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Visuel: A novel performance monitoring and analysis toolkit for cluster and grid environments

6th International Conference on Algorithms and Architectures for parallel Processing, ICA3PP

作者： Li, Kuan-Ching Cheng, Hsiang-Yao Yang, Chao-Tung Hsu, Ching-Hsien Wang, Hsiao-Hsi Hsu, Chia-Wen Hung, Sheng-Shiang Chang, Chia-Fu Liu, Chun-Chieh Pan, Yu-Hwa Parallel and Distributed Processing Center Department of Computer Science and Information Management Providence University Taichung 43301 Taiwan High Performance Computing Laboratory Department of Computer Science and Information Engineering Tunghai University Taichung 40704 Taiwan Department of Computer Science and Information Engineering Chung Hua University Hsinchu 300 Taiwan

ISBN: (纸本)3540292357

The computing power provided by high performance low-cost PC-based Cluster and Grid platforms are attractive, and they are equal or superior to supercomputers and mainframes widely available. In this research paper, we present the design rationale and implementation of Visuel, a toolkit for performance measurement and analysis of MPI parallel programs and real time resources monitoring in cluster and grid computing environments. The proposed toolkit is web-based interface to show performance activities of all computing nodes involved in the execution of a MPI parallel program, such as CPU and memory usage levels of each computing node, and monitors all computing nodes of a computing platform by displaying real time performance data. In addition, this toolkit is able to display comparative performance data charts of multiple executions of MPI parallel application under investigation, which facilitates the "what-if" analysis. The usage of this toolkit shows that it outperforms in easing the process of investigation of parallel applications. © Springer-Verlag Berlin Heidelberg 2005.

关键词： distributed computer systems

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Mitigating sync amplification for copy-on-write virtual disk 16

Mitigating sync amplification for copy-on-write virtual disk

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Proceedings of the 14th Usenix Conference on File and Storage Technologies

作者： Qingshu Chen Liang Liang Yubin Xia Haibo Chen Hyunsoo Kim Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University Samsung Electronics Co. Ltd.

ISBN: (纸本)9781931971287

Copy-on-write virtual disks (e.g., qcow2 images) provide many useful features like snapshot, de-duplication, and full-disk encryption. However, our study uncovers that they introduce additional metadata for block organization and notably more disk sync operations (e.g., more than 3X for qcow2 and 4X for VMDK images). To mitigate such sync amplification, we propose three optimizations, namely per virtual disk internal journaling, dual-mode journaling, and adaptive-preallocation, which eliminate the extra sync operations while preserving those features in a consistent way. Our evaluation shows that the three optimizations result in up to 110% performance speedup for varmail and 50% for TPCC.

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KC-GenRe: A Knowledge-constrained Generative Re-ranking Method Based on Large Language Models for Knowledge Graph Completion

arXiv

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arXiv 2024年

作者： Wang, Yilin Hu, Minghao Huang, Zhen Li, Dongsheng Yang, Dong Lu, Xicheng Defense Innovation Institute Academy of Military Sciences China Information Research Center of Military Science China National Key Laboratory of Parallel and Distributed Computing China

The goal of knowledge graph completion (KGC) is to predict missing facts among entities. Previous methods for KGC re-ranking are mostly built on non-generative language models to obtain the probability of each candidate. Recently, generative large language models (LLMs) have shown outstanding performance on several tasks such as information extraction and dialog systems. Leveraging them for KGC re-ranking is beneficial for leveraging the extensive pre-trained knowledge and powerful generative capabilities. However, it may encounter new problems when accomplishing the task, namely mismatch, misordering and omission. To this end, we introduce KC-GenRe, a knowledge-constrained generative re-ranking method based on LLMs for KGC. To overcome the mismatch issue, we formulate the KGC re-ranking task as a candidate identifier sorting generation problem implemented by generative LLMs. To tackle the misordering issue, we develop a knowledge-guided interactive training method that enhances the identification and ranking of candidates. To address the omission issue, we design a knowledge-augmented constrained inference method that enables contextual prompting and controlled generation, so as to obtain valid rankings. Experimental results show that KG-GenRe achieves state-of-the-art performance on four datasets, with gains of up to 6.7% and 7.7% in the MRR and Hits@1 metric compared to previous methods, and 9.0% and 11.1% compared to that without re-ranking. Extensive analysis demonstrates the effectiveness of components in KG-GenRe. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Don't Half-listen: Capturing Key-part Information in Continual Instruction Tuning

arXiv

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arXiv 2024年

作者： He, Yongquan Huang, Xuancheng Tang, Minghao Meng, Lingxun Li, Xiang Lin, Wei Zhang, Wenyuan Gao, Yifu Meituan China Institute of Information Engineering Chinese Academy of Sciences China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology China

Instruction tuning for large language models (LLMs) can drive them to produce results consistent with human goals in specific downstream tasks. However, the process of continual instruction tuning (CIT) for LLMs may bring about the catastrophic forgetting (CF) problem, where previously learned abilities are degraded. Recent methods try to alleviate the CF problem by modifying models or replaying data, which may only remember the surface-level pattern of instructions and get confused on held-out tasks. In this paper, we propose a novel continual instruction tuning method based on Key-part Information Gain (KPIG). Our method computes the information gain on masked parts to dynamically replay data and refine the training objective, which enables LLMs to capture task-aware information relevant to the correct response and alleviate overfitting to general descriptions in instructions. In addition, we propose two metrics, P-score and V-score, to measure the generalization and instruction-following abilities of LLMs. Experiments demonstrate our method achieves superior performance on both seen and held-out tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital storage

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NR-MPI: A non-stop and fault resilient MPI supporting programmer defined data backup and restore for E-scale super computing systems

Supercomputing Frontiers and Innovations

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Supercomputing Frontiers and Innovations 2016年第1期3卷 4-21页

作者： Suo, Guang Lu, Yutong Liao, Xiangke Xie, Min Cao, Hongjia State Key Laboratory of High Performance Computing National University of Defense Technology Changsha Hunan Province China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha Hunan Province China

Fault resilience has became a major issue for HPC systems, particularly, in the perspective of future E-scale systems, which will consist of millions of CPU cores and other components. MPI-level fault tolerant constructs, such as ULFM, are being proposed to support software level fault tolerance. However, there are few systematic evaluations by application programmers using benchmarks or pseudo applications. This paper proposes NR-MPI, a Non-stop and Fault Resilient MPI, supporting programmer defined data backup and restore. To help programmers write fault tolerant programs, NR-MPI provides a set of friendly programming interfaces and a state transition diagram for data backup and restore. This paper focuses on design, implementation and evaluation of NR-MPI. Specifically,this paper puts emphases on failure detection in MPI library, friendly programming interface extending for NR-MPI and examples of fault tolerant programs based NRMPI. Furthermore, to support failure recovery of applications, NR-MPI implements data backup interfaces based on double in-memory checkpoint/*** conduct experiments with both NPB benchmarks and Sweep3D on TH supercomputer in NSCC-TJ. Experimental results show that NR-MPI based fault tolerant programs can recover from failures online without restarting, and the overhead is small even for applications with tens of thousands of cores. © The Authors 2016.

关键词： Message passing

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HAWK: Halting Anomalies with Weighted choKing to rescue well-behaved TCP sessions from shrew DDoS attacks

HAWK: Halting Anomalies with Weighted choKing to rescue well...

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Third International Conference on Computer Network and Mobile computing, ICCNMC 2005

作者： Kwok, Yu-Kwong Tripathi, Rohit Chen, Yu Hwang, Kai University of Southern California Los Angeles CA 90089 United States Department of Electrical and Electronic Engineering HKU Hong Kong University of Southern California United States Internet and Grid Computing Laboratory University of Southern California United States IEEE Transactions on Parallel and Distributed System United States USC United States

High availability in network services is crucial for effective large-scale distributed computing. While distributed denial-of-service (DDoS) attacks through massive packet flooding have baffled researchers for years, a new type of even more detrimental attack - shrew attacks (periodic intensive packet bursts with low average rate) - has recently been identified. Shrew attacks can significantly degrade well-behaved TCP sessions, repel potential new connections, and are very difficult to detect, not to mention defend against, due to its low average rate. We propose a new stateful adaptive queue management technique called HAWK (Halting Anomaly with Weighted choKing) which works by judiciously identifying malicious shrew packet flows using a small flow table and dropping such packets decisively to halt the attack such that well-behaved TCP sessions can re-gain their bandwidth shares. Our NS-2 based extensive performance results indicate that HAWK is highly agile. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Mobile computing

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distributed Line Graphs: A Universal Framework for Building DHTs Based on Arbitrary Constant-Degree Graphs

Distributed Line Graphs: A Universal Framework for Building ...

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International Conference on distributed computing Systems

作者： Yiming Zhang Ling Liu Dongsheng Li Xicheng Lu National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Georgia Institute of Technology College of Computing Georgia Institute of Technology Atlanta USA

ISBN: (纸本)9781424431748

Most proposed DHTs have their unique maintenance mechanisms specific to the static graphs on which they are based. In this paper we propose distributed line graphs (DLG), a universal framework for building DHTs based on arbitrary constant-degree graphs. We prove that in a DLG-enabled, N-node DHT, the out-degree is d, the in-degree is between 1 and 2d, and the diameter is less than 2(log d N-log d N 0 +D 0 +1), where d, D 0 and N 0 represent the degree, diameter and number of nodes of the initial graph, respectively. The maintenance cost of DLG-enabled DHTs is O(log d N). We show the power of DLG technique by applying it to Kautz graphs to propose a new DHT scheme.

关键词： Routing Maintenance engineering Buildings Distance measurement Topology Industries Construction industry

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Replication-Based Fault-Tolerance for Large-Scale Graph Processing

Replication-Based Fault-Tolerance for Large-Scale Graph Proc...

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International Conference on Dependable Systems and Networks (DSN)

作者： Peng Wang Kaiyuan Zhang Rong Chen Haibo Chen Haibing Guan Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University Department of Computer Science Shanghai Jiao Tong University

The increasing algorithm complexity and dataset sizes necessitate the use of networked machines for many graph-parallel algorithms, which also makes fault tolerance a must due to the increasing scale of machines. Unfortunately, existing large-scale graph-parallel systems usually adopt a distributed checkpoint mechanism for fault tolerance, which incurs not only notable performance overhead but also lengthy recovery time. This paper observes that the vertex replicas created for distributed graph computation can be naturally extended for fast in-memory recovery of graph states. This paper proposes Imitator, a new fault tolerance mechanism, that supports cheaply maintenance of vertex states by replicating vertex states to their replicas during normal message exchanges, and provides fast in-memory reconstruction of failed vertices from replicas in other machines. Imitator has been implemented by extending Hama, a popular open-source clone of Pregel. Evaluation shows that Imitator incurs negligible performance overhead (less than 5% for all cases) and can recover from failures of more than one million of vertices with less than 3.4 seconds.

关键词： Fault tolerance Fault tolerant systems Computer crashes Checkpointing Synchronization Clustering algorithms Computational modeling

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Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning

arXiv

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arXiv 2023年

作者： Xu, Hongzuo Wang, Yijie Wei, Juhui Jian, Songlei Li, Yizhou Liu, Ning National Key Laboratory of Parallel and Distributed Computing China College of Computer National University of Defense Technology China College of Science National University of Defense Technology China

Due to the unsupervised nature of anomaly detection, the key to fueling deep models is finding supervisory signals. Different from current reconstruction-guided generative models and transformation-based contrastive models, we devise novel data-driven supervision for tabular data by introducing a characteristic – scale – as data labels. By representing varied sub-vectors of data instances, we define scale as the relationship between the dimensionality of original sub-vectors and that of representations. Scales serve as labels attached to transformed representations, thus offering ample labeled data for neural network training. This paper further proposes a scale learning-based anomaly detection method. Supervised by the learning objective of scale distribution alignment, our approach learns the ranking of representations converted from varied subspaces of each data instance. Through this proxy task, our approach models inherent regularities and patterns within data, which well describes data "normality". Abnormal degrees of testing instances are obtained by measuring whether they fit these learned patterns. Extensive experiments show that our approach leads to significant improvement over state-of-the-art generative/contrastive anomaly detection methods. © 2023, CC BY.

关键词： Anomaly detection

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

DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for...

引用

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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