Index | Title | Authors | Download |
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1 | Leveraging HPC Profiling & Tracing Tools to Understand the Performance of Particle-in-Cell Monte Carlo Simulations | Williams, J. J., Tskhakaya, D., Costea, S., Peng, I. B., Garcia-Gasulla, M., and Markidis, S. | |
2 | Optimizing BIT1, a Particle-in-Cell Monte Carlo Code, with OpenMP/OpenACC and GPU Acceleration | Williams, J. J., Liu, F., Tskhakaya, D., Costea, S., Podolnik, A., and Markidis, S. | |
3 | Enabling High-Throughput Parallel I/O in Particle-in-Cell Monte Carlo Simulations with openPMD and Darshan I/O Monitoring | Williams, J. J., Medeiros, D., Costea, S., Tskhakaya, D., Poeschel, F., Widera, R., Huebl, A., Klasky, S., Podhorszki, N., Kos, L., Podolnik, A., Hromadka, J., Narwal, T., Steiniger, K., Bussmann, M., Laure, E., and Markidis S. | |
4 | Understanding the Impact of openPMD on BIT1, a Particle-in-Cell Monte Carlo Code, through Instrumentation, Monitoring, and In-Situ Analysis | Williams, J. J., Costea, S, Malony, A. D., Tskhakaya, S., Kos, L., Podolnik, A., Hromadka, J., Huck, K., Laure, E., Markidis, S. | |
5 | Understanding Large-Scale Plasma Simulation Challenges for Fusion Energy on Supercomputers | Williams, J.J., Bhole, A., Kierans, D., Hoelzl, M., Holod, I., Tang, W., Tskhakaya, D., Costea, S., Kos, L., Podolnik, A. and Hromadka, J., Team, JOREK, Laure, E., and Markidis, S. | |
6 | Accelerating Particle-in-Cell Monte Carlo Simulations with MPI, OpenMP/OpenACC and Asynchronous Multi-GPU Programming | Williams, J.J., Liu, F., Trilaksono, J., Tskhakaya, D., Costea, S., Kos, L., Podolnik, A., Hromadka, J., Hegde, P., Garcia-Gasulla, M., Seitz, V., Jenko, F., Laure, E., and Markidis, S. | |
7 | MPI Performance Analysis in Vlasiator: Unraveling Communication Bottlenecks | Faj, J., Williams, J.J., Peng, I.B., Ganse, U., Battarbee, M., Pfau-Kempf, Y., Kotipalo, L., Palmroth, M., and Markidis, S. | |
8 | Integration of Modern HPC Performance Tools in Vlasiator for Exascale Analysis and Optimization | Coti, C., Pfau-Kempf, Y., Battarbee, M., Ganse, U., Shende, S., Huck, K., Rodriquez, J., Kotipalo, L., Faj, J., Williams, J.J., Peng, I., Malony, A. D., Markidis, S., and Palmroth M. | |
9 | Characterizing the Performance of the GENE-X Code for Gyrokinetic Turbulence Simulations | Trilaksono, J., Williams, J. J., Ulbl, P., Dannert, T., Laure, E., Markidis, S., and Jenko, F. | |
10 | Porting the grid-based 3D+ 3V hybrid-Vlasov kinetic plasma simulation Vlasiator to heterogeneous GPU architectures | Battarbee, M., Papadakis, K., Ganse, U., Hokkanen, J., Kotipalo, L., Pfau-Kempf, Y., Alho, M. and Palmroth, M. | |
11 | Global evolution of flux transfer events along the magnetopause from the dayside to the far tail | Pfau-Kempf, Y., Papadakis, K., Alho, M., Battarbee, M., Cozzani, G., Pänkäläinen, L., Ganse, U., Kebede, F., Suni, J., Horaites, K., Grandin, M., and Palmroth M. | |
12 | The Vlasiator 5.2 ionosphere – coupling a magnetospheric hybrid-Vlasov simulation with a height-integrated ionosphere model | Ganse, U., Pfau-Kempf, Y., Zhou, H., Juusola, L., Workayehu, A., Kebede, F., Papadakis, K., Grandin, M., Alho, M., Battarbee, M., Dubart, M., Kotipalo, L., Lalagüe, A., Suni, J., Horaites, K., and Palmroth, M. | |
13 | Physics-motivated cell-octree adaptive mesh refinement in the Vlasiator 5.3 global hybrid-Vlasov code | Kotipalo, L., Battarbee, M., Pfau-Kempf, Y., and MPalmroth, M. | |
14 | Harnessing Integrated CPU-GPU System Memory for HPC: a first look into Grace Hopper | Schieffer, G., Wahlgren, J., Ren, J., Faj, J., and Peng, I. | |
15 | On the Rise of AMD Matrix Cores: Performance, Power Efficiency, and Programmability | Schieffer, G., De Medeiros, D. A., Faj, J., Marathe, A., & Peng, I. | |
16 | Understanding Data Movement in AMD Multi-GPU Systems with Infinity Fabric | Schieffer, G., Shi, R., Markidis, S., Herten, A., Faj, J., & Peng, I. | |
17 | Integration of Modern HPC Performance Analysis in Vlasiator for Sustained Exascale | Coti, C., Malony, A. D., Shende, S., Huck, K., Rodriquez, J., Pfau-Kempf, Y., Battarbee, M., Ganse, U. and Palmroth, M. | |
18 | Boosting Performance of Iterative Applications on GPUs: Kernel Batching with CUDA Graphs | Ekelund, J., Markidis, S., & Peng, I. | |
19 | Non-Blocking GPU-CPU Notifications to Enable More GPU-CPU Parallelism | Elis B., Pearce O., Boehme D., Burmark J., and Schulz M. | |
20 | Quantum Computer Simulations at Warp Speed: Assessing the Impact of GPU Acceleration: A Case Study with IBM Qiskit Aer, Nvidia Thrust & cuQuantum | Faj J, Peng I, Wahlgren J, and Markidis S. | |
21 | Programming Quantum Neural Networks on NISQ Systems: An Overview of Technologies and Methodologies | Markidis S. | |
22 | Enabling Quantum Computer Simulations on AMD GPUs: a HIP Backend for Google’s qsim | Markidis S. | |
23 | Dynamic Resource Management for In-Situ Techniques Using MPI-Sessions | Ju, Y., Huber, D., Perez A., Ulbl P., Markidis S., Schlatter P., Schulz M., Schreiber M., and Laure E. | |
24 | A Performance Model of In-Situ Techniques | Ju, Y., Nicolas, V., Perez, A., Gainaru, A., Suter, F., Markidis, S., Schlatter, P., Klasky, S., and Laure, E. | |
25 | Hashinator: a portable hybrid hashmap designed for heterogeneous high performance computing | Papadakis, K., Battarbee, M., Ganse, U., Pfau-Kempf, Y., and Palmroth, M. | |
26 | Streaming Data in HPC Workflows Using ADIOS | Eisenhauer, G., Podhorszki, N., Gainaru A., Klasky, S., Davis, P.E., Parashar, M., Wolf, M., Suchtya. E, Fredj, E., Bolea, V., Pöschel, F., Steiniger, K., Bussmann, M., Pausch, R, and Chandrasekaran, S. |