Thommen, Yann 6. Tribin, Ana Trifkovic, Neda Tanasescu, Gabriel 7. Tang, Heiwai Tang, John P. Tieman, Alexander F. Tierney, James Edward 2. Tigre, Robson 3. Truger, Achim Tapp, Stephen 6. Tsagarakis, Konstantinos P. Tarassow, Artur Tarp, Finn Tinn, Katrin Tarverdi, Yashar Taskin, Temel Tavani, Daniele Tavares, Rafael 2. Tobon, Santiago Taylor, John B. Tubeuf, Sandy Todorova, Zdravka K. Toepfer, Ralf 3.
Tcherneva, Pavlina R. Togan Egrican, Asli 3. Tuesta, David Tuijp, Patrick 4. Tedds, Lindsay M. Tulung, Joy Elly 6. Tol, Richard S. Tumen, Semih Tunc, Cengiz Tombe, Trevor Tekin, Erdal Turhan, Ibrahim M. Tomio, Bruno Thiago Tonin, Mirco Tonkin, Richard Pearce 4. Turrini, Alessandro Topcu, Mert Tuy, Hector 1. Tori, Daniele Torre, Dominique This paper focuses on GPU implementation, which overtakes CPU timing by up to a factor 4 while not requiring a big code rewriting effort.
This gain increases with the splines order. Those performances shall enable advanced studies of turbulent transport in magnetic fusion devices. Many of the underlying algorithms were designed before the advent of multi-core processors and are therefore strictly serial. Since the introduction of multi-core platforms these workloads have been parallelized by executing multiple copies of the application.
This approach does not optimally utilize modern hardware. We present two different thread-parallel implementations of a straight-line particle track finding algorithm. This study allows us to better understand the impact of many-core hardware platforms supplementing traditional CPUs in the context of the upcoming LHCb upgrade.
Advanced Computing in Plasma, Particle and Astrophysics on Emerging HPC Architectures Non-traditional computing architectures such as general purpose graphics processing units GPGPUs and many-integrated core accelerators are providing leading edge performance for advanced scientific computing. Given that power costs are becoming more critical, future capability machines are likely to be dominated by these architectures. Non-traditional architectures may require non-traditional programming models, and the scientific community is still learning how to take full advantage of heterogeneous machines with reasonable programming effort.
This difficulty is compounded by the need for sophisticated algorithms to handle the large dynamic ranges encountered in state-of-the-art physics and astrophysics simulations. This minisymposium provides a forum for researchers in the computational plasma, particle physics and astrophysics communities to share their techniques and findings. The presentation and discussion of findings and lessons learned will foment more effective use of these new resources for the advancement of physics and astrophysics.
Kelly Imperial College London, United Kingdom The complexity inherent in the application of advanced numerics on modern hardware to coupled physical systems presents a critical barrier to simulation development. To overcome this, we must create simulation software which embodies the abstraction and composability of the underlying mathematics. In this way, a system is created in which mathematicians, computer scientists, and application specialists can each deploy their own expertise, benefiting from the expertise of the others.
Critically, this approach minimises the extent to which individuals must become polymaths to share in these advances. In this talk I will present Firedrake and PyOP2, a composition of new and existing abstractions which creates a particularly complete separation of concerns.
This enables the creation of high performance, sophisticated finite element models from a very high level mathematical specification and has enabled advances in computer science and numerics, while also facilitating the creation of simulation systems for a variety of applications. First, the effort to adapt the code to new processor architectures is significant compared to their typical release phase.
Second, optimisations for one target often incur performance penalties on others. Third, such codes are generally developed by domain scientists, which typically lack the expertise about specific details of the target platform. Successful projects like STELLA have shown that a way out of this situation is to apply the concept of separation of concerns. GridTools is pushing this concept even further: The domain scientist's work is conducted within a prototyping environment using a domain-specific language DSL , while the computer scientist profiles the automatically-generated code over diverse architectures, implemented by different hardware-specific backends.
This talk will give an overview of the GridTools ecosystem, highlighting the use of the prototyping environment in combination with the automatic-code generation engine. XcalableMP is a directive-based language extension of Fortran95 and C for scientific programming for high-performance distributed memory parallel systems.
Omni Compiler is an infrastructure for source-to-source transformation to design source-to-source compilers such as Omni XcalableMP compiler. In this talk, we will present internals of Omni compiler by taking Omni XcalableMP compiler as a case study, and our future plan. Model codes are growing in complexity and it is difficult to achieve consensus to deliver both high performance with high programmer productivity.
The user is required to specify the high-level application model and provide the stencil-operators while the library provides optimised backend for underlying computational hardware. This helps to detach model developer from implementation details. Production process becomes more straightforward with early deployment to HPC clusters.
Solution of tridiagonal linear systems, typical for implicit schemes such as advection, diffusion and radiation is abundant and performance critical in climate models. We use GridTools library to implement Preconditioned Conjugate Gradient Krylov solver, iterative method efficient for solving sparse linear systems.
We evaluate performance and compare it to other tools such as PETSc. Envisaged applications include grand-challenge simulations in astrophysics and geosciences. Our compute kernels rely on tensor operations - a type of operation scientific computing libraries only support to a limited degree. We demonstrate concepts of how the tensor operations can be reduced to dense matrix-matrix multiplications, which is undoubtedly one of the best optimised operations in linear algebra.
We apply reordering and reshaping techniques, which enables our code generator to exploit existing highly optimised libraries as back end and produce highly optimised compute kernels. As a result, our tool chain provides a "complete solution" for tensor product-based FEM 'operations'. Programming standards like OpenACC have been successfully applied to allow parallelism in existing code to be offloaded efficiently on accelerators.
Achieving optimal performance on various architectures with a single source code is not always possible. Restructuring the code and applying specific architecture optimisation is often needed to increase the performance. In order to help the code transformation and keep a single source code efficient on multiple architectures, we are developing a directive language as well as a tool named CLAW that allow the developer to specify and apply the necessary code transformations to generate both optimal GPU and CPU code from a single Fortran source code.
Code Generation Techniques for HPC Earth Science Applications Earth Science simulations share key characteristics: There is a constant drive to higher resolutions while simultaneously incorporating ever-more sophisticated descriptions of the underlying physical processes. This trend results in a dramatic increase in the computational requirements.
To support this trend, more sophisticated numerical techniques are required along with a drastic increase in computing power from emerging architectures, such as clusters of Graphics Processing Units GPUs.
Both aspects of this duality imply increased programming complexity. Codes targeting multiple architectures either have multiple implementations, or use extensive pre-processing macros controlled by compilation flags. The domain scientist usually has to manage multiple disciplines, including numerical analysis and high performance programming.
The resulting code becomes unreadable for anyone but the developer, meaning that software maintenance is intractable. Drawing on evidence from a wide spectrum of Earth Science applications, we feel it is intuitive that new tools describing numerics and parallelism at a high level of abstraction are needed to meet these challenges of increasing complexity. This minisymposium covers a spectrum of code generation approaches. Their over-arching goal is to separate the concerns of domain science, underlying numerical techniques and high performance computing i.
Such problem-solving frameworks are valuable for the scientist without HPC background who would like to formulate a numerical solution, which then runs optimally on a target architecture. For pre-existing applications written in other languages, such as Fortran, GridTools offers an interfacing layer to take care of data management.
Moreover, a Python environment is presented that can generate GridTools-compliant kernels. Source-to-source compilers, such as Omni, allow for translation of code, and can be used for domain specific extensions of existing languages, such as in the CLAW project. Finally, several use cases and numerical examples, such as the preconditioned conjugate gradient solver or the discontinuous Galerkin method, are present to illustrate the usability of these new tools.
We also address a scalable, localized resolution of identity based implementation of hybrid functionals that offers O N scalability for calculations of approximately 1, atoms system size. To be effective, it is necessary to have software particularly well suited to the hardware as well as a strong interaction with the ab initio application to optimise the amount of data produced and the execution time.
In this talk, I will discuss the recent developments done in ABINIT in order to pave the way towards exascale computation and the python framework we are developing to automate and optimise large workflows on HPC architectures. The next generation of HPC hardware, however, will be quite different from the current Xeon-like hardware: in all probability it will consist either of GPU accelerated nodes or MIC nodes e. Intel's upcoming Knights Landing processors. In recent articles, we presented the linear scaling version of BigDFT code[1], where a minimal set of localized support functions is optimised in situ for systems in various boundary conditions.
We will present how the flexibility of this approach is helpful in providing a basis set that is optimally tuned to the chemical environment surrounding each atom. In addition than providing a basis useful to project Kohn-Sham orbitals informations like atomic charges and partial density of states, it can also be reused as-is, without re-optimisation, for charge-constrained DFT calculations within a fragment approach[2]. We will demonstrate the interest of this approach to express highly precise and efficient calculations of systems in complex environments[3].
Advances in computer power have clearly played a major role, but as important has been the development of new methods that i enhance the scale and the scope of such calculations and ii keep up with current trends in high-performance computing hardware. In this talk, I will outline some aspects of the development of the ONETEP [1] linear-scaling DFT code that enables accurate calculations on tens of thousands of atoms on modern architectures. Such simulations give rise to both opportunities and challenges, which I will try to highlight.
I will then focus on a specific example of the application of ONETEP to a problem that is rather challenging for conventional cubic-scaling DFT due to the large system sizes involved, namely electron transport in carbon nanotube networks.
First-Principles Simulations on Modern and Novel Architectures The predictive power of the so-called ab initio methods, based on the fundamental quantum-mechanical models of matter at the atomic level, together with the growing computational power of high-end High Performance Computing HPC systems, have led to exciting scientific and technological results in Materials Science. The increase of computational power coupled with better numerical techniques open up the possibility to simulate and predict the behaviour of larger and larger atomic systems with a higher degree of accuracy, shortening the path from theoretical results to technological applications, and opening up the possibility to design new materials from scratch.
Despite the elegant simplicity of the formulation of the basic quantum mechanical principles, a practical implementation of a many-particle simulation has to use some approximations and models to be feasible. As there are several options for these approximations, different ab initio simulation codes have been developed, with different trade-offs between precision and computational effort. Each of these codes has its specific strengths and weaknesses, but all together have contributed to making computational materials science one of the domains where supercomputers raise the efficiency of producing scientific know-how and technological innovation.
Indeed, a large fraction of the available workload in supercomputers around the world is spent to perform Computational Materials Science simulations. These codes have mostly kept pace with hardware improvements over the years, by relying on proven libraries and paradigms, such as MPI, that could abstract the developers from low-level considerations while the architectures evolved within a nearly homogeneous model.
In the past few years, however, the emergence of heterogeneous computing elements associated with the transition from peta- to exascale has started to evidence the fragility of this model of development. The aim of the present minisymposium is to gather expert developers from different codes to discuss the challenges of porting, scaling, and optimizing material science application codes for modern and novel platforms. Capsules exist in nature under the form of cells or eggs; artificial microcapsules are widely used in industry to protect active substances, aromas or flavors and control their targeted release.
In most situations, capsules are suspended into another flowing liquid and are subjected to hydrodynamic forces. One robust method to model the three-dimensional fluid-structure interactions consists in coupling a boundary integral method for the internal and external fluid motion with a finite element method for the membrane deformation , which we have shown to be stable and accurate.
We will review how numerical models have provided insights into the dynamics of an ellipsoidal capsule in simple shear flow. We will determine which regimes are mechanically stable and correlate the results with experimental studies on artificial capsules and red blood cells. In the recent years, Large-Eddy Simulation LES has proven to bring significant improvements in the prediction of reacting turbulent flows.
It has been specifically tailored for dealing with very large meshes up to tens of billion cells and for solving efficiently the low-Mach number Navier-Stokes equations on massively parallel computers. The presentation will focus on the high-fidelity combustion LES and the analysis of the huge amount of data generated by these simulations.
Numerical methods used to decouple the different time-scales and to optimise the mesh resolution will also be emphasized. Applications at high Reynolds number, such as high speed liquid-gas flows, and low Reynolds and low Capillary numbers, are discussed. Problems of engineering and physical interest, such as jet atomisation or flow in porous media are investigated with these methods as will be shown. Just few routines have been manually written. Thanks to the increased performance and efficient use of memory, this tool allows for simulations in a range parameter that is unprecedented in Rayleigh-Benard convection.
This method is particularly well-suited for such simulations thanks to its versatility. Besides, some highly-optimised kernels are also implemented for both compute-bound and memory-bound algorithms. High-Performance Computing in Fluid Mechanics I Large-scale computer simulations have become an indispensable tool in fluid dynamics research. Elucidating the fundamental flow physics or designing and optimizing flows for applications ranging all the way from low Reynolds number multiphase flows at small length scales to fully developed turbulence at large scales requires state-of-the art simulation capabilities.
The need for large-scale HPC simulations in fluids dynamics has therefore been driving the development of novel simulation algorithms and of optimized software infrastructure. The envisioned PASC 16 symposium will bring together both developers and users of modern simulation tools. The set of talks will showcase a wide range of fields in which HPC computations are essential, highlight recent advances in computational methods and provide a platform to identify challenges and opportunities for future research.
While efficient methods are available to build and simulate single models, the problem of devising a general approach to integrate heterogeneous models has been studied only recently and is still an open issue. We propose an engineering methodology to automate the process of integrating heterogeneous computational models.
The methodology is based on the novel idea of capturing the relevant information about the different models and their integration strategies by means of meta-data that can be used to automatically generate an efficient integration framework for the specific set of models and interactions.
We discuss the various aspects of the integration problem, highlight the limits of the current solutions and characterize the novel methodology by means of a concrete case study. From a computational viewpoint this makes sense: the representation theorem of PDEs ensures that any data component can be represented as an array. From a software engineering viewpoint this is not so clearcut.
At the higher abstraction level, the array abstraction is not aligned with the concepts of the PDE domain. The effect is a lack of composition and reuse properties, manifest in the need to redevelop code from scratch when equations or assumptions change in unanticipated ways. At the lower level, the array abstraction is tied to a single core, uniform access memory model. The presentation will exemplify some of these problems, and sketch some solutions in the form of more appropriate abstractions.
A potential solution is to generalize the idea of Literate Programming. Our proposed Literate Process will not only generate program documentation and code from the source files, but also other software artifacts, such as the requirements specification, design documentation, and test reports.
Documentation quality will improve because a generator removes the drudgery and errors associated with information duplication and traceability. Using Haskell we have developed a prototype tool, named Drasil, to support this process. The fundamental task for Drasil is managing knowledge through what are termed chunks. A recipe is used to put the chunks together and a generator then interprets the recipes to produce the desired documentation.
An example will be shown for software that simulates the temperature in a solar water heating tank. Computing hardware is evolving at a faster-than-ever pace, the timely software development for these platforms requires exceptionally high productivity.
Developers are often exposed to high-risks design decisions that might irreversibly compromise the software performance on the target platform. In this talk we discuss an approach that attempts to address these issues while identifying its associated costs.
Software Engineering Meets Scientific Computing: Generality, Reusability and Performance for Scientific Software Platforms I: Engineering Methodologies and Development Processes Software platforms for modelling and simulation of scientific problems are becoming increasingly important in many fields and often drive the scientific discovery process.
These platforms present unique requirements in terms of functionalities, performance and scalability, which limit the applicability of consolidated software engineering practices for their design, implementation and validation. For instance, since the effectiveness of a software platform for scientific simulation strictly depends on the level of performance and scalability it can achieve, the design, development and optimization of the platform are usually tailored to the specific hardware architecture the platform is expected to run on.
Similarly, when a scientific simulation requires the integration of multiple software platforms, such integration is typically customized for the specific simulation problem at hand. Because of this, developing and integrating scientific computing platforms demands for a significant amount of relevant knowledge about the modeled domain and the software and hardware infrastructures used for simulation.
This information typically remains hidden in the implementation details of a specific solution and cannot be easily reused to port the simulation to different hardware infrastructures or to implement or integrate different simulation platforms on the same hardware infrastructure. The Software Engineering for Scientific Computing SESC minisymposium is concerned with identifying suitable engineering processes to design, develop, integrate and validate software platforms for scientific modelling and simulations.
This introduces challenges that require the expertise of researchers working in different areas, including computational scientists to model scientific problems, software engineers to propose engineering methodology and HPC experts to analyze platform dependent performance requirements that characterize simulations. The goal of the SESC minisymposium is to bring together software engineers, computational scientists and HPC experts to discuss and advance the engineering practices to implement platforms for scientific computing, aiming to reduce the development time, increase the reusability, the maintainability and the testability of the platforms, while offering the level of performance and scalability that is required by the simulation scenarios at hand.
Specifically, the Software Engineering for Scientific Computing SESC minisymposium aims to address two conflicting requirements in the definition of an effective software development process: 1 promoting generality and reusability of software components, to simplify maintenance, evolution, adaptation and porting of software platforms 2 defining solution that guarantee an adequate level of performance and scalability, which is of paramount importance in scientific simulations.
The SESC minisymposium is organized around two sessions: this first session focuses more specifically on design methodologies and development processes for general and reusable code; the second session Part 2 targets the requirements of performance and scalability in scientific software platforms. Smith Argonne National Laboratory, United States of America Elliptic partial differential equations PDEs frequently arise in continuum descriptions of physical processes relevant to science and engineering.
Multilevel preconditioners represent a family of scalable techniques for solving discrete PDEs of this type and thus are the method of choice for highresolution simulations. The scalability and time-to-solution of massively parallel multilevel preconditioners can be adversely affected by using a coarse-level solver with sub-optimal algorithmic complexity.
To maintain scalability, agglomeration techniques applied to the coarse level have been shown to be necessary. In this work, we present a new software component introduced within the Portable Extensible Toolkit for Scientific computation PETSc which permits agglomeration.
We provide an overview of the design and implementation of this functionality, together with several use cases highlighting the benefits of agglomeration. Lastly, we demonstrate via numerical experiments employing geometric multigrid with structured meshes, the flexibility and performance gains possible using our MPI-rank agglomeration implementation.
Condition number bounds can be theoretically established that are independent of the number of subdomains of the decomposition. Multilevel and highly-scalable algorithms can be obtained by replacing the coarse Cholesky solver with a coarse BDDC preconditioner.
BDDC methods have the remarkable ability to control the condition number, since the coarse space of the preconditioner can be adaptively enriched at the cost of solving local eigenproblems. The specific adaptive technique considered in this paper does not depend upon any interaction of discretization and partition; it relies purely on algebraic operations.
Furthermore, the discussion aims to give interested practitioners sufficient insights to decide whether or not to pursue BDDC in their applications. The weak scalability of the solver is shown on the 3 dimensional linear elasticity problem of a size up to 30 billion of Degrees Of Freedom DOF executed on compute nodes. The strong scalability is evaluated on the problem of size 2.
The results show the super-linear scaling of the single iteration time and linear scalability of the solver runtime. The large scale tests use our own parallel synthetics benchmark generator that is also described in the paper. The solver is based on a finite volume scheme for structured grids and advances the solution using an explicit Runge-Kutta time stepper. The numerical scheme requires the computation of the flux divergence based on an approximate Riemann problem. The computation of the divergence quantity is the most expensive task in the algorithm.
The computational problem is organized in subdomains small enough to be placed into the GPU memory. The compute intensive stencil scheme is offloaded to the GPU accelerator while advancing the solution in time on the CPU. Our method to implement the stencil scheme on the GPU is not limited to applications in fluid dynamics. The focus of this work is on the per-node performance of the heterogeneous solver. In addition, we examine the performance of the solver across compute nodes.
We present simulations for the shock-induced collapse of an aligned row of air bubbles submerged in water using 4 billion cells. Results show a final pressure amplification that is x stronger than the strength of the initial shock. Wall Technical University of Munich, Germany Cardiac electrophysiology simulations are numerically extremely challenging, due to the propagation of the very steep electrochemical wave front during depolarization.
Hence, in classical continuous Galerkin CG approaches, very small temporal and spacial discretisations are necessary to obtain physiological propagation. Until now, spatial discretisations based on discontinuous methods have received little attention for cardiac electrophysiology simulations. Application of such methods, when taking advantage of their parallelity features, would allow a speed-up of the computations.
We also study the effect of the numerical integration of the non-linear ionic current term. Furthermore we plan to show the difference between classic CG methods and HDG methods on large three-dimensional simulations with patient-specific cardiac geometries. Thus, computational models can step in to gain a better understanding of the fluid and structure mechanics of the heart.
We have developed a structural model of the heart muscle, which is coupled to a lumped parameter blood circulation. Doing this, we only focus on relevant processes for the design of heart assist devices acting on the heart's outer surface and therefore reduce computational effort.
In a first step we want to show the influence of deploying force on the pericardium and to what extent the heart's contraction can be supported. In a second step force patches will be optimised toward objectives like cardiac output, contraction pattern or stress inside the heart muscle. The knowledge of how to distribute force on the heart's surface will be fundamental to the assist device design.
For the latter, we consider the mono-domain equations with the Bueno-Orovio ionic model. As for the mechanics, we consider the Holzapfl-Ogden model together with an active strain approach with a transmurally variable activation parameter.
We spatially approximate the model by means of the Finite Element method and discuss the properties of different coupling strategies and time discretization schemes. Among these, we consider a fully coupled strategy with a semi-implicit scheme for the time discretization. We present and discuss numerical results obtained in the HPC framework, including patient-specific left ventricle geometries. For high-risk patients, coronary artery bypass graft is the preferred treatment.
Despite overall excellent patency rates, bypasses may fail due to restenosis. In this context, we present a computational study of the fluid-dynamics in patient-specific geometries with the aim of investigating a possible relationship between coronary stenosis and graft failure. Then, we show some results regarding numerical simulations in patients treated with grafts, in which the degree of coronary stenosis is virtually varied to compare the fluid-dynamics in terms of hemodynamic indices potentially involved in restenosis development.
In capillaries, the oxygen partial pressure PO2 is affected by the individual RBCs that flow in a single file. We have developed a novel overset grid method for oxygen transport from capillaries to tissue. This approach uses moving grids for RBCs and a fixed one for the blood vessels and the tissue. This combination enables accurate modelling of the intravascular PO2 field and the unloading of oxygen from RBCs.
Additionally, our model can account for fluctuations in hematocrit and hemoglobin saturation. Simulations of oxygen transport in the rodent cerebral cortex have been performed and are used to study the cerebral energy metabolism. Other applications include the investigation of hemoglobin saturation heterogeneity in capillary networks. Advanced Computational Methods for Applications to the Cardiovascular System II Cardiac and Cardiovascular Mathematics represents nowadays a challenging topic in view of the emerging and growing collaborations between clinicians and mathematicians.
On the system circulation side, although it has been studied for a longer time, several mathematical and numerical aspects still need to be addressed, as e. With the advancements in network technology and communication library's, new opportunities to explore advanced programming models and load-balancing runtime system in large HPC cluster have emerged. However, it requires deep code modification that cannot be practically applied at full-scale application.
Our first example features unstructured mesh computation using task-based parallelisation. The second demonstrates load-balancing for combustion simulation. Both proto-applications are open-source and can serve to the development of genuine HPC application. We demonstrate a much improved strong scaling down to mesh points per thread on 24, threads on the Intel Xeon Phi.
The threading model we have used is a modified domain decomposition where reads occur across thread domains, while writes are restricted to the thread domain. We present a relaxed synchronization model for communication with a multithreaded gather and scatter of ghost cell regions. We believe that this Proxy application is representative for a much broader class of applications which make use of unstructured meshes and as such will useful to a wider community.
We show trade-offs and performance implications of using the different technologies and explain what works and what does not. The basic ingredients of ACE is a fine granular domain decomposition supplemented by an efficient data dependency driven task scheduling on the underlying, possibly heterogeneous compute resources. As such, it provides combined data and task parallelism. This is complemented on the interprocess level by the one-sided communication primitives of GASPI equipped with lightweight remote completion checks.
This perfectly fits into the concept of data dependency driven execution and allows for perfect overlap of communication by computation. A contiguous stream of computational tasks to the underlying processing units is guaranteed.
The achieved scalability with GPI2. Correspondingly we expect major challenges in the strong-scaling capabilities of the applications. Due to hardware failures and soft errors the number of cores used in a single simulation may vary. Systems are expected to be heterogeneous with respect to compute resources and they are expected to feature a heterogenous memory architecture.
Machine jitter will occur at all scales with a corresponding impact on the relative application performance. Higher resolution and multiphysics simulation will require different parallelization strategies. The number of potential sources for load imbalance hence will significantly increase and the means of sharing data access, communication, and synchronization will have to be reconsidered on all available parallelization levels.
Fortunately, with the advancements in network technology and communication libraries, new opportunities to explore advanced programming models and load-balancing runtime systems in large HPC clusters have emerged. It does this using concepts and implementation techniques that are not yet available in other models e.
This minisymposium will present four talks from different application domains, which make use of hybrid task models and the extended feature set of the GASPI API in order to deliver high scalability and a much improved robustness versus jitter. BPMF is a large scale machine learning application that is able to predict e. Here we consider the prediction of chemical compound activity on systems with millions of items. Distributed work-stealing with GASPI: We present a load-balancing library based on work stealing for large scale runs where we demonstrate a chemistry computation in combustion CFD simulation.
As most aspects of the data assimilation system have improved over the years, this assumption becomes less realistic. There are theoretical benefits in using a weak-constraint formulation and a long assimilation window in 4D-Var and recent experiments have shown benefits in using overlapping assimilation windows even with strong constraint 4D-Var.
The weak constraint formulation writes the optimisation problem as a function of the four dimensional state over the length of the assimilation window. In addition to its theoretical advantages, it increases the potential for parallelisation and better scalability. Using a saddle point method make it possible to take full advantage of the potential for additional parallelism.
We will show how it can benefit future operational systems and reduce the time to solution in the critical path. The aim is in particular to forecast highly precipitating events over the Mediterranean. Yet, progress can be expected from increased use of ensembles in data assimilation, to better describe the background error statistics. A new data assimilation scheme is being developed for AROME, named EnVar, in which background error covariances are directly estimated over an ensemble and localized.
The variational framework is kept in order to allow to assimilate efficiently a wide range of observations. We will show preliminary results and discuss current achievements in the numerical efficiency of the scheme with particular attention to the localization.
Central to the code performance is the implementation of the correlation operator used for modelling of the background error covariance matrix. A new implicit formulation of the diffusion operator has been introduced recently which solves the underlying linear system using the Chebyshev iteration. The technique is more flexible and better suited for massively parallel machines than the method currently used operationally at ECMWF, but further improvements will be necessary for the future high-resolution applications.
This saddle point formulation of 4D-Var allows parallelization in time dimension. Therefore, it represents a crucial step towards higher computational efficiency, since 4D-Var approaches otherwise require many sequential computations. In recent years, there has been increasing interest in saddle point problems which arise in many other applications such as constrained optimisation, computational fluid dynamics, optimal control and so forth.
The key issue of solving saddle point systems with Krylov subspace methods is to find efficient preconditioners. Efficient Data Assimilation for Weather Forecasting on Future Supercomputer Architectures Data assimilation is the process by which the initial condition for weather forecasts is determined. It combines information from a previous forecast background and recent observations of the Earth system, together with estimates of their respective uncertainties, to produce the best estimate of the current state of the system to be used as the initial condition for the forecast.
Uncertainty around that best estimate are now also being produced. Weather forecasting models are well known for having always been at the forefront of high-performance computing HPC. With more and more accurate data assimilation algorithms and more and more observations becoming available, the computational cost of data assimilation has become as high as that of running the forecast.
However, this aspect has not been given as much attention in the HPC community. As for other applications, part of the challenge lie in the efficient use of increasingly complex supercomputer architectures. The fact that forecasting increasingly relies on coupled atmosphere-ocean-waves-land surface models has only just started to be really accounted for in data assimilation.
It opens new perspectives as observations in one part of the system could help improve the estimation of the state in another one. However, that will increase its overall cost and complexity. Data assimilation also poses its own specific challenges, due for example to the volume and very heterogeneous distribution of observations over the globe and their very heterogeneous nature. The minisymposium aims at bringing together experts in data assimilation to expose the challenges posed by data assimilation and some of the current directions of research for addressing those, with a focus on the scalability and efficiency aspects.
Among others, methods such as weak constraint 4D-Var or ensemble variational methods will be presented as they offer more parallelism in the time dimension or in exploring several directions in the space of solutions in parallel. More efficient methods for modelling background errors statistics will also be discussed. Furthermore, we discuss new, scalable uncertainty quantification information methods that allow to quantify the performance of such approximate inference tools in relation to specific quantities of interest, as well as screen the parametric sensitivity of molecular systems.
The output of the network is used as a nonlocal correction to conventional local and semi-local kinetic functionals. We show that this approximation qualitatively reproduces Kohn-Sham potential energy surfaces when used with conventional exchange correlation functionals. The density which minimizes the total energy given by the functional is examined in detail. We identify several avenues to improve on this exploratory work, by reducing numerical noise and changing the structure of our functional.
Finally we examine the features in the density learned by the neural network to anticipate the prospects of generalizing these models. As an alternative, we propose a machine learning approach for the fast prediction of solid-state properties. To achieve this, local spin-density approximation calculations are used as a training set. We focus on predicting the value of the density of electronic states at the Fermi energy.
We find that conventional representations of the input data, such as the Coulomb matrix, are not suitable for the training of learning machines in the case of periodic solids. We propose a novel crystal structure representation for which learning and competitive prediction accuracies become possible within an unrestricted class of spd systems of arbitrary unit-cell size.
This is joint work of K. Schutt, H. Glawe, F. Brockherde, A. Sanna, K. Muller, and E. The flow of the charge is determined by the electrostatic interactions and the local electronegativity of all the atoms. By introducing an atomic environment dependent electronegativity, which is predicted by a neural network, we can reach density functional accuracy at a small fraction of the numerical cost of a full density functional calculation for ionic materials.
Extension to other materials will also be discussed. From Materials' Data to Materials' Insight by Machine Learning The rise of high-throughput computational materials design promises to revolutionize the process of discovery of new materials, and tailoring of their properties. At the same time, by generating the structures of hundreds of thousands of hypothetical compounds, the issue of automated processing of large amounts of materials' data has been made very urgent - to identify structure-property relations, rationalize intuitively the behaviour of materials of increasing complexity, and re-use existing information to accelerate the prediction of properties and accelerate the search of materials' space.
To address this challenge, a strongly interdisciplinary effort has developed, uniting forces among researchers in applied mathematics, computer science, chemistry and materials science, that aims at adapting machine-learning techniques to the specific problems that are encountered when working with materials.
This minisymposium will showcase the most recent developments in this field, and provide a forum for some of the leading figures to discuss the most pressing challenges and the most promising directions. The participants will be selected to represent the many disciplines that are contributing to this endeavour and will cover the following topics: the representation of materials' structures and properties in a synthetic form that is best suited for automated processing, learning of the structure-property relations and circumventing the large computational cost of high-end electronic structure calculations, the identification of outliers and the automatic assessment of the reliability of input data, demonstrative applications to important materials science problems.
In this regime, classical PIC methods are subject to stability constraints on the time and space steps related to the small Larmor radius and plasma frequency. Here, we propose an asymptotic-preserving PIC scheme which is not subjected to these limitations. Our approach is based on first and higher order semi-implicit numerical schemes already validated on dissipative systems.
Additionally, when the magnitude of the external magnetic field becomes large, this method provides a consistent PIC discretization of the guiding-center equation, that is, incompressible Euler equation in vorticity form. We propose several numerical experiments which provide a solid validation of the method and its underlying concepts. Because the scalability potential and scientific impact demonstrated by iPIC3D, it has been selected in many European HPC projects to prepare for the future exascale machines.
In this talk, we present new algorithmic changes to iPIC3D in preparation for the coming exascale era. Our evaluation results show that the performance benefits from this model increases as the scale of simulation increases. In plasma physics the high dimensionality 6D of the problems raises the costs of grid based codes, favouring the mesh free transport with particles. A standard Particle in Cell PIC scheme couples the particle density to a grid based field solver using finite elements.
In this particle mesh coupling the stochastic error appears as noise, while the deterministic error leads to e. Projecting the particles onto a spectral grid yields an energy and momentum conserving, almost sure aliasing free scheme, Particle in Fourier PIF. For few electrostatic modes PIF has very little computational overhead, rendering it suitable for a fast implementation. We present 6D Vlasov-Poisson simulations of Landau damping and a Bump-on-Tail instability and compare the results as well as the computational performance to a grid based semi-Lagrangian solver.
In the struggle to reduce the number of degrees of freedom needed in the Eulerian simulations, several adaptive methods were developed. Adaptive Mesh Refinement technics allow to dynamically adapt the grid in an isotropic way. For higher dimensionality, the authors of adaptive methods tend to favour tensor product structures such as the Tree-of-Tree method [Kolobov], the Sparse Grids [Griebel, Bungartz] or the Tensor-Train method [Oseledets, Kormann].
We propose to discuss and compare their respective advantages and drawbacks. To enable the grid-based solution of the Vlasov equation in 6d phase-space, we need efficient parallelization schemes. In this talk, we consider the 6d Vlasov-Poisson problem discretized by a split-step semi-Lagrangian scheme. This method works with successive 1d interpolations on 1d stripes of the 6d domain.
We consider two parallelization strategies: A remapping strategy that works with two different layouts keeping parts of the dimensions sequential and a classical partitioning into hyper-rectangles. The 1d interpolations can be performed sequentially on each processor for the remapping scheme. On the other hand, the remapping consists in an all-to-all communication pattern. The partitioning only requires localized communication but each 1d interpolation needs to be performed on distributed data.
We compare both parallelization schemes and discuss how to efficiently handle the domain boundaries in the interpolation for partitioning. We implement an efficient from the memory access point of view particle-in-cell method which enables simulations with a large number of particles. We present numerical results for classical Landau damping and Kelvin-Helmholtz test cases.
Code performance is assessed by the observed speedup and attained memory bandwidth. HPC Implementations and Numerics for Kinetic Plasma Models The fundamental model in plasma physics is a kinetic description by a phase-space distribution function solving the Vlasov-Maxwell equation. Due to the complexity of the models, computer simulations are of key importance in understanding the behaviour of plasmas e.
However, kinetic simulations are very challenging due to the relatively high-dimensionality, the presence of multiple scales, and turbulences. For this reason, state-of-the-art plasma solvers mostly discretize simplified models like the gyrokinetic equations. Recent advances in computing power render it possible to approach the full six-dimensional system. The focus of the minisymposium is to bring together researchers developing modern numerical methods and optimized implementation of scalable algorithms for a future generation of plasma codes capable of simulating new physical aspects.
Two types of methods are used in state-of-the-art Vlasov solvers: particle-based and grid-based methods. Especially for high-dimensional models, particle-based methods are many times preferred due to a better scaling with dimensionality. Even though particle-in-cell codes are embarrassingly parallel, care has to be taken in the layout of the memory structure in order to enable fast memory access on high-performance computers.
On the other hand, grid-based methods are known to give accurate results for reduced Vlasov equations in two and four dimensional phase space. Domain partitioning strategies and scalable interpolation algorithms for semi-Lagrangian methods need to be developed.
Mesh refinement can also be used to reduce the number of grid points. Macro-scale properties of the plasma can often be described by a fluid model. Spectral discretization methods have the attractive feature that they reduce the kinetic model to a number of moments - thus incorporating a fluid description of plasmas.
A central aspect of this minisymposium will be the simulation of highly magnetized plasmas. This is the situation for fusion devices based on magnetic confinement fusion, like the ITER project. In this configuration, the particle exhibit a fast circular motion around the magnetic field lines, the so-called gyromotion.
This motion gives rise to multiple scales since turbulences arise on a much slower time scale. Asymptotically preserving schemes can tackle the time scale of the gyromotion beyond the gyrokinetic model. Current models are not only more detailed and accurate but also span across multiple scales and scientific domains. Instead of writing complicated and monolithic models ex novo, we have explored the coupling of existing single-scale and single-science applications in order to produce multi-scale and multi-science models.
We have proposed a theoretical formalism which allows to describe how submodels are coupled Multiscale Modeling Language - MML , as well as a coupling library MUSCLE which allows to build arbitrary workflow from the submodels. Currently, we are exploring the execution of such model across several computing resources, in order to increase available CPU power.
In order to properly deploy an execution across several clusters we have developed a discrete event simulator able to predict the relevance of a given an allocation scheme.
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