# Speedup of Micromagnetic Simulations with C++ AMP on Graphics Processing Units

@article{Zhu2016SpeedupOM, title={Speedup of Micromagnetic Simulations with C++ AMP on Graphics Processing Units}, author={Ru Zhu}, journal={Computing in Science \& Engineering}, year={2016}, volume={18}, pages={53-59} }

A finite-difference micromagnetic solver called Grace uses C++ Accelerated Massive Parallelism (C++ AMP). The high-speed performance of a single GPU is compared against a typical CPU-based solver. The speedup of GPU to CPU is shown to be two orders of magnitude for problems with larger sizes. This solver can run on GPUs from various hardware vendors, such as Nvidia, AMD, and Intel, regardless of whether it is a dedicated or integrated graphics processor. The Web extra for this article includes…

## Figures, Tables, and Topics from this paper

## 5 Citations

Grace: a Cross-platform Micromagnetic Simulator On Graphics Processing Units

- Computer Science, MathematicsArXiv
- 2014

A micromagnetic simulator running on graphics processing unit (GPU) is presented. It achieves significant performance boost as compared to previous central processing unit (CPU) simulators, up to two…

Accelerate micromagnetic simulations with GPU programming in MATLAB

- Computer ScienceArXiv
- 2015

A finite-difference Micromagnetic simulation code written in MATLAB is presented with Graphics Processing Unit (GPU) acceleration and the speed-up of GPU to CPU is shown to be greater than 30 for problems with larger sizes on a mid-end GPU in single precision.

Grace: A cross-platform micromagnetic simulator on graphics processing units

- 2015

A micromagnetic simulator running on graphics processing units (GPUs) is presented. Different from GPU implementations of other research groups which are predominantly running on NVidia’s CUDA…

A GPU accelerated micromagnetic simulator for modelling complex magnetic systems

- Computer Science
- 2021

An object orientated GPU accelerated micromagnetic simulator designed to model the material characteristics of complex magnetic alloy systems.

Exploring Parallel Programming Models for Heterogeneous Computing Systems

- Computer Science2015 IEEE International Symposium on Workload Characterization
- 2015

It is demonstrated that while the emerging programming models improve programmer productivity, they do not yet expose enough flexibility to extract maximum performance as compared to traditional programming models.

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