogita, Takseshi Kawai, Masatoshi Univ Tokyo Informat Technol Ctr RIKEN Tokyo Japan Tokyo Womans Christian Univ Div Math Sci Tokyo Japan Univ Tokyo Informat Technol Ctr Tokyo Japan

ISBN: (纸本)9781665435772

The parallel multigrid method is expected to play an important role in scientific computing on exa-scale supercomputer systems for solving large-scale linear equations with sparse coefficient matrices. Because solving sparse linear systems is a very memory-bound process, efficient method for storage of coefficient matrices is a crucial issue. In the previous works, authors implemented sliced ELL method to parallel conjugate gradient solvers with multigrid preconditioning (MGCG) for the application on 3D groundwater flow through heterogeneous porous media (pGW3D-FVM), and excellent performance has been obtained on large-scale multicore/manycore clusters. In the present work, authors introduced SELL-C-sigma with double/single precision computing to the MGCG solver, and evaluated the performance of the solver with OpenMP/MPI hybrid parallel programing models on the Oakforest-PACS (OFP) system at JCAHPC using up to 2,048 nodes of Intel Xeon Phi. Because SELL-C-sigma is suitable for wide-SIMD architecture, such as Xeon Phi, improvement of the performance over the sliced ELL was more than 35% for double precision and more than 45% for single precision on OFP. Finally, accuracy verification was conducted based on the method proposed by authors for solving linear equations with sparse coefficient matrices with M-property.

关键词： parallel Computing Iterative Solvers Multigrid Manycore SELL-C-sigma Low/Mixed Precision Accuracy Verification

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Checkpointing vs. Supervision Resilience Approaches for Dynamic Independent Tasks

Checkpointing vs. Supervision Resilience Approaches for Dyna...

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

作者： Posner, Jonas Reitz, Lukas Fohry, Claudia Univ Kassel Res Grp Programming Languages Methodol Kassel Germany

ISBN: (纸本)9781665435772

With the advent of exascale computing, issues such as application irregularity and permanent hardware failure are growing in importance. Irregularity is often addressed by task-based parallel programming coupled with work stealing. At the task level, resilience can be provided by two principal approaches, namely checkpointing and supervision. For both, particular algorithms have been worked out recently. They perform local recovery and continue the program execution on a reduced set of resources. The checkpointing algorithms regularly save task descriptors explicitly, while the supervision algorithms exploit their natural duplication during work stealing and may be coupled with steal tracking to minimize the number of task re-executions. Thus far, the two groups of algorithms have been targeted at different task models: checkpointing algorithms at dynamic independent tasks, and supervision algorithms at nested fork-join programs. This paper transfers the most advanced supervision algorithm to the dynamic independent tasks model, thus enabling a comparison between checkpointing and supervision. Our comparison includes experiments and running time predictions. Results consistently show typical resilience overheads below 1% for both approaches. The overheads are lower for supervision in practically relevant cases, but checkpointing takes over for order millions of processes.

关键词： Fault Tolerance Resilience Work Stealing Task-based parallel Programming Runtime systems

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Dynamic Adaptive Checkpoint Mechanism for Streaming Applications Based on Reinforcement Learning

Dynamic Adaptive Checkpoint Mechanism for Streaming Applicat...

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international Conference on parallel and distributed systems (ICPADS)

作者： Zhan Zhang Tianming Liu Yanjun Shu Siyuan Chen Xian Liu Department of Computer Science and Technology Harbin Institute of Technology Harbin China

For a stream processing system that uses checkpoints as a fault-tolerant method, selecting the appropriate checkpoint period is the key to ensuring the efficient operation of streaming applications. State-of-art stream processing systems currently only support fixed-cycle checkpoints, which is difficult to make a good trade-off between fault-tolerant processing and the cost of failure recovery in dynamically changing streaming application scenarios. Moreover, in a complex distributed streaming application environment, the dynamic environmental indicators (e.g., the values of workloads and failure rates) are not in coincidence with the model assumptions, such as the dynamics of Twitter’s hot events data changing quickly. In this paper, we consider the dynamic changes of environmental indicators and adaptively optimize the processing delay and fault recovery time. Then, we propose a dynamic adjustment method for the checkpoint interval by reinforcement learning, which is named DACM. DACM adaptively optimizes the processing delay and fault recovery time, while avoiding the overall environment modeling of streaming applications. The experiments conducted on the Flink platform show that DACM reduces the processing delay by 10% and the failure recovery time by 37% compared with the existing checkpoint interval optimization models.

关键词： Fault tolerance Adaptation models Costs Q-learning Heuristic algorithms Fault tolerant systems distributed databases

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PFIDT:Pyramid Focal Inverse Distance Transformer for Crowd Localization

PFIDT:Pyramid Focal Inverse Distance Transformer for Crowd L...

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Information Science, parallel and distributed systems (ISPDS), international Conference on

作者： Jin Ye Shaojie Guo Xiameng Li Yilin Liu School of Computer Engineering Jimei University Xiamen China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

Precise individual localization in densely crowded scenes remains a formidable challenge due to the complex interplay of occlusions and variable density patterns. Traditional approaches to crowd localization often employ CNNs to regress density maps, yet these are prone to inaccuracies due to the severe overlaps characteristic of dense populations. Addressing this conundrum, this paper introduces the Pyramid Focal Inverse Distance Transformer (PFIDT), a framework that leverages the multi-scale global receptive fields of Pyramid Vision Transformers to effectively conform to the multi-scale nature of crowds, thereby enhancing the precision of individual localization. The PFIDT incorporates Focal Inverse Distance techniques to adeptly tackle issues of scale variation and dense overlaps, prioritizing local small-scale features within the contextual understanding of the scene. Furthermore, the model introduces an independent Structural Similarity Index (I-SSIM) loss, encouraging the learning of local structural information and better recognition of local maxima. Rigorous testing on standard benchmarks, including the challenging ShanghaiTech dataset, has confirmed the supremacy of our method. The PFIDT has achieved outstanding results on the ShanghaiTech dataset, with Mean Absolute Errors (MAE) of 53.4 and 6.5 and Mean Squared Errors (MSE) of 93.5 and 10.7 for Parts A and B.

关键词： Location awareness Information science Laplace equations distributed databases Transformers Feature extraction Reliability

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