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The solution within each element is interpolated with a polynomial of usually low order. Again, the unknowns are the solution at the collocation points. The CFD community adopted the FEM in the s when reliable methods for dealing with advection dominated problems were devised. By local we mean that a particular collocation point is affected by a limited number of points around it. In contrast, spectral method have global approximation property. The interpolation functions, either polynomials or trigonomic functions are global in nature.

Their main benefits is in the rate of convergence which depends on the smoothness of the solution i. For infinitely smooth solution, the error decreases exponentially, i. Spectral methods are mostly used in the computations of homogeneous turbulence, and require relatively simple geometries. Atmospheric model have also adopted spectral methods because of their convergence properties and the regular spherical shape of their computational domain. Finite volume methods are primarily used in aerodynamics applications where strong shocks and discontinuities in the solution occur.

Finite volume method solves an integral form of the governing equations so that local continuity property do not have to hold. The CPU time to solve the system of equations differs substantially from method to method. Finite differences are usually the cheapest on a per grid point basis followed by the finite element method and spectral method. However, a per grid point basis comparison is a little like comparing apple and oranges. The coordination of data generation and analysis cannot rely on manual, centralized approaches as it is predominately done today.

In this talk Dr Taufer will discuss how the combination of machine learning and data analytics algorithms, workflow management methods, and high performance computing systems can transition the runtime analysis of larger and larger MD trajectories towards the exascale era. She will show how, by mapping individual substructures to metadata, frame by frame at runtime, it is possible to study the conformational dynamics of proteins in situ. The ensemble of metadata can be used for automatic, strategic analysis and steering of MD simulations within a trajectory or across trajectories, without manually identify those portions of trajectories in which rare events take place or critical conformational features are embedded.

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In this talk Professor Stevens will discuss the convergence of traditional high-performance computing, data analytics and deep learning and some of the architectural, algorithmic and software challenges this convergence creates as we push the envelope on the scale and volume of training and inference runs on todays' largest machines. Deep learning is beginning to have significant impact in science, engineering and medicine. The use of HPC platforms in deep learning ranges from training single models at high speed, training large number of models in sweeps for model development and model discovery, hyper-parameter optimisation, and uncertainty quantification as well as large-scale ensembles for data preparation, inferencing on large-scale data and for data post-processing.

This need for more performance is driving the development of architectures aimed at accelerating deep learning training and inference beyond the already high-performance of GPUs. These new 'AI' architectures are often optimised for common cases in deep learning, typically deep convolutional networks and variations of half-precision floating point.

Professor Stevens will review some of these new accelerator design points, the approaches to acceleration and scalability, and discuss some of the driver science problems in deep learning. Professor Rick Stevens is internationally known for work in high-performance computing, collaboration and visualisation technology, and for building computational tools and web infrastructures to support large-scale genome and metagenome analysis for basic science and infectious disease research. He teaches and supervises students in the areas of computer systems and computational biology, and he co-leads the DOE national laboratory group that has been developing the national initiative for exascale computing.

Despite having been introduced for sampling eigenvalue distributions of ensembles of random matrices, Determinantal Point Processes DPPs have recently been popularised due to their usage in encouraging diversity in recommender systems. Traditional sampling schemes have used dense Hermitian eigensolvers to reduce sampling to an equivalent of a low-rank diagonally-pivoted Cholesky factorization, but researchers are starting to understand deeper connections to Cholesky that avoid the need for spectral decompositions.

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This talk begins with a proof that one can sample a DPP via a trivial tweak of an LDL factorization that flips a Bernoulli coin weighted by each nominal pivot: simply keep an item if the coin lands on heads, or decrement the diagonal entry by one otherwise. The fundamental mechanism is that Schur complement elimination of variables in a DPP kernel matrix generates the kernel matrix of the conditional distribution if said variables are known to be in the sample. While researchers have begun connecting DPP sampling and Cholesky factorization to avoid expensive dense spectral decompositions, high-performance implementations have yet to be explored, even in the dense regime.

The primary contributions of this talk other than the aforementioned theorem are side-by-side implementations and performance results of high-performance dense and sparse-direct DAG-scheduled DPP sampling and LDL factorizations. The software is permissively open sourced as part of the catamari project at gitlab. Jack Poulson is a computational scientist with interests spanning numerical linear algebra, mathematical optimisation, lattice reduction, statistical inference, and differential geometry. As a graduate student at The University of Texas at Austin, he created a hierarchy of open source libraries for distributed-memory linear algebra that culminated in his doctorate on fast solvers for frequency-domain anisotropic wave equations.

He then spent several years developing production machine learning systems as a Senior Research Scientist at Google and is now the owner of a small scientific computing company. Within a decade, the technological underpinnings for the process Gordon Moore described will come to an end as lithography gets down to atomic scale.

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This talk provides an updated view of what a system might look like and the challenges ahead, based on our most recent understanding of technology roadmaps. It also will discuss the tapering of historical improvements in lithography, and how it affects options available to continue scaling of successors to the first exascale machine. For exascale applications under development in the U.

S Department of Energy DOE Exascale Computing Project ECP , nothing could be more apt, with the skin being exascale applications and the game being delivering comprehensive science-based computational applications that effectively exploit exascale HPC technologies to provide breakthrough modelling and simulation and data science solutions. Exascale applications and their companion co-designed computational motifs are a foundational element of the ECP and are the vehicle for delivery of consequential solutions and insight from exascale systems.

The breadth of these applications runs the gamut: chemistry and materials; energy production and transmission; earth and space science; data analytics and optimisation; and national security.


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Each ECP application is focused on targeted development to address a unique mission challenge problem, ie, one that possesses solution amenable to simulation insight, represents a strategic problem important to a DOE mission program, and is currently intractable without the computational power of exascale. Any tangible progress requires close coordination with exascale application, algorithm, and software development to adequately address six key application development challenges: porting to accelerator-based architectures; exposing additional parallelism; coupling codes to create new multi-physics capability; adopting new mathematical approaches; algorithmic or model improvements; and leveraging optimised libraries.

Each ECP application possesses a unique development plan base on its requirements-based combination of physical model enhancements and additions, algorithm innovations and improvements, and software architecture design and implementation. Illustrative examples of these development activities will be given along with results achieved to date on existing DOE supercomputers such as the Summit system at Oak Ridge National Laboratory. Doug is currently the Director of the U. Department of Energy Exascale Computing Project.

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Before coming to ORNL, Doug spent 20 years at Los Alamos National Laboratory, where he held a number of technical and line and program management positions, with a common theme being the development and application of modelling and simulation technologies targeting multi-physics phenomena characterised in part by the presence of compressible or incompressible interfacial fluid flow. Doug also spent one year at Lawrence Livermore National Laboratory in the late s as a physicist in defence sciences. There is now broad recognition within the scientific community that the ongoing deluge of scientific data is fundamentally transforming academic research.

Researchers now need new tools and technologies to manipulate, analyse, visualise, and manage the vast amounts of research data being generated at the national large-scale experimental facilities. In particular, machine learning technologies are fast becoming a pivotal and indispensable component in modern science, from powering discovery of modern materials to helping us handling large-scale imagery data from microscopes and satellites.

Despite these advances, the science community lacks a methodical way of assessing and quantifying different machine learning ecosystems applied to data-intensive scientific applications.

DAA70: Dijkstra Algorithm - Single Source Shortest Path - Greedy Method - Dijkstra algo Example

In this paper, Professor Hey will outline his approach for constructing such a 'SciML benchmark suite' that covers multiple scientific domains and different machine learning challenges. The output of the benchmarks will cover a number of metrics, not only the runtime performance, but also metrics such as energy usage, and training and inference performance. Professor Hey will present some initial results for some of these SciML benchmarks. Tony Hey began his career as a theoretical physicist with a doctorate in particle physics from the University of Oxford in the UK.

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After a career in physics that included research positions at Caltech and CERN, and a professorship at the University of Southampton in England, he became interested in parallel computing and moved into computer science. In the s he was one of the pioneers of distributed memory message-passing computing and co-wrote the first draft of the successful MPI message-passing standard. This covered such unconventional topics as the thermodynamics of computing as well as an outline for a quantum computer. Iterative methods for solving linear algebra problems are ubiquitous throughout scientific and data analysis applications and are often the most expensive computations in large-scale codes.

Approaches to improving performance often involve algorithm modification to reduce data movement or the selective use of lower precision in computationally expensive parts. Such modifications can, however, result in drastically different numerical behavior in terms of convergence rate and accuracy due to finite precision errors. A clear, thorough understanding of how inexact computations affect numerical behaviour is thus imperative in balancing tradeoffs in practical settings.

The CFD community adopted the FEM in the s when reliable methods for dealing with advection dominated problems were devised. By local we mean that a particular collocation point is affected by a limited number of points around it. In contrast, spectral method have global approximation property.

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The interpolation functions, either polynomials or trigonomic functions are global in nature. Their main benefits is in the rate of convergence which depends on the smoothness of the solution i. For infinitely smooth solution, the error decreases exponentially, i. Spectral methods are mostly used in the computations of homogeneous turbulence, and require relatively simple geometries. Atmospheric model have also adopted spectral methods because of their convergence properties and the regular spherical shape of their computational domain. Finite volume methods are primarily used in aerodynamics applications where strong shocks and discontinuities in the solution occur.

Finite volume method solves an integral form of the governing equations so that local continuity property do not have to hold. The CPU time to solve the system of equations differs substantially from method to method. Finite differences are usually the cheapest on a per grid point basis followed by the finite element method and spectral method.

However, a per grid point basis comparison is a little like comparing apple and oranges. The problem becomes one of defining the error measure which is a complicated task in general situations. In order to derive the error committed in the approximation we rely again on Taylor series.