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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. 

How to Position Your Gateway for Failure:The Ten Don’ts of Gateway DesignAbstractScience gateways are accelerators for science and education, providing user-friendly access to powerful computational resources and data analysis tools. Sustained science gateways frameworks such as Hubzero, Tapis, and Galaxy demonstrate the potential for gateways to revolutionize scientific exploration.However, despite initial promise, many gateway projects struggle to transition from prototypes to sustainable, long-term services. Well-intentioned, yet ultimately unsuccessful, gateways are part of the scientific landscape. This raises a critical question: what factors contribute to the demise of science gateways, and how can we avoid these pitfalls to ensure the success of future endeavors?This paper delves into the ten most common pitfalls that lead to science gateway failure. By analyzing these roadblocks, we aim to equip new and developing gateway initiatives with suggestions for long-term success. Our research draws on the collective experiences of numerous gateway projects.We identified critical areas where focused attention and strategic planning are essential. This knowledge will enable the development of good practices that nurture vibrant gateway communities and ensure the long-term sustainability of these valuable research tools. 

Abstract We presentAsterX, a novel open-source, modular, GPU-accelerated, fully general relativistic magnetohydrodynamic (GRMHD) code designed for dynamic spacetimes in 3D Cartesian coordinates, and tailored for exascale computing. We utilize block-structured adaptive mesh refinement (AMR) throughCarpetX, the new driver for theEinstein Toolkit, which is built onAMReX, a software framework for massively parallel applications.AsterXemploys the Valencia formulation for GRMHD, coupled with the ‘Z4c’ formalism for spacetime evolution, while incorporating high resolution shock capturing schemes to accurately handle the hydrodynamics.AsterXhas undergone rigorous testing in both static and dynamic spacetime, demonstrating remarkable accuracy and agreement with other codes in literature. Using subcycling in time, we find an overall performance gain of factor 2.5–4.5. Benchmarking the code through scaling tests on OLCF’s Frontier supercomputer, we demonstrate a weak scaling efficiency of about 67%–77% on 4096 nodes compared to an 8-node performance. 

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. 

Abstract We presentGRaM-X(GeneralRelativisticacceleratedMagnetohydrodynamics 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-Xsupports 3D adaptive mesh refinement (AMR) on GPUs via a new AMR driver for the Einstein Toolkit calledCarpetXwhich in turn leveragesAMReX, 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-Xsupports 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 thatGRaM-Xshows 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. 

We describe JetLag, a Python-based environment that provides access to a distributed, interactive, asynchronous many-task (AMT) computing framework called Phylanx. This environment encompasses the entire computing process, from a Jupyter front-end for managing code and results to the collection and visualization of performance data.We use a Python decorator to access the abstract syntax tree of Python functions and transpile them into a set of C++ data structures which are then executed by the HPX runtime. The environment includes services for sending functions and their arguments to run as jobs on remote resources.A set of Docker and Singularity containers are used to simplify the setup of the JetLag environment. The JetLag system is suitable for a variety of array computational tasks, including machine learning and exploratory data analysis.