High Performance Computing

Weak scaling results for a fluid solver.
Performance per watt measurements in a parallelization.

In the area of High Performance Computing, a parallel fluid dynamics solver for multi-GPU hardware has been ported to run on up to 36 GPUs with over 70% parallel weak scaling efficiency [Zas15; GZ10; ZG11; ZG13]. Furthermore, multi-GPU parallel uncertainty quantification methods [Zas15; GRZ19], a GPU parallel algebraic multigrid solver [Zas15; Zas], and GPU and multi-GPU parallel matrix approximation methods running on up to 1024 GPUs of the Titan cluster (the former top 1 system) at Oak Ridge National Lab [Zas15; Zas19a; HZ19a] have been developed. Most of the algorithms in machine learning and big data of this group have been also implemented in a (distributed-memory) GPU-/CPU-parallel fashion [Zas16; Zas19b; HZ19b].

Related work

  • [GRZ19] M. Griebel, C. Rieger, and Peter Zaspel. “Kernel-based stochastics collocation for the random two-phase Navier-Stokes equations”. In: International Journal for Uncertainty Quantification, 9(5), 2019, pp. 471–492.
  • [HZ19a] H. Harbrecht and P. Zaspel. A scalable H-matrix approach for the solution of boundary integral equations on multi-GPU clusters. Submitted to Computers & Mathematics with Applications, February 2019; available as arXiv Preprint. 2019.
  • [HZ19b] H. Harbrecht and P. Zaspel. “On the Algebraic Construction of Sparse Multilevel Approximations of Elliptic Tensor Product Problems”. In: Journal of Scientific Computing, 78(2), 2019, pp. 1272–1290.
  • [Zas19a] P. Zaspel. “Algorithmic Patterns for H-Matrices on Many-Core Processors”. In: Journal of Scientific Computing, 78(2), 2019, pp. 1174–1206.
  • [Zas19b] P. Zaspel. “Ensemble Kalman filters for reliability estimation in perfusion inference”. In: International Journal for Uncertainty Quantification, 9(1), 2019, pp. 15–32.
  • [Zas16] P. Zaspel. “Subspace correction methods in algebraic multi-level frames”. In: Linear Algebra and its Applications, 488, 2016, pp. 505–521.
  • [Zas15] P. Zaspel. “Parallel RBF Kernel-Based Stochastic Collocation for Large-Scale Random PDEs”. Dissertation. Institut für Numerische Simulation, Universität Bonn, 2015.
  • [Pfl+14] D. Pflüger et al. “EXAHD: An Exa-scalable Two-Level Sparse Grid Approach for Higher-Dimensional Problems in Plasma Physics and Beyond”. In: Euro-Par 2014: Parallel Processing Workshops. Vol. 8806. Lecture Notes in Computer Science. Springer International Publishing, 2014, pp. 565–576.
  • [ZG13] P. Zaspel and M. Griebel. “Solving incompressible two-phase flows on multi-GPU clusters”. In: Computers & Fluids, 80(0), 2013, pp. 356–364.
  • [ZG11] P. Zaspel and M. Griebel. “Massively Parallel Fluid Simulations on Amazon’s HPC Cloud”. In: First International Symposium on Network Cloud Computing and Applications (NCCA), 2011. 2011, pp. 73–78.
  • [GZ10] M. Griebel and P. Zaspel. “A multi-GPU accelerated solver for the three-dimensional two-phase incompressible Navier-Stokes equations”. In: Computer Science – Research and Development, 25(1–2), 2010, pp. 65–73.
  • [Zas] P. Zaspel. Analysis and parallelization strategies for Ruge-Stüben AMG on many-core processors. Preprint series of the Department of Mathematics and Computer Science, University of Basel, June 2017.