HemoCell at Bull Atos Sample Clauses

HemoCell at Bull Atos. As part of CompBioMed’s Task 2.6, two of CompBioMed’s Core Partners, namely Bull in France, and the University of Amsterdam, in The Netherlands, have been working on the HemoCell application. The goal is to port, profile, optimise and report on the performance results of a set of applications provided by the CompBioMed community. HemoCell has been profiled and tested on several compute nodes (Skylake, Xxxxxxx, Xxxxxxxxx). They also have access to AMD and Intel Xeon Phi compute nodes. In general, part of Bull’s goal is to co-work with CompBioMed core partners on the selected HPC codes and report on the implementations of CompBioMed applications on emerging HPC architectures and porting to new and maturing architectures. The first step is to port the applications on Bull HPC platform, run scalability tests, then perform a profiling of the applications to target the most time-consuming function and loops for optimisations. Finally, the deliverable consists of this report and suggestions for improving the performances of the applications. HemoCell is developed by the University of Amsterdam (UvA) and is a high- performance code which simulates the transport properties of dense cellular suspensions such as blood (Figure 1) below. The blood plasma is modelled by a continuous fluid simulated with an open-source Lattice Boltzmann Method (LBM) solver. On the other hand, the cells are modelled by discrete element method membranes. Both models are coupled using an immersed boundary implementation. Figure 1 Dense flow (left) and white blood cell extension (right) Starting from the portable implementation of HemoCell v1.4, the team members of Task 2.6 took the application and performed scaling test and optimisations on the Intel Skylake compute nodes. The profiling of the application led to targeting the hotspots of the application, where the optimisations need to be directed. In addition to the optimisation suggestions of the application, this report aims at determining the best practices for the implementation of numerical methods on emerging HPC architectures. The main outcome resides in the fact that the HemoCell code consists in an MPI implementation of the Lattice Boltzmann Method of Palabos and intensive I/O through the HDF5 library. The HPC code suffers from a lack of a load balancing method which causes the application to perform poorly. Fortunately, the upcoming version of HemoCell shall include a load balancing technique (Parmetis library). It was noticed th...
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