Efficient simulations of patient-specific electrical heart activity on the DGX-2. GPU Technology Conference (GTC) 2020 Silicon Valley: Nvidia, 2020.
Balancing the numerical and parallel performance for reservoir simulations In SIAM Conference on Computational Science and Engineering (CSE19), Spokane, Washington, USA., 2019.
Combining algorithmic rethinking and AVX-512 intrinsics for efficient simulation of subcellular calcium signaling In International Conference on Computational Science (ICCS 2019). Springer, 2019.
Compiling finite element variational forms for GPU-based assembly In FEniCS‘19, Washington DC, USA., 2019.
Heterogeneous computing for cardiac electrophysiology In PREAPP workshop on Efficient Frameworks for Compute- and Data-intensive Computing (EFFECT), University of Tromsø, Norway., 2019.
Performance optimization and modeling of fine-grained irregular communication in UPC." Scientific Programming 2019 (2019): Article ID 6825728."
Towards Detailed Real-Time Simulations of Cardiac Arrhythmia In Computing in Cardiology. Vol. 46. IEEE, 2019.
Towards Detailed Real-Time Simulations of Cardiac Arrhythmia. International Conference in Computing in Cardiology, Singapore, 2019.
Unstructured computational meshes and data locality In Fifth Workshop on Programming Abstractions for Data Locality (PADAL'19), Inria Bordeaux, France., 2019.
Education in HPC and Data Science at Simula Research Lab and UiO In SUPERDATA Workshop on curriculum development, Yunan, China., 2018.
Heterogeneous Computing: Programming, Performance and Applications In CoSaS 2018 Symposium, Erlangen, Germany., 2018.
Memory Bandwidth Contention: Communication vs Computation Tradeoffs in Supercomputers with Multicore Architectures In International Conference on Parallel and Distributed Systems (ICPADS), Edited by Y. Zou. Singapore: ACM/IEEE, 2018.
Quantifying data traffic of sparse matrix-vector multiplication in a multi-level memory hierarchy. London, UK, 2018.
Towards Detailed Organ-Scale Simulations in Cardiac Electrophysiology. International Symposium on Computational Science at Scale (CoSaS), Erlangen, Germany, 2018.
Unstructured mesh partitioning in the presence of strong coefficient heterogeneity In PDESoft 2018 Conference, Bergen, Norway., 2018.
Accelerated high-performance computing for computational cardiac electrophysiology In The University of Tokyo, Tokyo, Japan., 2017.
Automated Translation of MATLAB Code to C++ with Performance and Traceability In The Eleventh International Conference on Advanced Engineering Computing and Applications in Sciences (ADVCOMP 2017), Edited by C. Rückemann and D. Vucinic. International Academy, Research and Industry Association (IARIA), 2017.
Porting Tissue-Scale Cardiac Simulations to the Knights Landing Platform In International Conference on High Performance Computing, Edited by J. Kunkel. Lecture Notes in Computer Science, Springer, 2017.
Accelerating Detailed Tissue-Scale 3D Cardiac Simulations Using Heterogeneous CPU-Xeon Phi Computing." International Journal of Parallel Programming (2016): 1-23."
Enabling Tissue-Scale Cardiac Simulations Using Heterogeneous Computing on Tianhe-2 In IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS), Edited by C. Zhang. ACM/IEEE, 2016.
Matlab2cpp: A Matlab-to-C++ code translator In IEEE 2016 11th System of Systems Engineering Conference (SoSE), Edited by H. P. Dahle. IEEE, 2016.
Panda: A Compiler Framework for Concurrent CPU+GPU Execution of 3D Stencil Computations on GPU-accelerated Supercomputers." International Journal of Parallel Programming (2016)."
On the Performance and Energy Efficiency of the PGAS Programming Model on Multicore Architectures In High Performance Computing & Simulation (2016) - International Workshop on Optimization of Energy Efficient HPC & Distributed Systems, Edited by P. H. Ha. ACM IEEE, 2016.
Solving 3D Time-Fractional Diffusion Equations by High-Performance Parallel Computing." Fractional Calculus and Applied Analysis 19, no. 1 (2016): 140-160."
An Analytical GPU Performance Model for 3D Stencil Computations from the Angle of Data Traffic." The Journal of Supercomputing 71, no. 7 (2015): 2433-2453."