Full Program

DescriptionHeterogeneous computing architectures are unavoidable moving towards the era of exascale computing. Computing nodes are being built with the ever-increasing depth of the hierarchy. Hardware as well as performance portability are key capabilities to make efficient use of them. Particle-in-cell (PIC) methods are the method of choice in computational simulations of many physical applications including but not limited to particle accelerators, nuclear fusion, and astrophysics. Hence, portability in the context of PIC schemes is the need of the hour to carry out these extreme-scale simulations in current and next-generation architectures. This mini-symposium will serve as a platform to discuss the architecture and performance of hardware-independent and, thus, portable frameworks regarding particle and grid computations.

DescriptionSatellite Earth Observations (EO) data have already exceeded the petabyte-scale and are increasingly freely and openly available from different data holdings. This poses a number of Big Data challenges impending their full information potential to be realized. EO Data Cubes (EODC) are a new paradigm revolutionizing the way users can interact with EO data and a promising solution to store, organize, manage and analyze large volumes of EO data. Different implementations are currently operational and are paving the way to broaden the use of EO data to larger communities of users; support decision-makers with timely and actionable information converted into meaningful geophysical variables; and ultimately unlock the information power of EO data. This Minisymposium is aiming to cover the most recent advances in EODC developments from four leading countries on this technology: Australia, Switzerland, Mexico and Armenia.

DescriptionMachine learned potentials are an important tool in materials science and chemistry since they provide a highly accurate and computationally efficient approximation of the potential energy surface. Most of the currently used methods however, are based on a local description of atomic environments and are thus unable to describe effects which take place over long distances beyond the local cutoff, such as charge transfer or a change in the total charge of a system. The established methods can therefore not be applied to systems where such long range effects play an important role i.e. aromatic organic molecules, sp2 hybridized carbon systems or metal clusters adsorbed on doped substrates. This inherent shortcoming has recently gained attention which caused the development of a new generation of methods. In this minisymposium, we will discuss, with prominent experts in the field, the current challenges of incorporating long range interactions into machine learned potentials.

DescriptionSuperfluidity is a generic feature of many quantum systems at low temperatures. It has been experimentally confirmed in condensed matter systems like 3He and 4He liquids, in nuclear systems including nuclei and neutron stars, in both fermionic and bosonic cold atoms in traps. Superfluids exhibit fascinating dynamical properties. Presently the dynamics can be modelled microscopically via a framework based on the time-dependent density functional theory (TDDFT). The superfluid-TDDFT is applicable to a wide range of physical processes involving superfluidity including simulations of nuclear reactions (fission/fusion), modeling the superfluid interior of neutron stars, and dynamics of ultracold atomic gases (quantum turbulence, dynamics of topological excitations). Since superfluidity is an emergent phenomenon, a large number of quantum particles are needed in order to simulate it correctly. It sets high numerical and technical demands for evaluating superfluid-TDDFT with classical computers. This minisymposium will present the most relevant applications of the TDDFT framework achieved with the help of computer systems like Summit (ORNL, USA) and Piz Daint (CSCS, Switzerland) together with the presently utilized numerical and technical solutions. Challenges for future exascale systems in the context of modelling superfluidity/superconductivity will be also highlighted.

DescriptionCP2K is a quantum chemistry and solid state physics software package that can perform atomistic simulations of condensed phase systems. It can simulate the electronic structure and thermodynamic properties of liquids and solutions, complex materials and soft biological systems. CP2K is written in Fortran 2008 and can be run efficiently in parallel using a combination of shared memory multi-threading using OpenMP, distributed memory MPI, and on accellerators using e.g. CUDA. New low-scaling implementations of electronic structure methods enable simulations of systems containing millions of atoms for Density Functional Theory (DFT) and thousands of atoms for Random Phase Approximation (RPA). These methods are based on sparse linear algebra. Performance, portability and ease-of-development is ensured by the accompanying development of a general sparse matrix/tensor library (DBCSR). The desire to perform calculations on a large number of materials of interest is calling for automated workflows to organize massive amounts of data and calculations. This is enabled by combining CP2K with the Automated Interactive Infrastructure and Database for Computational Science (AiiDA). The applicability of CP2K to interesting large-scale problems is demonstrated by a DFT+U study of dislocations in functional oxide materials.

DescriptionThe advent of new supercomputing architectures often challenges the current best practices, programming paradigms and potentially renders state-of-the-art software outdated. Given the research and development man-years spent on scientific applications, this risk should make the HPC community consider more sustainable and long-term development strategies. A case in point is the GPU development, where one must decide carefully the platform to target, e.g., CUDA, OpenCL, ROCm. On the other hand, there exist few examples of software that is agnostic to the underlying architecture, offering the possibility of a single code to deal with multiple architectures. Currently, scientists and engineers often develop their applications for a very specific architecture, spending valuable time optimizing and tailoring their codes. Furthermore, as the code moves to different platforms with different accelerators, the code branches into multiple development streams, where each of them is dealing with its own platform-specific issues that are solved with diverging techniques. Thus, it is critical to open a wide discussion on frameworks, inherently parallel programming languages, compilers, platforms and a combination of them, that will help the community choose a development pipeline, leading to HPC models that favor flexible, versatile and sustainable solutions.

DescriptionMagnetic fusion plasmas are subject to a plethora of collective effects such as electromagnetic waves and instabilities, spanning multiple time- and length- scales. The low collisionality of reactor core plasmas makes a kinetic description mandatory for an accurate representation. Ions and electrons have very different dynamics, and therefore the problem is intrinsically multi-scale, both in space and time. As we go towards the plasma edge, collisionality increases and the relative fluctuation amplitudes become close to unity. Due to the extreme challenges at hand, fusion plasma computations have always exploited the largest HPC resources available at any point in time, and it is anticipated that will remain so in the foreseeable future. Adapting the codes to the ever changing landscape of new and emerging computer architectures is a non-trivial challenge. In this Part I of the minisymposium, the emphasis will be on code developments and software advances in particular for ensuring the efficient exploitation of heterogeneous architectures.

DescriptionSimulations of physical phenomena consume a large fraction of computer time on existing supercomputing resources. Today, the challenge of scaling multiscale simulations is primarily addressed by brute-force search-and-sample techniques and are computationally expensive. Emerging Exascale architectures pose challenges for simulations such as efficient and scalable execution of complex workflows, concurrent execution of heterogeneous tasks and the robustness of algorithms on millions of processing cores, data and I/O parallelism, and fault tolerance. Therefore, incremental approaches that scale simulations will not be successful in achieving the throughput and utilization on such machines. Machine learning (ML) techniques can be integrated with system and application changes and give many orders of magnitude higher effective performance. We term this convergence of high-performance computing (HPC) and ML methodologies/ practice as MLforHPC. Nowhere is the impact of MLforHPC methods likely to be greater than multiscale simulations in biological and material sciences, with early evidence suggesting several orders of magnitude improvement over traditional methods. Fueled by advances in statistical algorithms and runtime systems, ensemble-based methods have overcome some of the limitations of traditional monolithic simulations. Furthermore, integrating ML approaches with such ensemble methods holds even greater promise in overcoming performance barriers and enabling simulations of complex multiscale phenomena.

DescriptionComputational fluid dynamics is one of the main drivers of exascale computing, both due to its high relevance in today's world (from nano fluidics up to planetary flows), but also due to the inherent multiscale properties of turbulence. The numerical treatment is notoriously difficult due to the disparate scales in turbulence, and the need for resolving local features. In addition, aspects such as the quantification of (internal or external) uncertainties is becoming a necessity, together with in-situ visualisation/postprocessing. The recent trend in numerical methods goes towards high-fidelity methods (for instance continuous and discontinuous Galerkin) which are suitable for modern computers; however, relevant issues such as scaling, accelerators and heterogeneous systems, high-order meshing and error control, are still far from solved when it comes to largest scale simulations, e.g. in automotive and aeronautical applications. This two part minisymposium brings together eight experts from various international institutions (Europe, America, Japan) to discuss current and future issues of extreme scale CFD in engineering applications, with special focus on accurate CFD methods, and their implementation on current HPC systems. The interaction between participants of the Horizon2020 Centre of Excellence Excellerat and external experts will be particularly fruitful.

DescriptionThis minisymposium will focus on the tools and the models required to accurately model material behavior under various mechanical stimuli. Continuum-scale models traditionally have difficulty accounting for specific mesoscale deformation behavior due to the larger length scales (tens to hundreds of microns) at which these models are applicable. Accurately modeling fracture in higher length scale models is limited in similar ways; the sub-scale features of interest such as cracks and/or voids, and their interactions along boundaries cannot be resolved. Furthermore, when complex and extreme loading conditions are considered, the active deformation mechanisms can change, impacting overall material strength and damage evolution. Hence, the current state of the art models, particularly those active at larger length scales, cannot accurately predict material behavior especially under dynamic loading conditions. To get around these issues, many multiscale approaches have been developed in which information is ‘passed’ from lower length scales up to higher length scales. While this approach is reasonable, what information is needed, how different models on different length scales connect, and the fidelity of these connections is still not clear. This symposium is aimed at addressing these issues by bringing together modelers who have been working on modeling materials across scales.

DescriptionThe predictive skill of weather and climate models has significantly improved over the past few decades, thanks to a huge increase in resolution facilitated by increased supercomputing capacity. A million-fold increase in computational power has allowed the resolution of operational global weather models to increase from 500 km to 10 km since 1980, for example. Further increases towards 1 km resolution would deliver significant improvements in the skill of weather and climate simulations. However, these simulations are still not viable for operational predictions due to the vast increase in computational cost. The computational speed of global kilometer-scale simulations on today’s supercomputers is below a practical level by at least two orders of magnitude. Furthermore, taking advantage of future exascale supercomputers with heterogeneous architectures will require a substantial rethink of traditional coding paradigms. This two-part minisymposium will bring together researchers on global kilometer-scale atmosphere and ocean models from around the world. Speakers will discuss both the scientific and computational challenges of 1 km resolution. They will present the state-of-the-art of their respective simulation systems and their roadmaps for the future. The challenge of kilometer-scale global simulations can be met, but only by the synthesis of ideas across Earth-System science and supercomputing.

DescriptionFractures are ubiquitous at different scales in the subsurface regions, and can strongly dominate the hydraulic and mechanical response of such regions. Understanding their distribution, connectivity, initiation, and propagation is fundamental for several applications, such as geothermal energy production, hydrocarbon exploration, hydraulic stimulation and induced-seismicity assessment, CO2 storage. Modelling realistic fracture networks introduces several challenges, and, in the literature, several methods have been introduced to handle with the multiscale and multiphysics phenomena underlying the geophysics applications, including phase-field models for fracture initiation and propagation, hydro-mechanical and thermo-hydro-mechano-chemical coupling for fractured poroelastic media. For these kinds of problems, accurate and realistic discretization methods for fractured porous media hence give rise to large-scale problems for which modern high-performance computing architectures, such as hybrid GPU-CPU supercomputers, are necessary for efficient simulations. The goal of this mini-symposium is to bring together applied researchers and computational scientists working on the simulation of fractured porous media, with a particular focus on geoscientific applications. The presentations will be focused on the major challenges of the field and the most recent developments of HPC and large-scale software.

DescriptionWhile parallel applications in all scientific and engineering domains have always been prone to execution inefficiencies that limit their performance and scalability, exascale computer systems comprising millions of heterogeneous processors/cores present a very considerable imminent challenge to be addressed for academia and industry alike. Ten HPC Centres of Excellence are currently funded by the EU Horizon2020 programme to prepare applications for forthcoming exascale computer systems [https://www.focus-coe.eu/index.php/centres-of-excellence-in-hpc-applications/]. The transversal Performance Optimisation and Productivity Centre of Excellence (POP CoE) [https://www.pop-coe.eu] supports the others, along with the wider European community of application developers, with impartial application performance assessments of parallel execution efficiency and scaling based on a solid methodology analysing measurements with open-source performance tools. This minisymposium introduces the POP services and methodology, summarising results provided to date for over 200 customers with particular focus on those from the HPC CoEs. Engagements with the HPC CoEs will be reviewed in the introductory presentation, covering climate and weather (ESiWACE), chemistry and materials (BioExcel/MaX), and computational fluid dynamics in engineering (EXCELLERAT). The CoEs for Computational Biomedicine (CompBioMed [https://www.compbiomed.eu/]), Solid Earth (ChEESE [https://cheese-coe.eu/]) and Energy-oriented applications (EoCoE [https://www.eocoe.eu/]) will then report their experience of collaborating with POP in preparing their flagship codes for exascale.

DescriptionSolving Linear Algebra problems is at the core of many scientific and engineering applications. When the problem size is growing, it is critical to maintaining a reasonable run-time while using the computational resources at full capacity. The need for scalable algorithms is essential in preparing scientific applications for the new exascale ecosystem. Having flexibility and efficiency for the related software framework on supercomputers with hybrid nodes lies at this effort's core. This mini-symposium will address the development of libraries of solvers and preconditioners for the applications related to the current European transition towards renewable energies. In particular, in the context of the "Energy Oriented Center of Excellence: toward exascale for energy" European project on High-Performance Computing and Sustainable Energies.
The Talks will cover topics such as the acceleration and scalability techniques for direct solvers as the Block-Low Rank (BLR) feature or the use of GPUs. Recent developments in Multigrid Solvers, both Algebraic and Geometric type, will be discussed, e.g., the usage of graph matching aggregation schemes and direct distributed coarse solvers with BLR features.
The talks will explore several applications ranging from the simulation of hydrodynamic systems and large wind and material science models to illustrate these approaches' performance.

DescriptionThe path towards exascale computing holds enormous challenges for the community of weather and climate modelling regarding portability, scalability and data management. Exascale machines will allow running Global kilometre-resolving simulations which eventually will enable the representation of features impossible until now. However, the new complexity of the Earth System Models (ESMs) on development could be critical for the new exascale's deployment.
Speakers from the HPC-driven European centre of excellence ESiWACE on exascale computing for weather and climate models will address this matter from different points of view; each speaker will present different challenges faced in their scientific domain and the approaches to mitigate these challenges. Including topics such as 1) the latest results and technical achievements in the context of ESiWACE 2) the computational analysis of the O(1km) global model and challenges presented on the development of the new configurations, 3) the use of PSyclone in LFRic to achieve performance and portability on different architectures and accelerators such as GPUs or 4) the new coupling generation for ESMs on exascale platforms through OASIS3-MCT. All of them pursuing the main goal to prepare the weather and climate community to be able to make use of exascale systems when they become available.

DescriptionData locality on new and future generation platforms is expected to be of paramount importance for good performance. However, many applications in astrophysics are by nature multiphysics, involving multiple solvers with diverse data layouts and communication patterns. Additionally, because they can have a large dynamic range of scales, different resolutions in different parts of the domains are often necessary for computational efficiency. Thus, the constraints placed on data management due to the physics models and their corresponding numerical methods can be at cross purposes with data locality. This mini-symposium will examine constraints on data locality for two major class of astrophysics applications, cosmology and supernovae. For each class we include two types of discretization approaches, adaptive mesh refinement and smooth particle hydrodynamics, that have very different data layouts, access patterns and solver characteristics.

DescriptionResponding to disasters such as wildfires, hurricanes, flooding, earthquakes, tsunamis, winter weather conditions, spread of diseases, and accidents; technological advances are creating exciting new opportunities that have the potential to move HPC well beyond traditional computational workloads. While HPC has a long history of simulating disasters after the fact, an exciting possibility is to use these resources to support emergency, urgent, decision making in real-time. As our ability to capture data continues to grow significantly, it is only now possible to combine high velocity data and live analytics with HPC models to aid in urgently responding to real-world problems, ultimately saving lives and reducing economic loss. To make this vision a reality, a variety of technical and policy challenges must be identified and overcome. Whether it be developing more interactive simulation codes which include real-time data feeds, improving in-situ data analysis techniques, developing new large-scale data visualisation techniques, or guaranteeing bounded and predictable machine queue times, the challenges here are significant. In this minisymposium, we will discuss this emerging HPC use-case by bringing together experts in the field, researchers, practitioners, and interested parties from across our community to identify and tackle issues involved in using HPC for urgent decision making.

DescriptionDeep learning (DL) has the capability to transform many scientific problems, including COVID-19 research, cancer studies, and a wide range of energy sciences. Ever more complex couplings of data sources, analysis, and learning methods demand additional resources in computing capability, data movement, and storage. In practice, the growing scientific usage of DL faces technical and engineering challenges in the exascale era. This minisymposium will illustrate two different learning-based applications and two related systems software contributions to DL at exascale. Exascale challenges for DL include scaling models, scaling workflows, integrating methods, integrating software, and down-scaling results for return to users with varying levels of available computing power. The presenters of this minisymposium are developing a suite of software to support scalable DL on exascale supercomputing resources. The software suite includes capabilities for rapid prototyping and application development, portability across machine types and scales, support for typical DL workflow patterns such as error analysis and hyperparameter optimization, and hierarchical parallelism via distribution of larger models across multiple nodes. We will also present a range of research results in the usage of deep learning ensembles, including recursive search patterns used to look for outliers in experimental data.

DescriptionRecently, hardware manufacturers are responding to an increasing request for low precision functionality such as FP16 by integrating special low-precision functional units, e.g., NVIDIA Tensor cores. These, however, remain unused even for compute-intensive applications if high precision is employed for all arithmetic operations. At the same time, communication-intensive applications suffer from the memory bandwidth of architectures growing at a much slower pace than the arithmetic performance. In both cases, a promising strategy is to abandon the high-precision standard (typically fp64), and employ lower or non-standard precision for arithmetic computations or memory operations whenever possible. While employing formats other than working precision can render attractive performance improvements, it also requires careful consideration of the numerical effects. On the other end of the spectrum, precision formats with higher accuracy than the hardware-supported fp64 can be effective in improving the robustness and accuracy of numerical methods. With this breakout minisymposium, we aim to create a platform where those working with multiprecision or interested in using multiprecision technology come together and share their expertise and experience.