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Refined radial basis function-generated finite difference analysis of non-Newtonian natural convection

PHYSICS OF FLUIDS 2025年第3期37卷

作者： Rot, Miha Kosec, Gregor Jozef Stefan Int Postgrad Sch Jamova Cesta 39 Ljubljana 1000 Slovenia Jozef Stefan Inst Parallel & Distributed Syst Lab Jamova Cesta 39 Ljubljana 1000 Slovenia

In this paper, we present a refined radial basis function-generated finite difference solution for a non-Newtonian fluid in a closed differentially heated cavity. The non-Newtonian behavior is modeled with the Ostwald-de Waele power law and the buoyancy with the Boussinesq approximation. The problem domain is discretized with scattered nodes without any requirement for a topological relation between them. This allows a trivial generalization of the solution procedure to complex irregular three dimensional (3D) domains, which is also demonstrated by solving the problem in a two dimensional (2D) and 3D geometry mimicking a porous filter. The results in 2D are compared with two reference solutions that use the finite volume method in a conjunction with two different stabilization techniques, where we achieved good agreement with the reference data. The refinement is implemented on top of a dedicated meshless node positioning algorithm using piecewise linear node density function that ensures sufficient node density in the center of the domain while maximizing the node density in a boundary layer where the most intense dynamic is expected. The results show that with a refined approach, more than five times fewer nodes are required to obtain the results with the same accuracy compared to the regular discretization. The paper also discusses the convergence with refined discretization for different scenarios for up to 2x10(5) nodes, the impact of method parameters, the behavior of the flow in the boundary layer, the behavior of the viscosity, and the geometric flexibility of the proposed solution procedure.

关键词： Topology

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distributed parallel query processing on networks of workstations 8th

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8th International Conference on High Performance Computing and Networking, HPCNEurope 2000

作者： Soleimany, Cyrus Dandamudi, Sivarama P. IBM Canada Toronto Canada Centre for Parallel and Distributed Computing School of Computer Science Carleton University Ottawa Canada

ISBN: (纸本)9783540675532

Network of workstations (NOW) is an attractive alternative to parallel database systems. Here we present a distributed architecture for parallel query processing on networks of workstations. We describe a comprehensive design that synthesizes knowledge from parallel and distributed systems area on scheduling, load sharing and loop scheduling. To study the performance of this architecture, we have implemented the distributed architecture using PVM on a Pentium-based NOW system. Our results show that the distributed architecture achieves good speedups and scaleups. © Springer-Verlag Berlin Heidelberg 2000.

关键词： Query processing

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Tree-Structured parallel Regeneration for Multiple Data Losses in distributed Storage Systems Based on Erasure Codes

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China Communications 2013年第4期10卷 113-125页

作者：孙伟东王意洁裴晓强 National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology

To reduce the time required to complete the regeneration process of erasure codes, we propose a Tree-structured parallel Regeneration (TPR) scheme for multiple data losses in distributed storage systems. Under the scheme, two algorithms are proposed for the construction of multiple regeneration trees, namely the edge-disjoint algorithm and edge-sharing algorithm. The edge-disjoint algorithm constructs multiple independent trees, and is simple and appropriate for environments where newcomers and their providers are distributed over a large area and have few intersections. The edge-sharing algorithm constructs multiple trees that compete to utilize the bandwidth, and make a better utilization of the bandwidth, although it needs to measure the available band-width and deal with the bandwidth changes; it is therefore difficult to implement in practical systems. The parallel regeneration for multiple data losses of TPR primarily includes two optimizations: firstly, transferring the data through the bandwidth optimized-paths in a pipe-line manner; secondly, executing data regeneration over multiple trees in parallel. To evaluate the proposal, we implement an event-based simulator and make a detailed comparison with some popular regeneration methods. The quantitative comparison results show that the use of TPR employing either the edge-disjoint algorithm or edge-sharing algorithm reduces the regeneration time significantly.

关键词： distributed storage system erasure code replication regeneration tree

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SmartKV: A cost-effective and low-latency geo-distributed key-value store for the computing continuum

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2025年 171卷

作者： Poveda, Juan Aznar Ebner, Maximilian Franz Fahringer, Thomas Samani, Zahra Najafabadi Etheredge, Marlon Pedratscher, Stefan Saurabh, Nishant Univ Innsbruck Distributed & Parallel Syst Grp Tech Str 21A A-6020 Innsbruck Austria Univ Utrecht Dept Informat & Comp Sci Princetonpl 5 NL-3584 Utrecht Netherlands

Many data-intensive and distributed applications rely on low-latency and scalable key-value storage systems across the Computing Continuum. Key-value storage systems typically use consistent hashing or hash slotsharding mechanisms to distribute data across storage nodes, which ensures load balancing but often leads to sub-optimal response times and monetary costs, particularly in geo-distributed systems where nodes might have different unit prices and be widely dispersed. In this paper, we propose SmartKV, a cost-efficient geodistributed key-value store that optimizes data placement dynamically, abstracting the intricacies of data organization, transfer, access, and processing. SmartKV integrates a decentralized data placement algorithm that optimizes the replication factor and selects suitable locations for key-value pairs and replicas, balancing cost and access latency while keeping optimization overhead low. We employ a realistic cost model based on public and private Cloud and Edge providers that consider data transfer, request, and storage costs. In addition to conventional key-value pairs, SmartKV supports active key-value pairs, which enable the definition of custom data types and the execution of user-defined functions directly on the storage side. This contributes to reducing data transfer costs and round-trip times. We thoroughly evaluate SmartKV across different regions of the Chameleon testbed using several realistic workloads. Results show that the utilized decentralized data placement strategy allows SmartKV to reduce round trip times between 9 and 84% while reducing costs up to 4.84x under different client workloads and consistency models compared to state-of-the-art data placement strategies.

关键词： distributed data distributed key-value storage distributed data storage Decentralized Data placement Data migration Replica management Computing continuum

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Knowledge-based job scheduling for HPC

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第3期28卷 1-11页

作者： Diel, Guilherme Kraus, Ana Eloina Nascimento Koslovski, Guilherme Piegas Santa Catarina State Univ Dept Comp Sci Lab Parallel & Distributed Proc Joinville SC Brazil Santa Catarina State Univ Grad Program Appl Comp Joinville SC Brazil

Scheduling jobs for executing atop large-scale High-Performance Computing (HPC) infrastructures is a fundamental administrative challenge essential to maximize the productivity indicators of such complex Data Centers (DCs). Several policies were proposed by specialized literature, considering specific scenarios and applications, or proposing DC-tailored heuristics. However, the definition of the appropriate policy depends directly on the applications and infrastructure load, that is, indicators that vary over time. Given this context, this work advances the field on applying Machine Learning (ML) techniques to improve the existing scheduling policies. Specifically, we propose Knowledge-based Job Scheduler (KJS), which uses regression techniques to characterize the performance indicators of existing schedulers, composing a knowledge database, and instead of proposing a one-size-fits-all scheduling function, KJS consolidates the information and uses data classification for defining the jobs' execution order. The simulation campaign demonstrates that KJS can adapt to different workloads when compared to the individual use of existing policies. Essentially, KJS combines polynomial data regression and classification for improving the performance indicators of HPC DC, in both the users' and the administrators' perspectives.

关键词： Scheduling Supervised learning Linear regression High performance computing

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ER-SFM: Efficient and Robust Cluster-Based Structure from Motion 7th

ER-SFM: Efficient and Robust Cluster-Based Structure from Mo...

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7th Chinese Conference on Pattern Recognition and Computer Vision

作者： Ye, Zongxin Li, Wenyu Liu, Sidun Qiao, Peng Dou, Yong Natl Univ Def Technol Coll Comp Natl Key Lab Parallel & Distributed Comp Changsha Peoples R China

ISBN: (纸本)9789819785070;9789819785087

Structure from Motion (SfM) is a fundamental computer vision technique that recovers scene structure and camera motion from multi-view images. When facing large-scale scenarios, cluster-based methods are commonly employed to improve reconstruction efficiency. However, these methods currently face challenges regarding their limited robustness, redundant computation, and drift. To address these issues, we propose a unified pipeline called ER-SfM, which enhances the three key aspects of cluster-based SfM: image clustering, local reconstruction, and merging. In terms of image clustering, we propose a three-stage image clustering method to ensure adequate and reliable connections between clusters. In the local reconstruction stage, we expedite the reconstruction process by eliminating duplicate point cloud computation. In the final merging stage, we introduce a global merging algorithm without scale ambiguity to address the drift problem. Extensive experimental results demonstrate the superior performance of our method in terms of both robustness and efficiency compared to state-of-the-art methods.

关键词： parallel structure from motion 3D Reconstruction Image clustering Global averaging

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HTLL: Latency-Aware Scalable Blocking Mutex

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IEEE TRANSACTIONS ON parallel and distributed SYSTEMS 2025年第3期36卷 471-486页

作者： Yu, Ziqu Gu, Jinyu Wu, Zijian Liu, Nian Guo, Jian Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst IPADS Shanghai 200240 Peoples R China Guangdong OPPO Mobile Telecommun Co Ltd Dongguan 523860 Peoples R China

This article finds that existing mutex locks suffer from throughput collapses or latency collapses, or both, in the oversubscribed scenarios where applications create more threads than the CPU core number, e.g., database applications like mysql use per thread per connection. We make an in-depth performance analysis on existing locks and then identify three design rules for the lock primitive to achieve scalable performance in oversubscribed scenarios. First, to achieve ideal throughput, the lock design should keep adequate number of active competitors. Second, the active competitors should be arranged carefully to avoid the lock-holder preemption problem. Third, to meet latency requirements, the lock design should track the latency of each competitor and reorder the competitors according to the latency requirement. We propose a new lock library called HTLL that satisfies these rules and achieves both high throughput and low latency even when the cores are oversubscribed. HTLL only requires minimal human effort (e.g., add several lines of code) to annotate the latency requirement. Evaluation results show that HTLL achieves scalable performance in the oversubscribed scenarios. Specifically, for the real-world database, LMDB, HTLL can reduce the tail latency by up to 97% with only an average 5% degradation in throughput, compared with state-of-the-art alternatives such as Malthusian, CST, and Mutexee locks;In comparison to the widely used pthread_mutex_lock, it can increase the throughput by up to 22% and decrease the latency by up to 80%. Meanwhile, for the under-subscribed scenarios, it also shows comparable performance than state-of-the-art blocking locks.

关键词： Throughput Tail Low latency communication Spinning Protocols Libraries Codes Scalability Degradation Accuracy Synchronization primitives lock over-subscription latency-aware

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Efficient deep neural network training via decreasing precision with layer capacity

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FRONTIERS OF COMPUTER SCIENCE 2025年第10期19卷 1-17页

作者： Shen, Ao Lai, Zhiquan Sun, Tao Li, Shengwei Ge, Keshi Liu, Weijie Li, Dongsheng Natl Univ Def Technol Natl Key Lab Parallel & Distributed Comp Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China

Low-precision training has emerged as a practical approach, saving the cost of time, memory, and energy during deep neural networks (DNNs) training. Typically, the use of lower precision introduces quantization errors that need to be minimized to maintain model performance, often neglecting to consider the potential benefits of reducing training precision. This paper rethinks low-precision training, highlighting the potential benefits of lowering precision: (1) low precision can serve as a form of regularization in DNN training by constraining excessive variance in the model;(2) layer-wise low precision can be seen as an alternative dimension of sparsity, orthogonal to pruning, contributing to improved generalization in DNNs. Based on these analyses, we propose a simple yet powerful technique-DPC (Decreasing Precision with layer Capacity), which directly assigns different bit-widths to model layers, without the need for an exhaustive analysis of the training process or any delicate low-precision criteria. Thorough extensive experiments on five datasets and fourteen models across various applications consistently demonstrate the effectiveness of the proposed DPC technique in saving computational cost (-16.21%--44.37%) while achieving comparable or even superior accuracy (up to +0.68%, +0.21% on average). Furthermore, we offer feature embedding visualizations and conduct further analysis with experiments to investigate the underlying mechanisms behind DPC's effectiveness, enhancing our understanding of low-precision training. Our source code will be released upon paper acceptance.

关键词： low precision efficient training generalization regularization bit-width assignment

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End-To-End High-Quality Transformer Object Detection Model Applied to Human Head Detection 7th

End-To-End High-Quality Transformer Object Detection Model A...

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7th Chinese Conference on Pattern Recognition and Computer Vision

作者： Zhou, Zhen Li, Rongchun Qiao, Peng Jiang, Jingfei Natl Univ Def Technol Sch Comp Natl Key Lab Parallel & Distributed Comp Changsha 410073 Peoples R China

ISBN: (纸本)9789819788576;9789819788583

Head detection is a challenging and widely applied object detection task. Although previous CNN-based head detectors have made good progress, the inherent locality of CNN restricts the extraction of global contextual information, which leads to low precision and recall rates in head detection. In this article, we propose an end-to-end high-quality head detector based on Transformer, which effectively models the contextual relationships between heads, other objects and the background. To extract and generate discriminative feature maps suitable for detecting small head targets, we incorporate specific CNN-based auxiliary detector heads for joint training. The GIoU-aware classification loss function is improved to generate bounding boxes with high localization quality and high classification confidence, and a feature fusion module is introduced to enhance the feature representation capabilities of the model. We conduct experiments on COCO 2017 dataset and Brainwash head dataset, and the results demonstrate that our method outperforms in both COCO generalized object detection and Brainwash head detection tasks compared to previous CNN-based detectors as well as other current mainstream Transformer-based object detection models.

关键词： Head detection Vision transformer Auxiliary detector heads GIoU-aware Feature fusion

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Skipping Unused Events to Speed Up Rollback-Recovery in distributed Data-parallel CEP 5

Skipping Unused Events to Speed Up Rollback-Recovery in Dist...

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11th IEEE/ACM International Conference on Utility and Cloud Computing (UCC-Companion) / 5th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)

作者： Lima, Guilherme F. Endler, Markus Slo, Ahmad Bhowmik, Sukanya Rothermel, Kurt Pontificial Catholic Univ Rio De Janeiro Dept Informat Rio De Janeiro Brazil Univ Stuttgart Inst Parallel & Distributed Syst Stuttgart Germany

ISBN: (纸本)9781538655023

We propose two extensions for a state-of-the-art method of rollback-recovery in distributed CEP (complex event processing). In CEP, an operator network is used to search for patterns in events streams. Sometimes these operators fail and lose their state. Rollback-recovery is a method for dealing with such state losses. The type of rollback-recovery we consider is upstream backup, where the state of a failed operator is recovered by replaying to it the input events that led it to that state. These events are kept in upstream operators' memory buffers, which are trimmed continuously as the downstream operator progresses. The first extension we propose saves memory and speeds up recovery by avoiding to store and retransmit unnecessary events. The second extension makes the base method of upstream backup compatible with data-parallel CEP, allowing that the windows into which operators partition their input be processed in parallel. We evaluated the proposed extensions through experiments that showed a significant reduction in memory usage and recovery time at the expense of a negligible processing overhead during normal operation.

关键词： complex event processing fault-tolerance reliability recovery

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