Full Program

DescriptionWHPC is the only international organization working to improve equity, diversity and inclusion in High Performance Computing. The Swiss chapter of WHPC is being formed by senior professionals scientists and engineers working in Switzerland, representing academia, large research centre, national HPC Centre and the IT industry. At PASC22 we hold the second minisymposium dedicated to the chapter of Women in HPC in Switzerland. The first edition of the minisymposium was held virtually last year at PASC21. We hope to have an in-person gathering and networking event with prominent speakers. The mission of WHPC is to "promote, build and leverage a diverse and inclusive HPC workforce by enabling and energising those in the HPC community to increase the participation of women and highlight their contribution to the success of supercomputing. To ensure that women are treated fairly and have equal opportunities to succeed in their chosen HPC career. To ensure everyone understands the benefits of promoting and achieving inclusivity".

DescriptionWith exascale computing capabilities on the horizon, we have seen a transition to more heterogeneous architectures with various accelerators, such as GPUs. While offering high theoretical peak performance and high memory bandwidth, significant programming investments are necessary to exploit these systems efficiently, a challenge that can no longer be ignored with pre and exascale systems like LUMI and Frontier. CFD is a natural driver for exascale computing, with a virtually unbounded need for computational resources for accurate simulation of turbulent fluid flow, both for academic and engineering usage. However, established CFD codes build on years of verification and validation of their underlying numerical methods, potentially preventing a complete rewrite and rendering disruptive code changes a delicate task. Therefore, porting established codes to accelerators poses several interdisciplinary challenges, from formulating suitable numerical methods to applying sound software engineering practices to cope with disruptive code changes. The wide range of topics makes the exascale CFD transition relevant to a broader audience, extending outside the traditional fluid dynamics community. This minisymposium aims at bringing together the CFD community as a whole, from domain scientists to HPC experts, to discuss current and future challenges towards enabling exascale fluid dynamics simulations on anticipated accelerated systems.

DescriptionSince the pivotal work of Geim and Novoselov highlighting the outstanding properties of graphene nanosheets, there has been increasing excitement for structurally confined two-dimensional (2D) materials. These systems have been proven to possess distinctive physical characteristics, compared to the parental 3D analogues, making them very appealing for many technological applications. Still, the symmetry breaking associated with the confinement of the material in two dimensions results in 1) an immense structural and compositional flexibility, whose smart and rational exploration represents an important challenge; 2) the downfall of conventional theoretical models for the description of their properties and in the need for advanced approaches. Computer simulations can have a unique impact in facing these and many other challenges, thanks to the continuous efforts in translating advanced physical models into effective computer codes, together with the availability of cutting-edge computing resources. This minisymposium will focus on the use of computational tools for scientific studies related to the structures and properties of 2D materials. These include the use of artificial intelligence and high-throughput strategies for their discovery or design and large-scale parallel computing for cutting edge, atomistic simulations.

DescriptionMore than ever before, high-performance computing (HPC) is at the forefront of grand scientific challenges that significantly impact public life, ranging from the virulence of the SARS-CoV-2 pandemic to our understanding of the climate in the future with ever-changing weather patterns. Meanwhile, the massive infrastructure projects and sheer expense of high-performance computing systems limit the public's access to the understanding of the methodology and limit the inclusion of global scientists and perspectives. Political and social theorists have long recognized the relationship between inclusion, communication, democratization with public adoption, uptake, and trust. This minisymposium reviews the recent efforts of HPC scientists in disseminating their work through public media and public policy. It will also discuss broader implications for HPC, including how simulation interfaces with public reason and civil society and what a model of global inclusion might look like for HPC.

DescriptionThe central goal of computational geophysics is to infer physical properties of the Earth's interior, inaccessible for direct measurements (such as density or wave velocity), from data typically acquired near the surface with appropriate instruments. Inversion techniques are applied to infer unknown parameters of a mathematical model to fit the observed data. The resulting model provides a view of the current physical state and can be used to simulate previous and future states. Such models lead to complicated nonlinear numerical systems, which are usually too large for standard computers. Luckily, HPC facilities' ever-increasing power allows continuous model resolution and complexity growth. Nevertheless, harnessing this power brings new challenges for implementations. Despite the success of deterministic inversion methods, nonuniqueness and nonlinearity of the inverse problems can reduce the meaningfulness of the solutions. Bayesian inference represents an attractive tool to overcome these issues and provide uncertainty quantification. However, it brings another level of complexity that needs to be re-assessed in light of the recent advances in methodology and technologies. This three-sessions minisymposium aims to bring together scientists engaged in theory, numerical methods, algorithms, and scientific software engineering for scalable numerical modelling, inversion and uncertainty quantification in computational geophysics.

DescriptionThe COVID-19 pandemic put a pause to holding scientific meetings and exchanging scientific ideas. This minisymposium is a timely opportunity to catch up with current developments in method and software development related to computational chemistry and materials sciences, as well as novel applications they are being applied to. The minisymposium will highlight opportunities for scientific discoveries made possible via the emerging synergies between recent advances in high performance computing software and hardware development, method development, big data science and artificial intelligence/machine learning for computational chemistry and materials sciences applications. The workshop will feature three speakers and a discussion panel comprised of speakers and organizers.

DescriptionComputational mechanics simulations offer a valuable analysis tool for engineering applications ranging from design and conceptualization all the way to the operation and maintenance of engineered systems. Such simulations often involve very detailed High-Fidelity Models (HFM) accounting for complex physics, such as failure, nonlinearities and time-dependence, as well as multi-physics interactions. The resulting computational complexity can be prohibitive for several applications. This is particularly true for Digital Twinning applications, where the digital representation is often coupled with monitoring data derived from the physical counterpart of the Twin. In this case, models have to be evaluated repeatedly for reliably updating the digital representation, in certain cases even in real-time. Reduced Order Modelling (ROM) techniques allow to substantially reduce the involved computational cost, while preserving precision. These involve an offline stage, where a ROM is constructed based on information from the HFM, and an online stage where the ROM is deployed. The exploitation of HPC infrastructure is indispensable in both the offline and online phase, for ensuring robust training and real-time estimation capabilities, respectively. In this MS, recent advances in ROM methods, algorithms, and application challenges will be discussed, with a particular focus on exploitation of HPC capabilities and assimilation with data.

DescriptionComputational science projects today consist of numerous steps and methods which involve multiple software projects and may run on various devices, from classical HPC systems to cloud-based services. This makes the workflow itself an integral part of the result, and Workflow Managers one of the main tools computational scientists will have to use in their work. By using proper workflow tools, scientific results can more easily be shared and reproduced, while keeping the process to obtain them flexible and transferable. Together with our speakers we look at the current state of software packages for workflow orchestration and their building blocks to make such an automation possible across supercomputers. We give concrete examples demonstrating how to integrate Machine Learning into scientific workflows in a domain-agnostic way, and show how smart data management in a workflow allows for asynchronous data analysis to improve time to solution.

DescriptionThe next step in climate modeling is so-called "storm-resolving" earth system models (SR-ESMs), which work at kilometer-scale. SR-ESMs make it possible for the first time to directly simulate small-scale processes like the formation of thunderstorms. Emerging exascale high-performance computers have the necessary computing power to carry out such high-resolution simulations for longer simulation times. These mainly heterogeneous hardware architectures pose challenges for all scientific software development efforts, and the ICON weather and climate model is no exception. Several initiatives address these technical challenges in order to exploit the possibilities of km-scale ICON simulations, for example to improve forecast skills or to produce climate information of the highest possible quality. This minisymposium will present the research direction of four of these initiatives: ICON-22, WarmWorld, EXCLAIM, and ICON-C. While the first three are independent projects, they are all tied together through the common ICON model and the need to enable scalable development while ensuring performance portability. These three projects will also contribute to the fourth: the ICON-Consolidated effort will pull together various developments into a consolidated ICON software package. At PASC22, first results of each project will be presented along with their synergies towards the overarching goal of storm-resolving simulations.

DescriptionWith the development of large-scale computing platforms, cellular flows can now be simulated by taking into account the deformations and interactions of individual cells. The continuous increase in computational capacity enabled high-fidelity flow simulations employing detailed cell models to become a valuable complement to experimental studies, giving access to more details and aiding in the design of microfluidic setups. Some of these developments happen in competition, and some individually, around a diverse set of scientific questions. However, the functionality overlap, the limits, and the differences of the proposed methods are not necessarily clear. This minisymposium aims to benefit our community by setting the following goals: 1) to connect the prominent members of the current bleeding edge research in cellular flow simulations and display the state of the art of these simulations 2) to provide a platform for an open forum directed discussion after the presentations to deliberate on the current limitations, necessary validations, overlaps and unique features in functionality, and upcoming targets in the development of these codes.

DescriptionThis minisymposium will explore the physics potential of exascale computing for Lattice Quantum Chromodynamics (QCD) and its impact on our understanding of fundamental particle physics. Novel algorithms and new computing paradigms, in particular machine learning at the level of Monte Carlo integration and in data analysis will be discussed. To fully and efficiently exploit next-generation computing lattice QCD requires significant algorithm development and optimisation – including the development of efficient, resilient sparse solvers, redesigned workflows and data management on highly scalable heterogeneous architectures, including porting and optimisation of codes for accelerators. These challenges are common across computational domains and for the societal grand challenges addressed in the conference theme. Through talks and a panel discussion the minisymposium will elucidate how scientific computing motivated by open questions in fundamental physics leads to computing solutions at exascale that are relevant and timely for many HPC applications.

DescriptionIn the past few years, applications in scientific domains such as weather and forecast have evolved into complex data-centric workflows. This evolution has resulted in a data deluge which has been observed in large computing centers such as the National Energy Research Scientific Computing Center (NERSC), where the volume of data stored increased by a factor of 40 in the last ten years. However, the gap between computing and I/O capabilities continues to grow: the ratio of I/O bandwidth to computing power has decreased by an order of magnitude over the last ten years for the top 3 supercomputers listed in the top500. Faced with this critical situation, storage systems must adapt. In this minisymposium, we will examine the needs of scientific workflows in terms of massive data management through a concrete example. Then, we will explore the solutions proposed by the computer science research community, both from an infrastructure and a execution software stack perspective. We will see how these lines of research can improve data orchestration in scientific workflows where storage is central, and identify which research topics remain to be further developed.

DescriptionExascale computing, artificial intelligence (AI), and data science have the potential to significantly improve predictive capabilities and services across the weather, climate, ocean, and broader environment domains; however, successful application of these technologies requires overcoming many challenges. The World Meteorological Organization (WMO) Research Board commissioned the Task Team on Exascale, AI, and Data to produce whitepapers providing a synthesis of the current state of activity in these areas, as well as recommendations to guide progress and initiate coordinated activity in the WMO community for enabling effective uptake. An overview of the whitepapers and how they fit into the WMO’s long-term strategic plan will be presented. Subsequent talks will provide a deep dive into the whitepapers on exascale computing and data production, and AI and data analysis. Focus will be placed on challenges that must be overcome to realize the benefits of these new technologies, as well as critical gaps between regions with and without access to significant computing resources—and ways to address these gaps. Concrete short-term actions recommended in the whitepapers will be presented. A discussion panel will feature the whitepaper’ authors—representing international weather, climate, and supercomputing centers—providing opportunity for feedback and collaboration with the broader scientific computing community.

DescriptionThis minisymposium focuses on state-of-the-art computational approaches and their validation to explore the growth, dynamics and organization of biological tissues at various scales, from organs to the subcellular level. Recent advances in imaging technologies, algorithms, and computer hardware enable more realistic models of cell and tissue dynamics with subcellular features. Exploiting concepts from high-performance computing and advanced algorithms, computer simulations with high spatial and temporal resolution are becoming accessible. Coupling biomechanical models with chemical signaling or intra- or extracellular fluids and components further enhances the complexity of tissue processes that can be accurately simulated. Collective phenomena involving dozens or hundreds of cells need to be simulated within useful simulation times. In combination with theory development, computational approaches that enable this provide novel insights into the biomechanical principles of cell aggregation and tissue growth and structure, with important implications for our understanding of biological development, morphogenesis and diseases. In this minisymposium, recent advances in numerical high-performance simulations of cellular systems and their experimental validation are discussed.

DescriptionRecent advances in cutting-edge physics applications of kinetic theory in plasmas will be presented. Plasmas are subject to a multitude of collective effects including electromagnetic global modes, instabilities and turbulence, which span several orders of magnitude in space and time scales. Various dynamically reduced approaches, such as gyrokinetics of magnetized plasmas or hybrid fluid-kinetic, have been developed to circumvent some of these difficulties, but even with these reductions the problem remains far from trivial. Thanks to progresses in numerical methods, algorithmic development, massive parallelization schemes enabling the efficient use of the most powerful HPC platforms, numerical simulations have gained increasing levels of realism, making possible scientific application studies nowadays that were previously out of reach. This session represents the first part of the three minisymposia on simulations of plasmas and is dedicated more specifically towards issues related to the efficient implementation of the codes on Exascale architectures. Three talks will be devoted to different Particle-In-Cell codes targeting applications in magnetic fusion and plasma accelerators. The final talk will present semi-Lagrangian simulations on a six-dimensional grid.

DescriptionHigh-energy physics (HEP) experiments have developed millions of lines of code over decades that are optimized to run on traditional x86 CPU systems. However we are seeing a rapidly increasing fraction of floating point computing power in leadership-class computing facilities and traditional data centers coming from new heterogeneous accelerator architectures, such as GPUs. Though the GPU field is currently being led by NVIDIA, other manufacturers such as Intel and AMD are making increasing inroads into this territory, each with their own architecture and compiler languages. Many HEP experiments are also using FPGAs for their frontend detector readouts. Rewriting current CPU based High Energy Physics code for multiple accelerator architectures is not a viable scenario, given the available person power and code maintenance issues. Furthermore, as the number of architectures proliferate, it becomes increasingly onerous to validate the code, and it is vital to ensure that workflows on different hardware produce identical results. Developing portable solutions for HEP codebases to run on multiple heterogeneous architectures is essential. We explore how major HEP experiments are addressing these issues, and also a domain wide investigation to compare the efficacy of the various portability layers such as Kokkos, SYCL, Alpaka, OpenMP, and std::execution::parallel.

DescriptionAnthropogenic climate change and the associated economic damages constitute a substantial negative externality. Economic policy can mitigate this externality and potentially even lead to significant welfare gains across all economic agents. In order to determine the optimal mitigation strategies, economists need to develop quantitative models that produce a realistic link between CO2 emissions and global warming and that are informed by research in climate science as presented in the Intergovernmental Panel on Climate Change (IPCC) reports, that is, the “state-of-the-art” in climate science. One fundamental challenge is that the computational costs for Earth system models are so significant that they are not suitable for studying the two-way feedback between the Earth system and human behavior. Therefore, economic models focusing on this feedback have to rely on a much-simplified representation of the Earth system component. To this end, this minisymposium aims to bring together leading scientists from computational economics, machine learning, and climate science that work at the intersection of coupling the two fields in a sound and tractable way.

DescriptionScientific and technical computing has been facing challenges posed by hardware trends characterized by increasing hardware parallelism, cost of data movement, specialization and heterogeneity. To fully utilize the potential of upcoming hardware, efficient task parallelization frameworks are required. Successful frameworks need to be capable of efficiently expressing fine-grained concurrency, scheduling microsecond-granularity tasks and data movement, and tightly integrate with communication libraries to allow critical-path optimizations in a fully asynchronous schedule. Most of the current tasking frameworks have either struggled to provide low overhead, or have been mainly targeting accelerators, and integration with communication standards like MPI is still an active research area. Molecular dynamics (MD) has been facing these challenges for a decade due to the typically fixed problem size motivating the strong scaling needs, and very high, sub-millisecond, iteration rates. Hence, the experience in this field can be beneficial to the broader community likely to face similar challenges on future exascale architectures. In this session experts in runtimes and developers of flagship MD codes GROMACS and NAMD will to provide a cross-disciplinary view of state-of-the-art in tasking frameworks, APIs, as well as algorithmic and implementation advances made in tackling fine-grained task scheduling challenges while strong-scaling MD on heterogeneous architectures.