Materials Cloud is a platform designed to enable open and seamless sharing of resources for computational science, driven by applications in materials modelling. It hosts (1) archival and ...dissemination services for raw and curated data, together with their provenance graph, (2) modelling services and virtual machines, (3) tools for data analytics, and pre-/post-processing, and (4) educational materials. Data is citable and archived persistently, providing a comprehensive embodiment of entire simulation pipelines (calculations performed, codes used, data generated) in the form of graphs that allow retracing and reproducing any computed result. When an AiiDA database is shared on Materials Cloud, peers can browse the interconnected record of simulations, download individual files or the full database, and start their research from the results of the original authors. The infrastructure is agnostic to the specific simulation codes used and can support diverse applications in computational science that transcend its initial materials domain.
Cloud platforms allow users to execute tasks directly from their web browser and are a key enabling technology not only for commerce but also for computational science. Research software is often ...developed by scientists with limited experience in (and time for) user interface design, which can make research software difficult to install and use for novices. When combined with the increasing complexity of scientific workflows (involving many steps and software packages), setting up a computational research environment becomes a major entry barrier. AiiDAlab is a web platform that enables computational scientists to package scientific workflows and computational environments and share them with their collaborators and peers. By leveraging the AiiDA workflow manager and its plugin ecosystem, developers get access to a growing range of simulation codes through a python API, coupled with automatic provenance tracking of simulations for full reproducibility. Computational workflows can be bundled together with user-friendly graphical interfaces and made available through the AiiDAlab app store. Being fully compatible with open-science principles, AiiDAlab provides a complete infrastructure for automated workflows and provenance tracking, where incorporating new capabilities becomes intuitive, requiring only Python knowledge.
The Random Phase Approximation (RPA), which represents the fifth rung of accuracy in Density Functional Theory (DFT), is made practical for large systems. Energies of condensed phase systems ...containing thousands of explicitly correlated electrons and 1500 atoms can now be computed in minutes and less than 1 h, respectively. GPU acceleration is employed for dense and sparse linear algebra, while communication is minimized by a judicious data layout. The performance of the algorithms, implemented in the widely used CP2K simulation package, has been investigated on hybrid Cray XC30 and XK7 architectures, up to 16,384 nodes. Our results emphasize the importance of good network performance, in addition to the availability of GPUs and generous on node memory. A new level of predictivity has thus become available for routine application in Monte Carlo and molecular dynamics simulations.
We push the boundaries of electronic structure-based ab-initio molecular dynamics (AIMD) beyond 100 million atoms. This scale is otherwise barely reachable with classical force-field methods or novel ...neural network and machine learning potentials. We achieve this breakthrough by combining innovations in linear-scaling AIMD, efficient and approximate sparse linear algebra, low and mixed-precision floating-point computation on GPUs, and a compensation scheme for the errors introduced by numerical approximations. The core of our work is the non-orthogonalized local submatrix method (NOLSM), which scales very favorably to massively parallel computing systems and translates large sparse matrix operations into highly parallel, dense matrix operations that are ideally suited to hardware accelerators. We demonstrate that the NOLSM method, which is at the center point of each AIMD step, is able to achieve a sustained performance of 324 PFLOP/s in mixed FP16/FP32 precision corresponding to an efficiency of 67.7% when running on 1536 NVIDIA A100 GPUs.
•Record-sized electronic structure-based ab-initio molecular dynamics simulations are demonstrated.•Simulations with up to 102 million atoms are conducted using the CP2K simulation package.•Novel linear algebra and approximate computing methods are proposed and implemented.•Massive scalability of our submatrix method is demonstrated on 1536 NVIDIA A100 GPUs.•A sustained performance of 324 PFLOP/s (FP16/FP32) is achieved at an efficiency of 67.7%.
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