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The most recent Linux kernels have a new feature for securing applications: Landlock. Like Seccomp before it, Landlock makes it possible for a running process to give up access to resources. For applications running as Science Gateways, we want to have network access while starting up MPI, but we want to take away network access prior to the reading of parameter files in order to prevent malicious exploits of the gateway code. We explore the usefulness of this tool by modifying and locking down two mature scientific codes: The Einstein Toolkit, and Octo- Tiger. 

The Einstein Toolkit is a complex software system for numerical general relativity, a science domain that includes colliding black holes, neutron stars, supernovae, etc. As might be expected for a framework of this size and age (parts of it are over 20 years old), there is a significant learning curve to building it, running it, writing new modules for it, etc. Over the years, the Einstein Toolkit maintainers have given a number of tutorials for new users. In recent years, we have created a tutorial server which allows us to streamline the teaching/learning process through the use of Jupyter notebooks and docker images. In this paper we describe the special considerations and adaptations required by the image and the notebook server that enable us to (1) easily make logins and manage accounts which streamlines both the classroom and the independent study experiences, (2) create a simplified but natural user experience for compiling and developing a complex C++ application, (3) scale to increasing class sizes. 

We present GRaM-X (General Relativistic accelerated Magnetohydrodynamics on AMReX), a new GPU-accelerated dynamical-spacetime general relativistic magnetohydrodynamics (GRMHD) code which extends the GRMHD capability of Einstein Toolkit to GPU-based exascale systems. GRaM-X supports 3D adaptive mesh refinement (AMR) on GPUs via a new AMR driver for the Einstein Toolkit called CarpetX which in turn leverages AMReX, an AMR library developed for use by the United States DOE's Exascale Computing Project. We use the Z4c formalism to evolve the Einstein equations and the Valencia formulation to evolve the equations of GRMHD. GRaM-X supports both analytic as well as tabulated equations of state. We implement TVD and WENO reconstruction methods as well as the HLLE Riemann solver. We test the accuracy of the code using a range of tests on static spacetime, e.g. 1D magnetohydrodynamics shocktubes, the 2D magnetic rotor and a cylindrical explosion, as well as on dynamical spacetimes, i.e. the oscillations of a 3D Tolman-Oppenheimer-Volkhof star. We find excellent agreement with analytic results and results of other codes reported in literature. We also perform scaling tests and find that GRaM-X shows a weak scaling efficiency of ∼40%–50% on 2304 nodes (13824 NVIDIA V100 GPUs) with respect to single-node performance on OLCF's supercomputer Summit.