2026-04-13T02:33:19Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/4449732026-04-01T07:06:55Zcom_2072_1033col_2072_452950

urn:hdl:2117/444973 Batched DGEMMs for scientific codes running on long vector architectures Banchelli Gracia, Fabio Garcia Gasulla, Marta Mantovani, Filippo Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Aplicacions informàtiques a la física i l‘enginyeria Batched DGEMM RISC-V Long vector Optimization In this work, we evaluate the performance of SeisSol, a simulator of seismic wave phenomena and earthquake dynamics, on a RISCV-based system utilizing a vector processing unit. We focus on GEMM libraries and address their limited ability to leverage long vector architectures by developing a batched DGEMM library in plain C. This library achieves speedups ranging from approximately 3.5× to 32.6× compared to the reference implementation. We then integrate the batched approach into the SeisSol application, ensuring portability across different CPU architectures. Lastly, we demonstrate that our implementation is portable to an Intel CPU, resulting in improved execution times in most cases. Supported by the EuroHPC Joint Undertaking (JU): FPA N. 800928 (EPI), SGA N. 101036168 (EPI-SGA2), and GA N. 101093038 (ChEESE-2P CoE). The JU receives support from the EU Horizon 2020 research and innovation programme and from Croatia, France, Germany, Greece, Italy, Netherlands, Portugal, Spain, Sweden, Denmark and Switzerland. The EPI-SGA2 project, PCI2022-132935 is also co-funded by MCIN/AEI /10.13039/501100011033 and by the UE NextGenerationEU/PRTR. Supported by the pre-doctoral program AGAUR-FI ajuts (2024 FI200424) Joan Oró offered by Secretaria d’Universitats i Recerca del Departament de Recerca i Universitats de la Generalitat de Catalunya. Peer Reviewed Postprint (author's final draft) 2024 Conference report https://link.springer.com/chapter/10.1007/978-3-031-85700-3_2 info:eu-repo/grantAgreement/EC/H2020/101036168 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PCI2022-132935/ES/THE EUROPEAN PROCESSOR INITIATIVE (EPI) SGA2/ Open Access Springer