MatRIS: Addressing the Challenges for Portability and Heterogeneity Using Tasking for Matrix Decomposition (Cholesky)
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-61763-8_6
Reference16 articles.
1. Bosilca, G., Bouteiller, A., Danalis, A., Faverge, M., Hérault, T., Dongarra, J.J.: PaRSEC: exploiting heterogeneity to enhance scalability. Comput. Sci. Eng. 15(6), 36–45 (2013)
2. Bosilca, G., et al.: Distibuted dense numerical linear algebra algorithms on massively parallel architectures: DPLASMA (2010)
3. Cabrera, A.M., Hitefield, S., Kim, J., Lee, S., Miniskar, N.R., Vetter, J.S.: Toward performance portable programming for heterogeneous systems on a chip: a case study with qualcomm snapdragon SoC. In: 2021 IEEE High Performance Extreme Computing Conference, HPEC 2021, Waltham, MA, USA, 20–24 September 2021, pp. 1–7. IEEE (2021). https://doi.org/10.1109/HPEC49654.2021.9622794
4. Dongarra, J.J., et al.: PLASMA: parallel linear algebra software for multicore using OpenMP. ACM Trans. Math. Softw. 45(2), 16:1–16:35 (2019). https://doi.org/10.1145/3264491
5. Farhan, M.A.A., et al.: MAGMA templates for scalable linear algebra on emerging architectures. Int. J. High Perform. Comput. Appl. 34(6), 645–658 (2020). https://doi.org/10.1177/1094342020938421
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