Micromagnetics on high-performance workstation and mobile computational platforms

Fu, Sidi; Chang, Ramsay; Couture, S.; Menarini, Marco; Escobar, Marco Antonio Camacho; Kuteifan, M.; Lubarda, Marko V.; Gabay, D.; Lomakin, Vitaliy

doi:10.1063/1.4918638

Cited by 12 publications

(4 citation statements)

References 10 publications

(15 reference statements)

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“…The derivative and function evaluations in dynamical and static micromagnetic computations are very expensive, mostly due to the nonlocal stray field component. However, the use of a stray field algorithm that scales optimally or quasi-optimially with problem size such as the algebraic multigrid method [46,42,13], the fast multipole method [50,4,33] and hierarchical matrices [36], fast Fourier transform based methods (FFT) [50,16,49] or non-uniform FFT algorithms [29,15,20] is not sufficient to obtain a micromagnetic solver that scales linearly with problem size. Owing to the exchange interactions the micromagnetic equations can be considered as stiff [9] and the number of time steps in a dynamical solver or the number of iterations in a static solver increase with increasing problem size.…”

Section: Introductionmentioning

confidence: 99%

Preconditioned nonlinear conjugate gradient method for micromagnetic energy minimization

Exl

Fischbacher

Kovacs

et al. 2019

Computer Physics Communications

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Fast computation of demagnetization curves is essential for the computational design of soft magnetic sensors or permanent magnet materials. We show that a sparse preconditioner for a nonlinear conjugate gradient energy minimizer can lead to a speed up by a factor of 3 and 7 for computing hysteresis in soft magnetic and hard magnetic materials, respectively. As a preconditioner an approximation of the Hessian of the Lagrangian is used, which only takes local field terms into account. Preconditioning requires a few additional sparse matrix vector multiplications per iteration of the nonlinear conjugate gradient method, which is used for minimizing the energy for a given external field. The time to solution for computing the demagnetization curve scales almost linearly with problem size.

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Section: Introductionmentioning

confidence: 99%

Preconditioned nonlinear conjugate gradient method for micromagnetic energy minimization

Exl

Fischbacher

Kovacs

et al. 2019

Computer Physics Communications

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“…Traditionally, images sensed by spaceborne Earth-observation missions are not on-board processed. The main rationale behind this is the limited on-board power capacity that forces the use of low-power devices, which are normally not as highly performing as their commercial counterparts [3][4][5][6][7][8]. In this regard, images are subsequently downloaded to the Earth surface where they are off-line processed on high-performance computing systems based on Central Processing Units (CPUs), Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), or heterogeneous architectures.…”

Section: Introductionmentioning

confidence: 99%

FPGA-Based On-Board Hyperspectral Imaging Compression: Benchmarking Performance and Energy Efficiency against GPU Implementations

et al. 2020

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Remote-sensing platforms, such as Unmanned Aerial Vehicles, are characterized by limited power budget and low-bandwidth downlinks. Therefore, handling hyperspectral data in this context can jeopardize the operational time of the system. FPGAs have been traditionally regarded as the most power-efficient computing platforms. However, there is little experimental evidence to support this claim, which is especially critical since the actual behavior of the solutions based on reconfigurable technology is highly dependent on the type of application. In this work, a highly optimized implementation of an FPGA accelerator of the novel HyperLCA algorithm has been developed and thoughtfully analyzed in terms of performance and power efficiency. In this regard, a modification of the aforementioned lossy compression solution has also been proposed to be efficiently executed into FPGA devices using fixed-point arithmetic. Single and multi-core versions of the reconfigurable computing platforms are compared with three GPU-based implementations of the algorithm on as many NVIDIA computing boards: Jetson Nano, Jetson TX2 and Jetson Xavier NX. Results show that the single-core version of our FPGA-based solution fulfils the real-time requirements of a real-life hyperspectral application using a mid-range Xilinx Zynq-7000 SoC chip (XC7Z020-CLG484). Performance levels of the custom hardware accelerator are above the figures obtained by the Jetson Nano and TX2 boards, and power efficiency is higher for smaller sizes of the image block to be processed. To close the performance gap between our proposal and the Jetson Xavier NX, a multi-core version is proposed. The results demonstrate that a solution based on the use of various instances of the FPGA hardware compressor core achieves similar levels of performance than the state-of-the-art GPU, with better efficiency in terms of processed frames by watt.

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“…Fortunately, the emergence of computing boards that embed low energy consumption GPUs has made more attractive their use, especially in on-board applications carried out by unmanned aerial vehicles (UAVs) [9,17,18]. Nevertheless, these low-power GPUs (LPGPUs) are not as high performing as the same generation desktop GPUs [19,20].…”

Section: Introductionmentioning

confidence: 99%

Towards the Concurrent Execution of Multiple Hyperspectral Imaging Applications by Means of Computationally Simple Operations

et al. 2020

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The on-board processing of remotely sensed hyperspectral images is gaining momentum for applications that demand a quick response as an alternative to conventional approaches where the acquired images are off-line processed once they have been transmitted to the ground segment. However, the adoption of this on-board processing strategy brings further challenges for the remote-sensing research community due to the high data rate of the new-generation hyperspectral sensors and the limited amount of available on-board computational resources. This situation becomes even more stringent when different time-sensitive applications coexist, since different tasks must be sequentially processed onto the same computing device. In this work, we have dealt with this issue through the definition of a set of core operations that extracts spectral features useful for many hyperspectral analysis techniques, such as unmixing, compression and target/anomaly detection. Accordingly, it permits the concurrent execution of such techniques reusing operations and thereby requiring much less computational resources than if they were separately executed. In particular, in this manuscript we have verified the goodness of our proposal for the concurrent execution of both the lossy compression and anomaly detection processes in hyperspectral images. To evaluate the performance, several images taken by an unmanned aerial vehicle have been used. The obtained results clearly support the benefits of our proposal not only in terms of accuracy but also in terms of computational burden, achieving a reduction of roughly 50% fewer operations to be executed. Future research lines are focused on extending this methodology to other fields such as target detection, classification and dimensionality reduction.

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Micromagnetics on high-performance workstation and mobile computational platforms

Cited by 12 publications

References 10 publications

Preconditioned nonlinear conjugate gradient method for micromagnetic energy minimization

Preconditioned nonlinear conjugate gradient method for micromagnetic energy minimization

FPGA-Based On-Board Hyperspectral Imaging Compression: Benchmarking Performance and Energy Efficiency against GPU Implementations

Towards the Concurrent Execution of Multiple Hyperspectral Imaging Applications by Means of Computationally Simple Operations

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