Implementation and performance of Barnes-hut n-body algorithm on extreme-scale heterogeneous many-core architectures

Iwasawa, Masaki; Namekata, Daisuke; Sakamoto, Ryo; Nakamura, Takashi; Kimura, Yasuyuki; Nitadori, Keigo; Wang, Long; Tsubouchi, Miyuki; Makino, Jun; Liu, Zhao; Fu, Haohuan; Yang, Guangwen

doi:10.1177/1094342020943652

Cited by 5 publications

(2 citation statements)

References 20 publications

(33 reference statements)

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“…This means that grains which are far away from the point of interest can be clustered without a big lack in accuracy. A similar approximation is successfully applied by the Barns-Hut tree method which is used to solve N-body problems in astrophysics or electrodynamics [17,18]. The tree code reduces the theoretical run time to the order O(nlog(n)) and there are libraries available which implement this method.…”

Section: Demagnetization Fieldmentioning

confidence: 99%

Description of collective magnetization processes with machine learning models

Kornell¹,

Exl²,

Breth³

et al. 2022

Preprint

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This work introduces a latent space method to calculate the demagnetization reversal process of multigrain permanent magnets. The algorithm consists of two deep learning models based on neural networks. The embedded Stoner-Wohlfarth method is used as a reduced order model for computing samples to train and test the machine learning approach. The work is a proof of concept of the method since the used microstructures are simple and the only varying parameters are the magnetic anisotropy axes of the hard magnetic grains. A convolutional autoencoder is used for nonlinear dimensionality reduction, in order to reduce the required amount of training samples for predicting the hysteresis loops. We enriched the loss function with physical information about the underlying problem which increases the accuracy of the machine learning approach. A deep learning regressor is operating in the latent space of the autoencoder. The predictor takes a series of previously encoded magnetization states and outputs the next magnetization state along the hysteresis curve. To predict the complete demagnetization loop, we apply a recursive learning scheme.

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Section: Demagnetization Fieldmentioning

confidence: 99%

Description of collective magnetization processes with machine learning models

Kornell¹,

Exl²,

Breth³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Multi‐core processor refers to the use of multiple processor cores on a chip, and each core performs the same or similar tasks, the whole chip acts as a unified structure to provide external services, output performance, to meet the needs of improving performance and achieving load balance at a minimum cost [3]. The future development of high‐performance computing will come from the development of multi‐core processor technology [4]. The system tends to be composed of multi‐core, and multi‐core forms a cluster hierarchically [5].…”

Section: Introductionmentioning

confidence: 99%

Intelligent fitting global real‐time task scheduling strategy for high‐performance multi‐core systems

Zhao

et al. 2021

CAAI Trans on Intel Tech

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With the development of high-performance computing, it is possible to solve large-scale computing problems. However, the irregularity and access characteristics of computing problems bring challenges to the realisation and performance optimisation. Improving the performance of a single core makes it challenging to maintain Moore's law, and multicore processors emerge. A chip brings together multiple universal processor cores of equal status and has the same structure supported by an isomorphic multi-core processor. In high-performance computing, the granularity of computing tasks leads to the complexity of scheduling strategies. Satisfying high system performance, load balancing and processor fault tolerance at a minimum cost is the key to task scheduling in the highperformance field, especially in specific multi-core hardware architecture. In this study, global real-time task scheduling is implemented in a high-performance multi-core system. The system adopts the hybrid scheduling among clusters and the intelligent fitting within clusters to implement the global real-time task scheduling strategy. In the cluster scheduling policy, tasks are allowed to preempt the core with low priority, and the priority of tasks that access memory is dynamically improved, higher than that of all the tasks without memory access. An intelligent fitting method is also proposed. When the data read by the task is in the cache and the cache access ability value of the task is within a reasonable threshold, the priority of the task is promoted to the highest priority, preempting the core without the access memory task. The results show that the intelligently fitting global scheduling strategy for multi-core systems has better performance in the nuclear utilisation rate and task schedulability. K E Y W O R D S genetic algorithms, intelligent systems | INTRODUCTIONWith the expansion of application requirements and the progress of software technology, the demand for computing tasks on computing power increases sharply [1]. The method of improving system performance by increasing processor frequency is limited by power consumption, cost and volume; so multi-core processors emerge as the times require [2]. Multicore processor refers to the use of multiple processor cores on a chip, and each core performs the same or similar tasks, the whole chip acts as a unified structure to provide external services, output performance, to meet the needs of improving performance and achieving load balance at a minimum cost [3]. The future development of high-performance computing will come from the development of multi-core processor technology [4]. The system tends to be composed of multi-core, and multi-core forms a cluster hierarchically [5]. Furthermore, many-core, with hundreds or thousands of cores, will provide the computing potential for petaflops of high performance/ supercomputing [6]. Only by making the best use of parallel multi-core architecture can the performance of multi-core be brought into full play, and the new algorithm oriented to mul...

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Extreme-scale particle-based simulations on advanced HPC platforms

et al. 2020

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We overview the current status and future development directions of our framework for developing particle simulator (FDPS). Many of particle-based simulation codes share the same characteristic that the most time-consuming part of the simulation is the calculation of the interactions between particles, and a large fraction of programming effort is spent for procedures to make the force calculation efficient, such as the decomposition of computational domain, exchange of particles between domains, exchange of information necessary to calculate the interaction to particles in different domains, and efficient neighbor search. The basic idea of FDPS is to provide generic and high-performance library for these procedures. Using these procedures, researchers or application programmers in various fields can write their programs without taking care of parallelization and performance tuning. In order to make FDPS useful on advanced HPC platforms at present and in (near) future, we investigated its performance on several modern platforms and learned what can be the bottleneck. In this paper we summarize what we learned.

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Implementation and performance of Barnes-hut n-body algorithm on extreme-scale heterogeneous many-core architectures

Cited by 5 publications

References 20 publications

Description of collective magnetization processes with machine learning models

Description of collective magnetization processes with machine learning models

Intelligent fitting global real‐time task scheduling strategy for high‐performance multi‐core systems

Extreme-scale particle-based simulations on advanced HPC platforms

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