Improving speed of LLG reservoir simulations

Our paper got accepted at MLNCP^^

Title Virtual reservoir acceleration for CPU and GPU: Case study for coupled spin-torque oscillator reservoir

Abstract We provide high-speed implementations for simulating reservoirs described by $N$-coupled spin-torque oscillators. Here $N$ also corresponds to the number of reservoir nodes. We benchmark a variety of implementations based on CPU and GPU. Our new methods are at least 2.6 times quicker than the baseline for $N$ in range 1 to $10^4$. More specifically, over all implementations the best factor is 78.9 for $N=1$ which decreases to 2.6 for $N=10^3$ and finally increases to 23.8 for $N=10^4$. GPU outperforms CPU significantly at $N=2500$. Our results show that GPU implementations should be tested for reservoir simulations. The implementations considered here can be used for any reservoir with evolution that can be approximated using an explicit method.

References

2023

MLNCP

Virtual reservoir acceleration for CPU and GPU: Case study for coupled spin-torque oscillator reservoir

Thomas G de Jong, Nozomi Akashi, Tomohiro Taniguchi, and 2 more authors

In NeurIPS: ML with New Compute Paradigms Workshop , 2023

Bib HTML

@inproceedings{de2023mlncp,
  title = {Virtual reservoir acceleration for CPU and GPU: Case study for coupled spin-torque oscillator reservoir},
  author = {Jong, Thomas G de and Akashi, Nozomi and Taniguchi, Tomohiro and Notsu, Hirofumi and Nakajima, Kohei},
  booktitle = {NeurIPS: ML with New Compute Paradigms Workshop },
  year = {2023},
  organization = {NeurIPS},
}