Publikationer av Måns Andersson
Refereegranskade
Artiklar
[1]
Liu, F., Andersson, M. I., Fredriksson, A. & Markidis, S. (2025). Preconditioned CG and GMRES for interior point methods with applications in radiation therapy. Journal of Computational Science, 90.
[2]
Karp, M., Suarez, E., Meinke, J. H., Andersson, M. I., Schlatter, P., Markidis, S. & Jansson, N. (2024). Experience and analysis of scalable high-fidelity computational fluid dynamics on modular supercomputing architectures. The international journal of high performance computing applications.
[3]
Buckler, A. J., van Wanrooij, M., Andersson, M., Karlof, E., Matic, L. P., Hedin, U. & Gasser, T. C. (2022). Patient-specific biomechanical analysis of atherosclerotic plaques enabled by histologically validated tissue characterization from computed tomography angiography : A case study. Journal of The Mechanical Behavior of Biomedical Materials, 134.
Konferensbidrag
[4]
Pennati, L., Andersson, M. I., Steiniger, K., Widera, R., Narwal, T., Bussmann, M., Markidis, S. (2025). A Parallel and Highly-Portable HPC Poisson Solver: Preconditioned Bi-CGSTAB with alpaka. I 2025 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). (s. 473-483). Institute of Electrical and Electronics Engineers (IEEE).
[5]
Andersson, M., Karp, M., Jansson, N., Markidis, S. (2025). Portable High-Performance Kernel Generation for a Computational Fluid Dynamics Code with DaCe. I Proceedings 31st European Conference on Parallel and Distributed Processing: HETEROPAR 202523RD INTERNATIONAL WORKSHOP. Springer.
[6]
Andersson, M., Liu, F., Markidis, S. (2024). Anderson Accelerated PMHSS for Complex-Symmetric Linear Systems. I 2024 SIAM Conference on Parallel Processing for Scientific Computing, PP 2024. (s. 39-51). Society for Industrial and Applied Mathematics Publications.
[7]
Andersson, M., Karp, M., Markidis, S. (2024). Towards Performance Portable Kernels for Computational Fluid Dynamics Using DaCe. I 53rd International Conference on Parallel Processing, ICPP 2024 - Workshops Proceedings. (s. 110-111). Association for Computing Machinery (ACM).
[8]
Andersson, M., Markidis, S. (2023). A Case Study on DaCe Portability & Performance for Batched Discrete Fourier Transforms. I Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region: 2023. Association for Computing Machinery (ACM).
[9]
Andersson, M., Natarajan Arul, M., Podobas, A., Markidis, S. (2023). Breaking Down the Parallel Performance of GROMACS, a High-Performance Molecular Dynamics Software. I PPAM 2022. Lecture Notes in Computer Science, vol 13826.. (s. 333-345). Springer Nature.
[10]
Liu, F., Andersson, M., Fredriksson, A., Markidis, S. (2023). Distributed Objective Function Evaluation for Optimization of Radiation Therapy Treatment Plans. I PPAM 2022. Lecture Notes in Computer Science, vol 13826.. Springer Nature.
[11]
He, Y., Podobas, A., Andersson, M., Markidis, S. (2023). FFTc: An MLIR Dialect for Developing HPC Fast Fourier Transform Libraries. I Euro-Par 2022: Parallel Processing Workshops: Euro-Par 2022 International Workshops, Glasgow, UK, August 22–26, 2022, Revised Selected Papers. (s. 80-92).
Icke refereegranskade
Avhandlingar
[12]
Andersson, M. (2025). Methods for Solving Large-scale Linear Systems in Scientific Computing : Preconditioners and Performance Portability (Doktorsavhandling , KTH Royal Institute of Technology, Stockholm, TRITA-EECS-AVL 2025:75). Hämtad från https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-368974.
[13]
Andersson, M. (2023). Leveraging Intermediate Representations for High-Performance Portable Discrete Fourier Transform Frameworks : with Application to Molecular Dynamics (Licentiatavhandling , KTH Royal Institute of Technology, Stockholm, TRITA-EECS-AVL 2023:43). Hämtad från https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-326223.
Övriga
[14]
Karp, M., Suarez, E., Meinke, J., Andersson, M., Schlatter, P., Markidis, S., Jansson, N. (). Experience and Analysis of Scalable High-Fidelity Computational Fluid Dynamics on Modular Supercomputing Architectures. (Manuskript).
[15]
He, Y., Andersson, M., Markidis, S. (). Optimizing FDTD Solvers for Electromagnetics : A Compiler-Guided Approach with High-Level Tensor Abstractions. (Manuskript).
Senaste synkning med DiVA:
2025-09-20 23:05:30 UTC