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PyMW - A Python module for desktop grid and volunteer computing

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PyMW - A Python module for desktop grid and volunteer comput...

International Symposium on parallel and distributed Processing (IPDPS)

作者： Eric M. Heien Yusuke Takata Kenichi Hagihara Adam Kornafeld Graduate School of Information Science and Technology Osaka University Toyonaka Osaka Japan Laboratory of Parallel and Distributed Systems Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

We describe a general purpose master-worker parallel computation Python module called PyMW. PyMW is intended to support rapid development, testing and deployment of large scale master-worker style computations on a desktop grid or volunteer computing environment. This module targets non-expert computer users by hiding complicated task submission and result retrieval procedures behind a simple interface. PyMW also provides a unified interface to multiple computing environments with easy extension to support additional environments. In this paper, we describe the internal structure and external interface to the PyMW module and its support for the Condor computing environment and the Berkeley Open Infrastructure for Network Computing (BOINC) platform. We demonstrate the effectiveness and scalability of PyMW by performing master-worker style computations on a desktop grid using Condor and a BOINC volunteer computing project.

关键词： Grid computing Concurrent computing Computer interfaces Large-scale systems Virtual colonoscopy distributed computing Computer networks Software packages Packaging Information science

Polyjuice: High-performance transactions via learned concurrency control

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arXiv 2021年

作者： Wang, Jiachen Ding, Ding Wang, Huan Christensen, Conrad Wang, Zhaoguo Chen, Haibo Li, Jinyang Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China Shanghai AI Laboratory China Engineering Research Center for Domain-specific Operating Systems Ministry of Education China Department of Computer Science New York University

Concurrency control algorithms are key determinants of the performance of in-memory databases. Existing algorithms are designed to work well for certain workloads. For example, optimistic concurrency control (OCC) is better than twophase- locking (2PL) under low contention, while the converse is true under high contention. To adapt to different workloads, prior works mix or switch between a few known algorithms using manual insights or simple heuristics. We propose a learning-based framework that instead explicitly optimizes concurrency control via offline training to maximize performance. Instead of choosing among a small number of known algorithms, our approach searches in a "policy space" of fine-grained actions, resulting in novel algorithms that can outperform existing algorithms by specializing to a given workload. We build Polyjuice based on our learning framework and evaluate it against several existing algorithms. Under different configurations of TPC-C and TPC-E, Polyjuice can achieve throughput numbers higher than the best of existing algorithms by 15% to 56%. Copyright © 2021, The Authors. All rights reserved.

关键词： Concurrency control

Meshless method stencil evaluation with machine learning

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arXiv 2022年

作者： Rot, Miha Rashkovska, Aleksandra Jozef Stefan International Postgraduate School Jamova cesta 39 Ljubljana1000 Slovenia "Jožef Stefan" Institute Parallel and Distributed Systems Laboratory Jamova cesta 39 Ljubljana1000 Slovenia

Meshless methods are an active and modern branch of numerical analysis with many intriguing benefits. One of the main open research questions related to local meshless methods is how to select the best possible stencil - a collection of neighbouring nodes - to base the calculation on. In this paper, we describe the procedure for generating a labelled stencil dataset and use a variation of pointNet - a deep learning network based on point clouds - to create a classifier for the quality of the stencil. We exploit features of pointNet to implement a model that can be used to classify differently sized stencils and compare it against models dedicated to a single stencil size. The model is particularly good at detecting the best and the worst stencils with a respectable area under the curve (AUC) metric of around 0.90. There is much potential for further improvement and direct application in the meshless domain. © 2022, CC BY.

关键词： Classification (of information)

Discretized boundary surface reconstruction

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arXiv 2021年

作者： Jančič, Mitja Cvrtila, Viktor Kosec, Gregor Jožef Stefan International Postgraduate School Ljubljana Slovenia Jožef Stefan Institute Parallel and Distributed Systems Laboratory Ljubljana Slovenia Faculty of Mathematics and Physics University of Ljubljana Ljubljana Slovenia

Domain discretization is an essential part of the solution procedure in numerical simulations. Meshless methods simplify the domain discretization to positioning of nodes in the interior and on the boundary of the domain. However, generally speaking, the shape of the boundary is often undefined and thus needs to be constructed before it can be discretized with a desired internodal spacing. Domain shape construction is far from trivial and is the main challenge of this paper. We tackle the simulation of moving boundary problems where the lack of domain shape information can introduce difficulties. We present a solution for 2D surface reconstruction from discretization points using cubic splines and thus providing a surface description anywhere in the domain. We also demonstrate the presented algorithm in a simulation of phase-change-like problem. Copyright © 2021, The Authors. All rights reserved.

关键词： Surface reconstruction

A Connectivity-Enhanced Multi-Task Learning based on Anatomical Priors for 3D Class-Balanced Pulmonary Airway Segmentation

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A Connectivity-Enhanced Multi-Task Learning based on Anatomi...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yan Jia Yong Dou Peng Qiao Yuqing Cheng Kele Xu Zhouyu He National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Accurate and efficient airway segmentation is essential for evaluating pulmonary diseases, aiding diagnosis, reducing the preoperative burden of airway identification, and minimizing patient discomfort during prolonged surgeries. However, current pulmonary airway reconstruction techniques are hindered by two major challenges: difficulty in accurately reconstructing fine airway branches due to the tendency to overlook small targets, and insufficient structural connectivity leading to frequent branch discontinuities within the airway tree. These limitations directly affect the clinical applicability of reconstructed airways. To overcome these challenges, a novel 3D pulmonary airway segmentation multi-task framework is proposed, designed to enhance the performance of existing backbone models. This approach integrates Anatomical Prior-Based Multi-Task Learning (AP-MTL) through the use of Gaussian-constructed connectivity-enhanced isosurfaces, significantly improving the network’s ability to maintain airway continuity. Additionally, a Class-Balanced CT Density Distribution Reconstruction mechanism (DDR-CB) is introduced, further refining the model’s capability to detect and segment fine airway branches. As a result of these enhancements, the model demonstrates a 11.5% average improvement in segmentation accuracy and connectivity compared to the baseline. The source code is publicly accessible at https://***/inexhaustible419/APMTLAirwaySegment.

关键词： Image segmentation Three-dimensional displays Accuracy Lungs Atmospheric modeling Supervised learning Surgery Multitasking Image reconstruction Biomedical imaging

Discretized Boundary Surface Reconstruction

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Discretized Boundary Surface Reconstruction

Proceedings of the International Convention MIPRO

作者： Mitja Jančič Viktor Cvrtila Gregor Kosec “Jožef Stefan” International Postgraduate School Ljubljana Slovenia “Jožef Stefan” Institute Parallel and Distributed Systems Laboratory Ljubljana Slovenia Faculty of Mathematics and Physics University of Ljubljana Ljubljana Slovenia

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Deep Isolation Forest for Anomaly Detection

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arXiv 2022年

作者： Xu, Hongzuo Pang, Guansong Wang, Yijie Wang, Yongjun The College of Computer National University of Defense Technology Changsha410073 China The Science and Technology on Parallel and Distributed Processing Laboratory China The School of Computing and Information Systems Singapore Management University 178902 Singapore

Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector in recent years due to its general effectiveness across different benchmarks and strong scalability. Nevertheless, its linear axis-parallel isolation method often leads to (i) failure in detecting hard anomalies that are difficult to isolate in high-dimensional/non-linear-separable data space, and (ii) notorious algorithmic bias that assigns unexpectedly lower anomaly scores to artefact regions. These issues contribute to high false negative errors. Several iForest extensions are introduced, but they essentially still employ shallow, linear data partition, restricting their power in isolating true anomalies. Therefore, this paper proposes deep isolation forest. We introduce a new representation scheme that utilises casually initialised neural networks to map original data into random representation ensembles, where random axis-parallel cuts are subsequently applied to perform the data partition. This representation scheme facilitates high freedom of the partition in the original data space (equivalent to non-linear partition on subspaces of varying sizes), encouraging a unique synergy between random representations and random partition-based isolation. Extensive experiments show that our model achieves significant improvement over state-of-the-art isolation-based methods and deep detectors on tabular, graph and time series datasets;our model also inherits desired scalability from iForest. © 2022, CC BY.

关键词： Anomaly detection

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

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arXiv 2022年

作者： Rot, Miha Kosec, Gregor Jozef Stefan International Postgraduate School Jamova cesta 39 Ljubljana1000 Slovenia Jožef Stefan Institute Parallel and Distributed Systems Laboratory Jamova cesta 39 Ljubljana1000 Slovenia

In this paper we present a refined Radial Basis Function-generated Finite Difference (RBF-FD) solution for a non-Newtonian fluid in a closed differentially heated cavity. The non-Newtonian behaviour is modelled with the Ostwald-de Waele power law and the buoyancy with the Boussinesq approximation. The problem domain is discretised with scattered nodes without any requirement for a topological relation between them. This allows a trivial generalisation 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 stabilisation 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 centre of the domain while maximising the node density in a boundary layer where the most intense dynamic is expected. The results show that with a refined approach, more than 5 times fewer nodes are required to obtain the results with the same accuracy compared to the regular discretisation. The paper also discusses the convergence with refined discretisation for different scenarios for up to 2·105 nodes, the impact of method parametres, the behaviour of the flow in the boundary layer, the behaviour of the viscosity and the geometric flexibility of the proposed solution procedure. © 2022, CC BY.

关键词： Navier Stokes equations

GPU accelerated RBF-FD solution of Poisson's equation

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arXiv 2021年

作者： Jančič, Mitja Slak, Jure Kosec, Gregor Jožef Stefan Institute Parallel and Distributed Systems Laboratory Ljubljana Slovenia Jožef Stefan International Postgraduate School Ljubljana Slovenia Faculty of Mathematics and Physics University of Ljubljana Ljubljana Slovenia

The Radial Basis Function-generated finite differences became a popular variant of local meshless strong form methods due to its robustness regarding the position of nodes and its controllable order of accuracy. In this paper, we present a GPU accelerated numerical solution of Poisson's equation on scattered nodes in 2D for orders from 2 up to 6. We specifically study the effect of using different orders on GPU acceleration efficiency. © 2021, CC BY-SA.

关键词： Poisson equation