High Performance Computing

Medulloblastomas (MBs) are malignant pediatric brain tumors that are molecularly and clinically heterogenous. The application of omics technologies—mainly studying nucleic acids—has significantly improved MB classification and stratification, but treatment options are still unsatisfactory. The proteome and their N-glycans hold the potential to discover clinically relevant phenotypes and targetable pathways. We compile a harmonized proteome dataset of 167 MBs and integrate findings with DNA methylome, transcriptome and N-glycome data. We show six proteome MB subtypes, that can be assigned to two main molecular programs: transcription/translation (pSHHt, pWNT and pG3myc), and synapses/immunological processes (pSHHs, pG3 and pG4). Multiomic analysis reveals different conservation levels of proteome features across MB subtypes at the DNA methylome level. Aggressive pGroup3myc MBs and favorable pWNT MBs are most similar in cluster hierarchies concerning overall proteome patterns but show different protein abundances of the vincristine resistance-associated multiprotein complex TriC/CCT and of N-glycan turnover-associated factors. The N-glycome reflects proteome subtypes and complex-bisecting N-glycans characterize pGroup3myc tumors. Our results shed light on targetable alterations in MB and set a foundation for potential immunotherapies targeting glycan structures.

Radial basis function finite difference (RBF-FD) discretization has recently emerged as an alternative to classical finite difference or finite element discretization of (systems) of partial differential equations. In this paper, we focus on the construction of preconditioners for the iterative solution of the resulting linear systems of equations. In RBF-FD, a higher discretization accuracy may be obtained by increasing the stencil size. This, however, leads to a less sparse and often also worse conditioned stiffness matrix which are both challenges for subsequent iterative solvers. We propose to construct preconditioners based on stiffness matrices resulting from RBF-FD discretization with smaller stencil sizes compared to the one for the actual system to be solved. In our numerical results, we focus on RBF-FD discretizations based on polyharmonic splines (PHS) with polynomial augmentation. We illustrate the performance of smaller stencil preconditioners in the solution of the three-dimensional convection-diffusion equation.

There exists an extensive literature of algorithms for short-range pairwise interactions in particle simulations, however, there is no single algorithm that performs the most optimally in every scenario, motivating the use of auto-tuning to select the optimal pairwise interaction algorithm. Previous efforts to auto-tune Molecular Dynamics have focused on Single-Site Molecular Dynamics, where the computational cost for the intermolecular force calculation is constant. Alternatively, for Multi-Site Molecular Dynamics, the cost of this calculation varies with the number of sites, which, as we show in this paper, can result in different optimal algorithms. Despite this further benefit for auto-tuning, it has yet to be applied to Multi-Site Molecules. In this paper, we introduce an implementation of Multi-Site Molecular Dynamics that is integrated with AutoPas. Using this implementation, we analyse how the relative performance between these algorithms varies as the number of sites varies, for both homogeneous and heterogeneous molecule distributions, and for two different hardware. Furthermore, we demonstrate the advantage of auto-tuning in the context of Multi-Site Molecular Dynamics using the node-level short-range particle simulation library, AutoPas.