Performance Boosting Portable Acceleration of SISSO++ for Symbolic Descriptor Learning
ORAL
Abstract
Sure-independence screening and sparsifying operator (SISSO) is a powerful, artificial intelligence tool for identifying symbolic descriptors and predictive models [1]. It has been successfully applied for discovering new optimal materials, e.g. [2]. Recently, we have developed SISSO++, a new implementation of SISSO that uses both OpenMP and MPI to approach linear-scaling parallel performance on CPUs [3]. Here, we present an updated algorithm that uses the Kokkos performance-portable programming model to offload the performance-critical region of our algorithm to accelerators, such as Nvidia or AMD GPUs [4]. We demonstrate the performance of these updates by using the prediction of thermal conductivity over rock salts and chalcopyrites as an example and highlight the opportunities opened by the improvement.
[1] R. Ouyang et al. Phys. Rev. Mater. 2, 083802 (2018).
[2] T. Purcell et al. submitted to npj Comput. Mater. arXiv:2204.12968 (2022).
[3] T. Purcell et al. Journal of Open Source Software 7, 3960 (2022).
[4] Ch. Trott et al. IEEE Transactions on Parallel and Distributed Systems 33, 805 (2022).
[1] R. Ouyang et al. Phys. Rev. Mater. 2, 083802 (2018).
[2] T. Purcell et al. submitted to npj Comput. Mater. arXiv:2204.12968 (2022).
[3] T. Purcell et al. Journal of Open Source Software 7, 3960 (2022).
[4] Ch. Trott et al. IEEE Transactions on Parallel and Distributed Systems 33, 805 (2022).
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Presenters
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Yi Yao
- The NOMAD Laboratory at the FHI-MPG and IRIS-Adlershof of HU, Berlin, Germany
- The NOMAD Laboratory at the Fritz Haber Institute of the MPG