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Leveraging the Compute Power of Two HPC systems for higher-Dimensional Grid-Based Simulations with the Widely-distributed Sparse Grid Combination Technique 23

Leveraging the Compute Power of Two HPC Systems for Higher-D...

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2023 International Conference for high performance Computing, Networking, Storage and Analysis, SC 2023

作者： Pollinger, Theresa Van Craen, Alexander Niethammer, Christoph Breyer, Marcel Pflüger, Dirk Institute for Parallel and Distributed Systems Universität Stuttgart Stuttgart Germany High Performance Computing Center Universität Stuttgart Stuttgart Germany

ISBN: (纸本)9798400701092

Grid-based simulations of hot fusion plasmas are often severely limited by computational and memory resources;the grids live in four- to six-dimensional space and thus suffer the curse of dimensionality. However, high resolutions are required to fully capture the physics of interest. The sparse grid combination technique is a multi-scale method in which many anisotropically coarse resolved grids are used to approximate a fine-scale solution - -and it alleviates the curse of *** paper presents the core concepts of the widely-distributed combination technique, which allows us to use the compute power and memory of more than one HPC system for the same simulation. We apply the sparse grid combination technique to a six-dimensional advection problem serving as a proxy for plasma simulations. The full-grid solution approximated by the combination technique would contain ≈ 5 ZB if computed with conventional grid-based methods. Even the combination technique simulation operates on ≈ 1 × 1011 double-precision degrees of freedom, or 988 GB, plus the supporting sparse grid data structures. We propose a new approach to divide the compute load between the two HPC systems, requiring only 76 GB to be exchanged. Based on this, we have realized the first synchronous combination technique simulation using two HPC systems, in our case the two German Tier-0 supercomputers HAWK and SuperMUC-NG. On two systems, the simulation can be computed at an average overhead of ≈ 35 % (108 s per combination step) for file I/O and transfer. The presented concepts apply to any pair of HPC systems if high-speed data transfer is possible. © 2023 ACM.

关键词： Supercomputers

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A New Method for Evaluating Core Developers In Open Source Software 13

A New Method for Evaluating Core Developers In Open Source S...

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13th IEEE International Conference on Software Engineering and Service Science, ICSESS 2022

作者： Song, Yulin Wang, Tao Shen, Yang Chang, Junsheng National University of Defense Technology National Laboratory for Parallel and Distributed Processing Hunan Province Changsha China National University of Defense Technology Institute for Quantum Information State Key Laboratory of High Performance Computing Hunan Province Changsha China

ISBN: (数字)9781665410328

ISBN: (纸本)9781665410311

The high-quality development of Open Source Software(OSS) benefits from the contributions of developers. An important research question is how to truly assess the extent of contribution of all developers in open source software and effectively identify the core developers to avoid their loss. In this paper, we use the undirected weighted network model to build a Developer Collaboration Network(DCN) based on developers and their contributions to the GitHub community's open source software Rails, analyse the characteristics of developer partnerships through the Developer Collaboration Network, and propose a Developer Personal Impact Factor(DPIF) to evaluate the importance of developer, select the top 10 core developers in Rails, focus on their participation in the open source software process, and predicting the changes of their contributions. The experimental results show that core developers with extensive partnerships are more likely to associate with developers with fewer partnerships. The average degree of developer contribution does not increase with more developers. Developer partnerships are characterized by close localization and loose overall relationships. The Developer Personal Impact Factor identifies the core developers in open source software well. The Developer Personal Impact Factor gives a ranking in which the core team members rank in the top 1.17% on average. © 2022 IEEE.

关键词： Open source software

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Simultaneously Learning Syntactic Dependency and Semantics Reasonability for Relation Extraction

Simultaneously Learning Syntactic Dependency and Semantics R...

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International Conference on Image, Vision and Intelligent systems, ICIVIS 2021

作者： Wang, Xin Yin, Nan Zhang, Xiang Bai, Xinyi Luo, Zhigang College of Computer National University of Defense Technology Changsha China Institute for Quantum and State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9789811669620

Relation extraction as an important Natural Language Processing (NLP) task is to identify relations between named entities in text. Recently, graph convolutional networks over dependency trees have been widely used to capture syntactic features and achieved attractive performance. However, most existing dependency-based approaches ignore the positive influence of the words outside the dependency trees, sometimes conveying rich and useful information on relation extraction. In this paper, we propose a novel model, Entity-aware Self-attention Contextualized GCN (ESC-GCN), which efficiently incorporates syntactic structure of input sentences and semantic context of sequences. To be specific, relative position self-attention obtains the overall semantic pairwise correlation related to word position, and contextualized graph convolutional networks capture rich intra-sentence dependencies between words by adequately pruning operations. In this way, our proposed model not only reduces the noisy impact from dependency trees but also obtains easily-ignored entity-related semantic representation. Extensive experiments demonstrate that our model achieves encouraging performance. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Towards Data Gravity and Compliance Aware distributed Deep Learning on Hybrid Clouds

Towards Data Gravity and Compliance Aware Distributed Deep L...

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IEEE International Conference on high performance Computing Workshops (HiPCW)

作者： Avinash Maurya Jaiaid Mobin M. Mustafa Rafique High Performance Distributed Systems Laboratory (HPDSL) Rochester Institute of Technology New York USA

To store large volumes of data concurrently from a diverse set of sources, data stores such as data silos, lakes, and warehouses, have been widely embraced by various organizations. Thanks to data fabric architectures, such scattered data (both structurally and geographically), can be accessed transparently at scale while adhering to various administrative regulations (e.g. governance, privacy, compliance, etc.). However, modern workload schedulers and distributed deep learning (DDL) runtimes are oblivious to the uneven data distribution across different storage services and compliance regulations, leading to sub-optimal resource utilization and training completion times. Al-though state-of-art workflow schedulers such as Apache Hadoop Yarn, Horovod, etc. exploit data locality, they require application developers to explicitly map data and resources available across various cloud services during job submission. These approaches are redundant and counterproductive for next-generation data fabric architectures that feature automated transparency and compliance abstractions for accessing disparate data sources with uneven data distribution. To this end, we propose an algorithm based on greedy programming that leverages the meta-data catalog of data fabric to efficiently determine training schedules based on data gravity, compliance, and resource availability. Our simulations based on synthetic data and resource distribution profiles demonstrate significant improvements in execution times and resource utilization compared to traditional DDL scheduling approaches in hybrid multi-cloud environments.

关键词： Training Deep learning distributed databases Computer architecture Organizations Fabrics Regulation

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DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for highly-Effective Drug-Protein Interaction Representation

DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Qiu, Yanlong Wang, Siqi Yang, Xi Qiu, Xinyuan Wu, Chengkun Cui, Yingbo Yang, Canqun National University of Defense Technology Institute for Quantum Information State Key Laboratory of High-Performance Computing College of Computer Science Hunan Changsha410073 China National Supercomputer Center in Tianjin Tianjin300457 China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing College of Computer Science Hunan Changsha410073 China National University of Defense Technology Department of Biology and Chemistry College of Science Hunan Changsha410073 China

ISBN: (纸本)9798350337488

With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support the need of information retrieval from researchers and clinicians. To mine knowledge from graph databases, most previous methods view a triple in a graph (see Fig. 1) as the basic processing unit and embed the triplet element (i.e. drugs/chemicals, proteins/genes and their interaction) as separated embedding matrices, which cannot capture the semantic correlation among triple elements. To remedy the loss of semantic correlation caused by disjoint embeddings, we propose a novel approach to learn triple embeddings by combining entities and interactions into a unified representation. Furthermore, traditional methods usually learn triple embeddings from scratch, which cannot take advantage of the rich domain knowledge embedded in pre-trained models, and is also another significant reason for the fact that they cannot distinguish the differences implied by the same entity in the multi-interaction triples. In this paper, we propose a novel fine-tuning based approach to learn better triple embeddings by creating weakly supervised signals from pre-trained knowledge graph embeddings. The method automatically samples triples from knowledge graphs and estimates their pairwise similarity from pre-trained embedding models. The triples are then fed pairwise into a Siamese-like neural architecture, where the triple representation is fine-tuned in the manner bootstrapped by triple similarity scores. Finally, we demonstrate that triple embeddings learned with our method can be readily applied to several downstream applications (e.g. triple classification and triple clustering). We evaluated the proposed method on two open-source drug-protein knowledge graphs constructed from PubMed abstracts, as provided by BioCreative. Our method achieves consistent improvement in both t

关键词： Drug-Protein Interaction Knowledge Graph Embedding Triple Embedding Weakly Supervised Learning

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Atomic-action-based Contrastive Network for Weakly Supervised Temporal Language Grounding

Atomic-action-based Contrastive Network for Weakly Supervise...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Hongzhou Wu Yifan Lyu Xingyu Shen Xuechen Zhao Mengzhu Wang Xiang Zhang Zhigang Luo Parallel and Distributed Processing Lab National University of Defense Technology Changsha China Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China School of Computer National University of Defense Technology Changsha China Institute for Quantum Information and the State Key Lab of High Performance Computing National University of Defense Technology Changsha China

As one knows, an event often consists of several actions while each action is atomic. Inspired by this insight, we propose a novel framework named Atomic-action-based Contrastive Network model (ACN) for weakly supervised temporal language grounding task to localize the query-related event moment in an untrimmed video, without access to any temporal annotations. Specifically, ACN first determines the accurate moment boundary of each action in a query-agnostic way. This can adequately exploit homogeneous visual cues while impeding the heterogeneity of the query from hurting the atomicity of visual action, i.e., action boundary. To effectively localize the query-related event, we seek the discriminative words in the given query, and explore a composite-grained contrastive module to retrieve those corresponding atomic actions in the common latent space across modalities. This boosts feature discrimination of visual event segment to remove irrelevant action video segments. Experiments on two popular datasets show the efficacy of our model.

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A Cython Bound Tracklet- Tracklet Correlation for Resident Space Objects

A Cython Bound Tracklet- Tracklet Correlation for Resident S...

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IEEE Conference on Aerospace

作者： Kathrin Rack Simon Schmitz Martin Stoffers Benjamin Hofmann Michael Meinel Hauke Fiedler Thomas Schildknecht Department High-Performance Computing Institute for Software Technology German Aerospace Center Linder Hohe Koln Germany Department Space Flight Technology Institute for Space Operations and Astronaut Training German Aerospace Center Weßling Germany Department Intelligent and Distributed Systems Institute for Software Technology German Aerospace Center Berlin Germany Astronomical Institute University of Bern Bern Switzerland

Almost all humankind depends on satellite technology orbiting the Earth. However, space operations are increasingly endangered by the rapidly growing number of orbiting objects and debris. To avoid collisions with debris particles and to keep the space infrastructure alive, it is mandatory to monitor cooperative and uncooperative resident space objects. For this purpose, we are developing a software system called Backbone Catalogue of Relational Debris Information (BACARDI). It provides a database with orbit information of resident space objects and related products like ephemerides, which enables us to derive close approaches and provide collision warnings. The challenge is to detect, identify and track new resident space objects that were recently launched, created by fragmentations and collisions or recently observed due to improved sensor tech-nologies. Typically, passive optical telescopes observe objects for a relatively short time. A short time series of observation is called a tracklet. To derive the full orbit of an object, at least a pair of tracklets is needed. In case the tracklets cannot be associated to an already known object, it should therefore be identified which tracklets belong to the same object. The algorithm performing this task is called the Tracklet- Tracklet Correlation (TTC). Our aim is to integrate the TTC into BACARDI. Therefore, we have to consider that the individual algorithm meets different requirements than the overall software. On the one hand, BAC- ARDI is a software system that needs to be easily maintainable, extendable and usable and is thus written in Python. On the other hand, the TTC has to be computationally fast, which is why it is written in C++. To fulfil these opposing requirements, we present a successful linkage of a fast C++ based TTC to a Python-based BACARDI. To perform this task, we used the pro-gramming language Cython. The Cython syntax is a superset of the Python syntax and provides additional rules to address

关键词： Space vehicles Codes Correlation Software algorithms C++ languages Object recognition Task analysis

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Theoretically Guaranteed Online Workload Dispatching for Deadline-Aware Multi-Server Jobs

arXiv

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arXiv 2021年

作者： Zhao, Hailiang Deng, Shuiguang Yin, Jianwei Dustdar, Schahram Zomaya, Albert Y. School of Computer Science and Technology Wuhan University of Technology Wuhan China The Massachusetts Institute of Technology United States China The Distributed Systems Group The Tu Wien Austria The Centre for Distributed and High-Performance Computing The University of Sydney Australia

Multi-server jobs are imperative in modern computing clusters. A multi-server job has multiple task components and each of the task components is responsible for processing a specific size of workloads. Efficient online workload dispatching is crucial but challenging to co-located heterogeneous multi-server jobs. The dispatching policy should decide (i) where to launch each task component instance of the arrived jobs and (ii) the size of workloads that each task component processes. Existing policies are explicit and effective when facing service locality and resource contention in both offline and online settings. However, when adding the deadline-aware constraint, the theoretical superiority of these policies could not be guaranteed. To fill the theoretical gap, in this paper, we design an α-competitive online workload dispatching policy for deadline-aware multi-server jobs based on the spatio-temporal resource mesh model. We formulate the problem as a social welfare maximization program and solve it online with several well designed pseudo functions. The social welfare is formulated as the sum of the utilities of jobs and the utility of the computing cluster. The proposed policy is rigorously proved to be α-competitive for some α ≥ 2. We also validate the theoretical superiority of it with simulations and the results show that it distinctly outperforms two handcrafted baseline policies on the social welfare. © 2021, CC BY-NC-ND.

关键词： Cluster computing

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DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for highly-Effective Drug-Protein Interaction Representation

DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yanlong Qiu Siqi Wang Xi Yang Xinyuan Qiu Chengkun Wu Yingbo Cui Canqun Yang Institute for Quantum Information & State Key Laboratory of High-Performance Computing College of Computer Science National University of Defense Technology Changsha P. R. China National Supercomputer Center in Tianjin Tianjin P. R. China National Key Laboratory of Parallel and Distributed Computing College of Computer Science National University of Defense Technology Changsha P. R. China Department of Biology and Chemistry College of Science National University of Defense Technology Changsha P. R. China

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A Complexity-Reduced Block-Selective Algebraic Multigrid Method for Implicitly Coupled Velocity-Pressure System on high performance Computers

SSRN

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SSRN 2023年

作者： Liang, Yuechao Guo, Xiao-Wei Li, Chao Yuan, Fan Song, Min Zhang, Qingyang Chen, Xinhai Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China School of Mathematics and Computational Sciences Xiangtan University Xiangtan411105 China College of Computer Science and Technology National University of Defense Technology Changsha410073 China

The implicitly coupled pressure-based algorithm is widely acknowledged for its superior convergence and robustness in solving incompressible flow problems. However, the increased expansion scale of equations and difficulty in solving the resulting equation system has limited the widespread use of the coupling algorithm. This paper presents an optimized block-SAMG method, referred to as Red-SAMG, to significantly reduce computational complexity and memory consumption. Our approach employs a parallel modified independent set algorithm, allowing each process to perform matrix coarsening individually. An aggressive coarsening strategy further reduces complexity to address larger-scale problems. We conducted numerical experiments on a serial two-dimensional backward-facing step case and a parallel three-dimensional tunnel between rod bundles in the reactor core problem. The results demonstrate that the Red-SAMG method with aggressive coarsening reduces solution time by 12\% on the backward-facing step case. For large-scale three-dimensional problems under the same parallel conditions, the Red-SAMG method shortens the solution time by 14\% compared to the existing SAMG method. The Red-SAMG method with aggressive coarsening further reduces solution time by 49\%. Our optimized Red-SAMG method effectively reduces the complexity of the linear system arising from the implicitly coupled algorithm. The aggressive coarsening strategy also represents a significant advancement in improving the computational efficiency of the SAMG method. © 2023, The Authors. All rights reserved.

关键词： Coarsening

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