Particle-In-Cell Simulation using Asynchronous Tasking

Recently, task-based programming models have emerged as a prominent alternative among shared-memory parallel programming paradigms. Inherently asynchronous, these models provide native support for dynamic load balancing and incorporate data flow concepts to selectively synchronize the tasks. However, tasking models are yet to be widely adopted by the HPC community and their effective advantages when applied to non-trivial, real-world HPC applications are still not well comprehended. In this paper, we study the parallelization of a production electromagnetic particle-in-cell (EM-PIC) code for kinetic plasma simulations exploring different strategies using asynchronous task-based models. Our fully asynchronous implementation not only significantly outperforms a conventional, synchronous approach but also achieves near perfect scaling for 48 cores.Comment: To be published on the 27th European Conference on Parallel and Distributed Computing (Euro-Par 2021

Exploiting nested task-parallelism in the H-LU factorization

[EN] We address the parallelization of the LU factorization of hierarchical matrices (H-matrices) arising from boundary element methods. Our approach exploits task-parallelism via the OmpSs programming model and runtime, which discovers the data-flow parallelism intrinsic to the operation at execution time, via the analysis of data dependencies based on the memory addresses of the tasks' operands. This is especially challenging for H-matrices, as the structures containing the data vary in dimension during the execution. We tackle this issue by decoupling the data structure from that used to detect dependencies. Furthermore, we leverage the support for weak operands and early release of dependencies, recently introduced in OmpSs-2, to accelerate the execution of parallel codes with nested task-parallelism and fine-grain tasks. As a result, we obtain a significant improvement in the parallel performance with respect to our previous work.The researchers from Universidad Jaume I (UJI) were supported by projects CICYT TIN2014-53495-R and TIN2017-82972-R of MINECO and FEDER; project UJI-B2017-46 of UJI; and the FPU program of MECD.Carratalá-Sáez, R.; Christophersen, S.; Aliaga, JI.; Beltrán, V.; Börm, S.; Quintana Ortí, ES. (2019). Exploiting nested task-parallelism in the H-LU factorization. Journal of Computational Science. 33:20-33. https://doi.org/10.1016/j.jocs.2019.02.004S203333Hackbusch, W. (1999). A Sparse Matrix Arithmetic Based on \Cal H -Matrices. Part I: Introduction to {\Cal H} -Matrices. Computing, 62(2), 89-108. doi:10.1007/s006070050015Grasedyck, L., & Hackbusch, W. (2003). Construction and Arithmetics of H -Matrices. Computing, 70(4), 295-334. doi:10.1007/s00607-003-0019-1Dongarra, J. J., Du Croz, J., Hammarling, S., & Duff, I. S. (1990). A set of level 3 basic linear algebra subprograms. ACM Transactions on Mathematical Software, 16(1), 1-17. doi:10.1145/77626.79170Buttari, A., Langou, J., Kurzak, J., & Dongarra, J. (2009). A class of parallel tiled linear algebra algorithms for multicore architectures. Parallel Computing, 35(1), 38-53. doi:10.1016/j.parco.2008.10.002Quintana-Ortí, G., Quintana-Ortí, E. S., Geijn, R. A. V. D., Zee, F. G. V., & Chan, E. (2009). Programming matrix algorithms-by-blocks for thread-level parallelism. ACM Transactions on Mathematical Software, 36(3), 1-26. doi:10.1145/1527286.1527288Badia, R. M., Herrero, J. R., Labarta, J., Pérez, J. M., Quintana-Ortí, E. S., & Quintana-Ortí, G. (2009). Parallelizing dense and banded linear algebra libraries using SMPSs. Concurrency and Computation: Practice and Experience, 21(18), 2438-2456. doi:10.1002/cpe.1463Aliaga, J. I., Badia, R. M., Barreda, M., Bollhofer, M., & Quintana-Orti, E. S. (2014). Leveraging Task-Parallelism with OmpSs in ILUPACK’s Preconditioned CG Method. 2014 IEEE 26th International Symposium on Computer Architecture and High Performance Computing. doi:10.1109/sbac-pad.2014.24Agullo, E., Buttari, A., Guermouche, A., & Lopez, F. (2016). Implementing Multifrontal Sparse Solvers for Multicore Architectures with Sequential Task Flow Runtime Systems. ACM Transactions on Mathematical Software, 43(2), 1-22. doi:10.1145/2898348Aliaga, J. I., Carratala-Saez, R., Kriemann, R., & Quintana-Orti, E. S. (2017). Task-Parallel LU Factorization of Hierarchical Matrices Using OmpSs. 2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). doi:10.1109/ipdpsw.2017.124The OpenMP API specification for parallel programming, http://www.openmp.org/.OmpSs project home page, http://pm.bsc.es/ompss.Perez, J. M., Beltran, V., Labarta, J., & Ayguade, E. (2017). Improving the Integration of Task Nesting and Dependencies in OpenMP. 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS). doi:10.1109/ipdps.2017.69HLIBpro library home page, https://www.hlibpro.com/.Bempp library home page, https://bempp.com/.HACApK library github repository, https://github.com/hoshino-UTokyo/hacapk-gpu.hmglib library github repository, https://github.com/zaspel/hmglib.HiCMA library github repository, https://github.com/ecrc/hicma.Hackbusch, W., & Börm, S. (2002). -matrix approximation of integral operators by interpolation. Applied Numerical Mathematics, 43(1-2), 129-143. doi:10.1016/s0168-9274(02)00121-