parallel component technology improves the development efficiency and performance of parallel software. parallel component applications often need to be deployed on unstable heterogeneous cluster platforms. By analyzi...
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External and internal disruptions into power system stability motivates to research on a qualified grid resilience system. With this, indispensability of electricity infrastructure emphasizes the critical need for pow...
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Stochastic block partitioning (SBP) is a community detection algorithm that is highly accurate even on graphs with a complex community structure, but its inherently serial nature hinders its widespread adoption by the...
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ISBN:
(纸本)9798350307924
Stochastic block partitioning (SBP) is a community detection algorithm that is highly accurate even on graphs with a complex community structure, but its inherently serial nature hinders its widespread adoption by the wider scientific community. To make it practical to analyze large real-world graphs with SBP, there is a growing need to parallelize and distribute the algorithm. The current state-of-the-art distributed SBP algorithm is a divide-and-conquer approach that limits communication between compute nodes until the end of inference. This leads to the breaking of computational dependencies, which causes convergence issues as the number of compute nodes increases and when the graph is sufficiently sparse. To address this shortcoming, we introduce EDiSt - an exact distributed stochastic block partitioning algorithm. Under EDiSt, compute nodes periodically share community assignments during inference. Due to this additional communication, EDiSt improves upon the divide-and-conquer algorithm by allowing it to scale out to a larger number of compute nodes without suffering from convergence issues, even on sparse graphs. We show that EDiSt provides speedups of up to 26.9x over the divide-and-conquer approach and speedups up to 44.0x over shared memory parallel SBP when scaled out to 64 compute nodes.
Owing to population growth and industrialization, the need for electricity is at its apex, which creates stress on the grid due to the continuous consumption of power. To produce energy, natural resources are utilized...
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Graph processing has become increasingly popular and essential for solving complex problems in various domains, like social networks. However, the processing of graphs on a massive scale poses critical challenges, suc...
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ISBN:
(纸本)9783031488023;9783031488030
Graph processing has become increasingly popular and essential for solving complex problems in various domains, like social networks. However, the processing of graphs on a massive scale poses critical challenges, such as inefficient utilization of resources and energy. To bridge such challenges, the Graph-Massivizer project, funded by the Horizon Europe research and innovation program, conducts research and develops a high-performance, scalable, and sustainable platform for information processing and reasoning based on the massive graph (MG) representation of extreme data. This paper presents an initial architectural design for the Graph-Choreographer, one of the five Graph-Massivizer tools. We explain Graph-Choreographer's components and demonstrate how Graph-Choreographer can adopt the emerging serverless computing paradigm to process Basic Graph Operations (BGOs) as serverless functions across the computing continuum efficiently. We also present an early vision of our federated Function-as-a-Service (FaaS) testbed, which will be used to conduct experiments and assess the tool's performance.
The N -body problem is a classical computational challenge that involves integrating the motion equations of a system of interacting bodies. This problem is computationally demanding and, as power consumption becomes ...
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In the era marked by rapid advancements in "green energy"and storage technologies, microgrids integrated with distributed generation principles emerge as a promising avenue for efficient power management. DC...
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In the era of Big Data, the computational demands of machine learning (ML) algorithms have grown exponentially, necessitating the development of efficient parallelcomputing techniques. This research paper delves into...
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A new hierarchical communication parallelcomputing algorithm for transient structural analysis is introduced based on the architecture characteristics of heterogeneous multi-core processors in order to increase the p...
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Serverless computing has emerged as a new execution model which gained a lot of attention in cloud computing thanks to the latest advances in containerization technologies. Recently, serverless has been adopted at the...
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ISBN:
(纸本)9781538674628
Serverless computing has emerged as a new execution model which gained a lot of attention in cloud computing thanks to the latest advances in containerization technologies. Recently, serverless has been adopted at the edge, where it can help overcome heterogeneity issues, constrained nature and dynamicity of edge devices. Due to the distributed nature of edge devices, however, the scaling of serverless functions presents a major challenge. We address this challenge by studying the optimality of serverless function scaling. To this end, we propose Semi-Markov Decision Process-based (SMDP) theoretical model, which yields optimal solutions by solving the serverless function scaling problem as a decision making problem. We compare the SMDP solution with practical, monitoring-based heuristics. We show that SMDP can be effectively used in edge computing networks, and in combination with monitoring-based approaches also in real-world implementations.
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