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PDCO 2022 Keynote Talk: Performance and Energy models for modern HPC servers

PDCO 2022 Keynote Talk: Performance and Energy models for mo...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Georges Da Costa Toulouse Institute for Research in Computer Science

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Most energy-aware studies are based on hypothesis. The most important are the models of the studied system. Depending on the precision of these models, the proposed theoretical framework can be transposed on actual system or are limited to theoretical studies. During this keynote, we will explore the differences between classically used models of performance, power, and energy of HPC systems on one side, and actual measures on several clusters on the other. It will include elements on the heterogeneity of homogeneous systems. We will also propose more realistic models for expressing the behavior of HPC systems, from hardware to applications. Some elements on the tradeoff between the complexity of the algorithms and their precision will also be precised. One of the key metric of this complexity will be the amount of data needed to obtain them as measuring energy-related information on HPC datacenters is often difficult.

关键词： Computational modeling Energy measurement distributed processing Conferences Complexity theory Servers Power measurement

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(CGRA4HPC) 2022 Invited Speaker: Pushing the Boundaries of HPC with the Integration of AI

(CGRA4HPC) 2022 Invited Speaker: Pushing the Boundaries of H...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Raghu Prabhakar SambaNova Systems

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

As the market for high-performance computing continues to heat up there are constraints caused by the limited processing speed capacity of current architectures like CPUs and GPUs. Higher-resolution image processing requires massive computational capabilities making it challenging and costly for even the most innovative companies to succeed. SambaNova systems has pioneered the ability to liberate organizations from the limitations of their legacy solutions — providing the core datacenter infrastructure to quickly build and deploy AI technologies at scale. Developers can run a 100-billion parameter model with ease, and enable highperformance processing of complex structures such as high-resolution images, pushing computer vision boundaries far beyond 4k. SambaNova systems will showcase groundbreaking technology delivered by SambaNova's Reconfigurable Dataflow Architecture™ - an accelerated computing architecture designed to efficiently execute a broad range of AI and HPC applications.

关键词： Computer architecture Artificial intelligence distributed processing Conferences Image processing High performance computing Heating systems

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Pipelining Graph Construction and Agent-based Computation over distributed Memory

Pipelining Graph Construction and Agent-based Computation ov...

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2022 IEEE international Conference on Big Data, Big Data 2022

作者： Hong, Yan Fukuda, Munehiro University of Washington Bothell Computing and Software Systems BothellWA98011 United States

ISBN: (纸本)9781665480451

Graph streaming has received substantial attention for the past 10+ years to cope with large-scale graph computation. Two major approaches, one using conventional data-streaming tools and the other accessing graph databases, facilitate continuous analysis of endlessly flowing graphs and query-based incremental construction of huge graphs, respectively. However, some scientific graphs including biological networks need to stay in memory for repetitive but various analyses. Although a cluster system, thus distributed memory can entirely handle a big graph in memory, a challenge is substantial overhead incurred by loading graphs into memory. A solution is hiding such graph-loading and construction overheads with graph computation in a pipelined fashion. We adapted this pipelining approach for agent-based graph computing where thousands of agents traverse a graph for finding its attributes and shape. We used the multi-agent spatial simulation (MASS) library to implement the concept. A huge graph is incrementally constructed in batches, each spawning and walking agents over the corresponding subgraph, and thus all eventually completing a given computation. We coded and ran two MASS benchmark programs: triangle counting and connected components, with which we evaluated our pipelined graph processing. The best performance was obtained once the batch size shrunk enough to fit cache memory, regardless of the number of cluster nodes. For a single node execution of connected components over a 140MB graph, our graph-pipelining implementation performed 7.7 times faster than non-pipelining execution. Its parallel execution with 24 cluster nodes achieved 8.3 times speed-up as compared to the pipelined single-node execution. © 2022 IEEE.

关键词： Cache memory

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TMan: A High-Performance Trajectory Data Management System Based on Key-Value Stores

TMan: A High-Performance Trajectory Data Management System B...

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international Conference on Data Engineering

作者： Huajun He Zihang Xu Ruiyuan Li Jie Bao Tianrui Li Yu Zheng School of Computing and Artificial Intelligence Southwest Jiaotong University China JD iCity JD Technology Beijing China JD Intelligent Cities Research China Chongqing University China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The effective management of trajectory data heavily relies on the utilization of fundamental spatio-temporal queries. The surge in trajectory data, with its dynamic spatio-temporal properties, poses notable management challenges. Existing systems are inadequate in providing fine-grained trajectory representations and efficient architecture for processing queries, leading to significant computational overhead. This paper introduces TMan to address these challenges. First, TMan presents two innovative index structures that precisely capture the spatio-temporal characteristics of trajectory data. Compared to the state-of-the-art indexes, our indexes for temporal range and spatial range queries can reduce the number of retrievals by up to 77% and 83%, respectively. Next, TMan devises concise and effective encoding methods for these indexes. Leveraging these indexes, TMan provides a distributed storage structure and an index caching mechanism for efficiently managing trajectories in key-value data stores. Moreover, TMan introduces a parallel query processing approach incorporating a push-down strategy to enhance the efficiency of fundamental queries. Extensive experimental results demonstrate that TMan's index structures and architecture outperform the baselines.

关键词： Shape Query processing distributed databases Computer architecture Data engineering Encoding Trajectory

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4th Workshop on parallel AI and systems for the Edge

Proceedings - 2022 IEEE 36th International Parallel and Dist...

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Proceedings - 2022 IEEE 36th international parallel and distributed Processing symposium Workshops, IPDPSW 2022 2022年 982-984页

作者： Akkus, Istemi Ekin Desai, Nirmit V Nokia Bell Labs Germany Ibm Research United States

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A parallelized Clustering Method for High-dimensional Power Multilevel Data Resources in load changes Mode

A Parallelized Clustering Method for High-dimensional Power ...

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Energy, Electrical and Power Engineering (CEEPE), international Conference on

作者： Guoliang Qian Jiahui Zhang Zilong Deng Qinbin Huang Leijian Hu Technology Department (of State Grid Zhejiang) State Grid Zhejiang Pinghu City Electric Power Supply Co. Ltd. Pinghu China Technology Department (of State Grid Zhejiang) Pinghu General Electric Installation Co. Ltd. Pinghu China

ISBN: (数字)9798350375794

ISBN: (纸本)9798350375800

Conventional parallelized clustering methods for high-dimensional electric power multilevel data resources are based on a single data feature, which cannot meet the demand of parallelized clustering. Therefore, a parallelized clustering method for high-dimensional power multilevel data resources under load changes mode is designed. The sparse features of high-dimensional electric power multilevel data resources are extracted, and the high-dimensional electric power data are changed into low-dimensional observation data to reduce the data dimensions and solve the optimal sparse subspace, so as to satisfy the demand of parallel clustering of electric power resources. The parallelized clustering center of high-dimensional electric power multilevel data resources is established based on load changes, and the high-dimensional electric power multilevel data resources are optimized by parallel genetic algorithm, and whether the electric power data belongs to the value domain is detected, so as to realize the accurate clustering of data resources. Comparison experiments are used to verify that the method has better parallelized clustering effect and can be applied in real life.

关键词： Power engineering Clustering methods distributed databases Feature extraction Data processing Explosives Power systems

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Debugging strongly-compartmentalized distributed systems 34

Debugging strongly-compartmentalized distributed systems

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Zhu, Henry Sultana, Nik Loo, Boon Thau Univ Penn Comp & Informat Sci Philadelphia PA 19104 USA

ISBN: (纸本)9781728174457

Splitting software into distributed compartments is an important software security technique that limits the effect of vulnerabilities. Unfortunately the resulting systems are difficult to analyze or debug interactively when compared to the original. Not only are compartments distributed and executed in parallel, but they may also be strongly isolated by being sandboxed or run in minimal environments that lack debugging facilities. This paper is the first to study practical debugging techniques for strongly-isolated distributed compartments. We adapt ideas from other remote or distributed debugging settings to this domain, and implement and describe two radically different approaches to this problem. We evaluate these approaches both qualitatively and quantitatively, and using both toy examples and real-world open-source software. Our main finding is that out of the two approaches, using GDB remote stubs presents a good balance of performance, flexibility and usability, and we characterize this more precisely in our evaluation.

关键词： software compartments distributed debugging development tools

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6th IEEE Workshop on parallel and distributed Processing for Computational Social systems (ParSocial 2022)

6th IEEE Workshop on Parallel and Distributed Processing for...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： John Korah Eunice E. Santos

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Welcome to the Sixth IEEE Workshop on parallel and distributed Processing for Computational Social systems (ParSocial 2022). This year the workshop highlights novel algorithms and models that leverage parallel computing with applications in social network and social media analysis. The first set of papers focus on the key individual identification problem in social network analysis. The paper by Vandromme et al entitled “Efficient parallel PageRank Algorithm for Network Analysis” proposes a more efficient parallel algorithm for PageRank that has been shown to improve the time complexity by a factor of two. In a similar vein, the paper by Sahu et al entitled “Dynamic Batch parallel Algorithms for Updating PageRank” proposes two parallel algorithms for recomputing PageRank of nodes in a dynamic social network that can scale across various architectures. A related research problem is identifying opinion leaders that can improve information dissemination within communities. The paper entitled “Effect of Community-based Opinion Leaders on Guideline Dissemination in Large-Scale Physician Networks” by Murugappan et al, focuses on the problem of the dissemination of medical guidelines. The authors propose a culturally infused agent based model to analyze the effectiveness of various opinion leader selection strategies and the tradeoffs between the reach and rate of spread of medical guideline information. The next set of papers focus on social media analysis. systems for large scale ingestion of social media data sets can support a wide range of research problems in computational social systems. A step in this direction is taken by authors Huber et al, who have proposed a parallel system for large scale processing of Reddit data in their paper entitled “A Streaming System for Large-scale Mining of Reddit Data”. On the other hand, authors Abeysinghe et al in their short research paper entitled “Unsupervised User Stance Detection on Tweets Against Web Articles Using Sentence

关键词： Social networking (online) Conferences distributed processing parallel algorithms Heuristic algorithms Guidelines Analytical models

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Decentralized model predictive control for autonomous robot swarms with restricted communication skills in unknown environments 14

Decentralized model predictive control for autonomous robot ...

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14th international symposium on Intelligent systems

作者： Puzicha, Alexander Buchholz, Peter Tech Univ Dortmund Emil Figge Str 61 D-44227 Dortmund Germany

The paper presents a new approach and a related real-time parallel simulation tool for modeling and analyzing a swarm of more than 60 distributed autonomous mobile robots communicating over an unreliable and capacity restricted wireless communication network. It includes a physical simulation of static obstacles, dynamic obstacles with scriptable movement, soil condition, active jammers, static and dynamic link obstacles with configurable damping as well as thermal noise. The simulated ground based mobile robots use CPBP [12] as probabilistic model predictive closed loop controller in combination with gradient free cost functions to evaluate complex unsteady goodness aggregations. The goal of this approach is the development of connection aware swarm behavior for complex missions such as terrain exploration, formations, convoy escorting or creation of a mobile ad hoc network in dynamic disaster areas under realistic environmental conditions. The missions can be combined in any manner and the target extraction of the high-level commands for each agent is done implicitly. To validate the developed behavior, the independent software control kernel is able to be used on real robots as well. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Real-Time Simulations parallel Simulations Simulation-Based Virtual Environments Swarm Robotics Model Predictive Control Autonomous Robots Swarm Behavior

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Mitigating Data Hotspots in distributed Cloud Storage Architecture

Mitigating Data Hotspots in Distributed Cloud Storage Archit...

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Computer, Big Data and Artificial Intelligence (ICCBD+AI), international Conference on

作者： Junjie Li Haodong Zou Wangdong Wu Lei Wang Lichen Wang Wenpan Liu Mei Yan State Grid Electric Power Research Institute Co. Ltd Nanjing China Information & Telecommunication Branch State Grid Jiangsu Electric Power Co. Ltd. Nanjing China State Grid Information & Telecommunication Branch

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

Cloud storage is a vital component of cloud architecture, often utilizing distributed key-value stores like Amazon S3 and Google Cloud Storage for managing da-ta and metadata. These systems distribute data across nodes using key range or consistent hashing, but they face challenges such as load imbalance and limited parallelism due to uneven data distribution and varying node performance. Cur-rent implementations, such as MongoDB, address these imbalances by migrating data between nodes but often neglect the characteristics of the underlying data structures, leading to increased overhead from costly delete and insert operations. To address these issues, this design leverages the properties of the LSM tree, a commonly used storage engine, to optimize data migration. The approach intro-duces hot zone prediction using nonlinear regression to accurately identify data hotspots based on key characteristics, insertion time, and TTL. A storage engine-aware migration system is developed to migrate grouped SSTable files rather than individual key-value pairs, significantly reducing migration overhead. Additionally, the data migration I/O process is offloaded using the NVMe-oF protocol, minimizing CPU involvement and preserving node performance. Implemented on mongo-rocks, this solution improves load balancing by directly moving SSTable files across nodes, enhancing efficiency and reducing performance degradation in distributed key-value stores.

关键词： Degradation Cloud computing Protocols distributed databases parallel processing Metadata Load management Optimization Faces Engines

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