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ESIREOS: Efficient, Scalable, Internal, Relative Evaluation of Outliers Solutions 29

ESIREOS: Efficient, Scalable, Internal, Relative Evaluation ...

引用

29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Alves, William A. Marques, Henrique O. Naldi, Murilo C. Sander, Jorg Universidade Federal de São Carlos Department of Computing Science Sorocaba Brazil University of Southern Denmark Department of Mathematics and Computer Science Odense Denmark Universidade Federal de São Carlos Department of Computing Science São Carlos Brazil University of Alberta Department of Computing Science Edmonton Canada

ISBN: (纸本)9798350330717

Anomaly (outlier) detection is one of the main tasks of data mining. Since anomalies can translate into important information in numerous fields, several methods have been developed to identify them. Unsupervised methods for outlier detection, which is the focus of this work, have become increasingly important due to the lack of labeled data in many applications. A common challenge when dealing with unsupervised methods, however, is how to evaluate the quality of their results. Without labels available, one has to rely on the so-called internal evaluation, which is based solely on the data and the assessed solutions. In this context, IREOS was proposed as the first internal evaluation measure for unsupervised anomaly detection. IREOS allows one to select better solutions (algorithms, parameters) for a given problem using only intrinsic information from the data. One major limitation of IREOS, however, is the demand to train many highly complex classifiers, which makes it impractical for large datasets. In this work, we propose the first Efficient, Scalable version of IREOS, ESIREOS. We address the computational performance shortcomings of IREOS by using Massive parallel Computing (MPC) techniques that efficiently implement horizontal computational scaling for many machine learning problems. ESIREOS also makes use of approximated nearest neighbor graphs (NNGs) to reduce the volume of data and processing power demanded by IREOS without any significant loss in the quality of the results. We evaluate ESIREOS theoretically by estimating its asymptotic complexity and empirically with experiments on real and synthetic datasets to assess its effectiveness and efficiency compared to the original version. Our results showed that ESIREOS significantly improved the computational runtime compared to the original IREOS while maintaining quality. Also, ESIREOS proved capable of evaluating solutions for very large datasets, even those which IREOS cannot evaluate in a feasible time.

关键词： Data Mining Massive parallel Computing Outlier Detection Unsupervised Evaluation

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Optimized and Adaptive Dynamic Load Balancing in distributed Database Server 7

Optimized and Adaptive Dynamic Load Balancing in Distributed...

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7th international conference on Computing in Engineering and Technology, ICCET 2022

作者： Waghmode, Santosh T. Patil, Bankat M. Dr. Babasaheb Ambedkar Marathwada University MS Aurangabad India

ISBN: (纸本)9781839537042

Modern large-scale and small-scale data applications and processing solutions depend primarily on the distributed database system. distributed database servers are the most commonly used structures that give data availability, reliability, replication, and processing for both homogeneous and heterogeneous software and hardware systems. One of the significant challenges that will address by distributed file system is a parallel but not intersecting in the data load, which seems to be in the distribution of data items or data access from the nodes in the system. A distributed database system with high availability and scalability gives record offerings in terms of tolerance, availability, and reaction speed. Adaptive load balancing outperforms centralized and distributed load balancing. The technique considers restrictions such as network load, Input and output load, capacity and load, which were not taken into account in the previous method. The restriction satisfying hypothesis, which determines the best level for enhanced system productivity and response time, addresses the load balancing issue. According to experimental results, overall performance assessments in reading and writing operations are enhanced in response time. © 2022 The Institution of Engineering and Technology.

关键词： distributed database systems

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Mobius: Fine Tuning Large-Scale Models on Commodity GPU Servers 2023

Mobius: Fine Tuning Large-Scale Models on Commodity GPU Serv...

引用

28th ACM international conference on Architectural Support for Programming Languages and Operating systems (ASPLOS)

作者： Feng, Yangyang Xie, Minhui Tian, Zijie Wang, Shuo Lu, Youyou Shu, Jiwu Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9781450399166

Fine-tuning on cheap commodity GPU servers makes large-scale deep learning models benefit more people. However, the low interGPU communication bandwidth and pressing communication contention on the commodity GPU server obstruct training efficiency. In this paper, we present Mobius, a communication-efficient system for fine tuning large-scale models on commodity GPU servers. The key idea is a novel pipeline parallelism scheme enabling heterogeneous memory for large-scale model training, while bringing fewer communications than existing systems. Mobius partitions the model into stages and carefully schedules them between GPU memory and DRAM to overlap communication with computation. It formulates pipeline execution into a mixed-integer program problem to find the optimal pipeline partition. It also features a new stage-to-GPU mapping method termed cross mapping, to minimize communication contention. Experiments on various scale models and GPU topologies show that Mobius significantly reduces the training time by 3.8-5.1x compared with the prior art.

关键词： Neural networks parallel training distributed training

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Decoding the Granular Puzzle of Macromolecules: Efficient 3D Protein Structure Alignment in the Age of Big Data with Apache Spark

Decoding the Granular Puzzle of Macromolecules: Efficient 3D...

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2024 IEEE international conference on Big Data, BigData 2024

作者： Malysiak-Mrozek, Bozena Pawlowicz, Paulina Sunderam, Vaidy Hung, Che-Lun Kwiecien, Andrzej Mrozek, Dariusz Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice Poland Silesian University of Technology Department of Applied Informatics Gliwice Poland Emory University Department of Computer Science AtlantaGA United States National Yang Ming Chiao Tung University Institute of Biomedical Informatics Taipei City Taiwan

ISBN: (纸本)9798350362480

Proteins are complex biological information granules that play a crucial role in various cellular processes within living organisms. Processing 3D protein structures, which are the most informative from the biological point of view, is both intricate and time-consuming. In particular, performing 3D protein structure searches against large protein datasets involves identifying similarities and conducting structural alignments across numerous molecules (granules). This task demands advanced methods for matching identical and similar regions within protein structures and substantial computational resources to handle large collections of macromolecular data efficiently. In this paper, we present our parallel implementation of scalable 3D structural alignment on the Apache Spark big data platform. We describe a customized approach that leverages Spark data transformations within the data processing pipeline for the alignment process. Our experimental results demonstrate that this solution, tightly integrated with the Spark processing model, is both efficient and scalable, even with the increasing volume of protein structure data. © 2024 IEEE.

关键词： Information granules

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Towards Safe HPC: Productivity and Performance via Rust Interfaces for a distributed C plus plus Actors Library (Work in Progress) 20

Towards Safe HPC: Productivity and Performance via Rust Inte...

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20th ACM SIGPLAN international conference on Managed Programming Languages and Runtimes (MPLR)

作者： Parrish, John Wren, Nicole Kiang, Tsz Hang Hayashi, Akihiro Young, Jerey Sarkar, Vivek Georgia Inst Technol Atlanta GA 30332 USA Block Franklin Pk IL USA

ISBN: (纸本)9798400703805

In this work-in-progress research paper,we make the case for using Rust to develop applications in the High Performance Computing (HPC) domain which is critically dependent on native C/C++ libraries. This work explores one example of Safe HPC via the design of a Rust interface to an existing distributed C++ Actors library. This existing library has been shown to deliver high performance to C++ developers of irregular Partitioned Global Address Space (PGAS) applications. Our key contribution is a proof-of-concept framework to express parallel programs safely in Rust (and potentially other languages/systems), along with a corresponding study of the problems solved by our runtime, the implementation challenges faced, and user productivity. We also conducted an early evaluation of our approach by converting C++ actor implementations of four applications taken from the Bale kernels to Rust Actors using our framework. Our results show that the productivity benefits of our approach are significant since our Rust-based approach helped catch bugs statically during application development, without degrading performance relative to the original C++ actor versions.

关键词： Rust high performance computing actors unsafe annotations

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distributed Training Optimization for DCU

Distributed Training Optimization for DCU

引用

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

作者： Biaoyuan Chen Jianan Li Pengfei Li Jingming Xie Rongcai Zhao Wei Gao Lin Han Zhengzhou University Zhengzhou China National Supercomputing Center Zhengzhou National Supercomputing Center Zhengzhou Zhengzhou China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

With the continuous advancement of large-scale models and expanding volumes of data, a single acceleration hardware is no longer sufficient to meet the training demands. Simply stacking multiple acceleration hardware together in parallel does not enhance training efficiency, but rather results in a waste of computing resources. Therefore, for existing distributed training parallel methods, we first establish the definition of parallel strategies and construct cost boundaries to enable the automatic selection of efficient parallel strategies. Additionally, we implement a time-balanced partition algorithm based on pipeline parallelism to achieve more equitable model partitioning at each stage and reduce the overall cost of model training time. Experiments conducted on a single machine with four DCU cards and a dual machine with eight DCU cards demonstrate that multiple models, supported by the automatic parallel strategy, exhibit outstanding distributed training performance. Simultaneously, the average acceleration ratio can reach 1.13× with pipeline optimization. In summary, automatic selection of strategies and pipeline parallelism optimization significantly enhances the efficiency of distributed training for large models.

关键词： Training Costs Computational modeling Pipelines Stacking parallel processing Hardware

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Exploring Quantization Techniques for Large-Scale Language Models: Methods, Challenges and Future Directions 24

Exploring Quantization Techniques for Large-Scale Language M...

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9th international conference on Cyber Security and Information Engineering, ICCSIE 2024

作者： Shen, Ao Lai, Zhiquan Li, Dongsheng National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology China

ISBN: (纸本)9798400718137

Breakthroughs in natural language processing (NLP) by large-scale language models (LLMs) have led to superior performance in multilingual tasks such as translation, summarization, and Q&A. However, the size and complexity of these models raise challenges in terms of computational requirements, memory usage, and energy consumption. Quantization strategies, as a type of model compression technique, have gained attention for their advantages in reducing model size and accelerating inference speed. In this paper, we review the rapid development of quantization techniques for LLMs, and systematically explore methods such as post-training quantization (PTQ), quantization-aware fine-tuning (QAF), and quantization-aware training (QAT), which provide a comprehensive solution to the resource-intensive problem of LLMs from training to post-deployment. We also analyze state-of-the-art benchmarks and datasets to evaluate the effectiveness of quantitative methods in terms of performance retention and computational efficiency. The purpose of this review is to provide researchers with a snapshot of the latest progress in LLM quantization techniques, helping them to quickly grasp the dynamics of the field and understand the key techniques and challenges, so that they can more efficiently devote themselves to this evolving research area. © 2024 Copyright held by the owner/author(s).

关键词： Natural language processing systems

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Event - Triggered Control of distributed Networked Control systems with Time-varying Coupling Strength 2

Event - Triggered Control of Distributed Networked Control S...

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2nd international conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2022

作者： Bansal, Kritika Mukhija, Pankaj National Institute of Technology Delhi Department of Electrical and Electronics Engineering Delhi India

ISBN: (数字)9781665488754

ISBN: (纸本)9781665488754

Control of interconnected system communicating over limited network resources and having limited energy is a challenging task. With this objective, an event-triggering strategy for control of nonlinear distributed networked systems with time-varying coupling strength has been proposed in this paper. In the proposed methodology, the nonlinear subsystems are modelled through Takagi-Sugeno fuzzy approximation technique and the event-triggered controller is developed using parallel distributed compensation. A state based event-triggering strategy is proposed wherein each subsystem transmits its information over a network only when its local error exceeds a predetermined threshold value. This paper addresses the co-design problem of triggering parameters and feedback controller gain of nonlinear distributed networked systems with time-varying coupling strength. The efficacy of the results is validated via a simulation example. © 2022 IEEE.

关键词： Linear matrix inequalities

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Multi-Stage Vertex-Centric Programming for Agent-Based Simulations 2023

Multi-Stage Vertex-Centric Programming for Agent-Based Simul...

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22nd ACM SIGPLAN international conference on Generative Programming - Concepts and Experiences (GPCE)

作者： Tian, Zilu Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9798400704062

In vertex-centric programming, users express a graph algorithm as a vertex program and specify the iterative behavior of a vertex in a compute function, which is executed by all vertices in a graph in parallel, synchronously in a sequence of supersteps. While this programming model is straight-forward for simple algorithms where vertices behave the same in each superstep, for complex vertex programs where vertices have different behavior across supersteps, a vertex needs to frequently dispatch on the value of supersteps in compute, which suffers from unnecessary interpretation overhead and complicates the control flow. We address this using meta-programming: instead of branching on the value of a superstep, users separate instructions that should be executed in different supersteps via a staging-time wait() instruction. When a superstep starts, computations in a vertex program resume from the last execution point, and continue executing until the next wait(). We implement this in the programming model of an agent-based simulation framework CloudCity and show that avoiding the interpretation overhead caused by dispatching on the value of a superstep can improve the performance by up to 25% and lead to more robust performance.

关键词： agent-based simulations vertex-centric programming distributed systems staging metaprogramming

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Droop Control Strategy in Decentralized DC/AC Inverter-based Smartgrid for Power Balance 6

Droop Control Strategy in Decentralized DC/AC Inverter-based...

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6th international conference on Electrical Engineering and Information and Communication Technology, ICEEICT 2024

作者： Shufian, Abu Hannan, Nasif Kabir, Shaharier Kabir, Md Ahsan Fattah, Shaikh Anowarul American International University-Bangladesh Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Bangladesh University of Engineering and Technology Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Electronic and Communication Engineering Military Institute of Science and Technology Dept. of Electrical Dhaka Bangladesh

ISBN: (纸本)9798350385779

Microgrids represent a core component of future power systems, consisting of interconnected distributed generators (DGs), energy storing units, with multiple consumers functioning under coordinated command, often incorporating renewables. This paper introduces a decentralized power-balance control strategy for grid voltage and current for a smartgrid involving a virtual impedance droop controller. The proposed technique focuses on three parallel voltage source inverters responsible for sharing both residential and industrial loads. Virtual impedances are employed to mitigate the effects of fluctuating line impedances, thereby minimizing circulating voltages, currents, and imbalances in power distribution. This research evaluates the strengths and weaknesses of conventional droop control methods and suggests a decentralized, novel droop control for balancing active and reactive power among integrated multiple DG's, incorporating grid voltage (310 V), current (24.11 A), and frequency (45.95 Hz) control in OFF/ON- grid mode operation of smartgrid. The operational validity of the droop controller model is verified in real-time using MATLAB, showcasing robust performance under inductive behavior in an OFF -grid or ON-grid system. © 2024 IEEE.

关键词： Controllers

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