118 research outputs found
User Applications Driven by the Community Contribution Framework MPContribs in the Materials Project
This work discusses how the MPContribs framework in the Materials Project
(MP) allows user-contributed data to be shown and analyzed alongside the core
MP database. The Materials Project is a searchable database of electronic
structure properties of over 65,000 bulk solid materials that is accessible
through a web-based science-gateway. We describe the motivation for enabling
user contributions to the materials data and present the framework's features
and challenges in the context of two real applications. These use-cases
illustrate how scientific collaborations can build applications with their own
"user-contributed" data using MPContribs. The Nanoporous Materials Explorer
application provides a unique search interface to a novel dataset of hundreds
of thousands of materials, each with tables of user-contributed values related
to material adsorption and density at varying temperature and pressure. The
Unified Theoretical and Experimental x-ray Spectroscopy application discusses a
full workflow for the association, dissemination and combined analyses of
experimental data from the Advanced Light Source with MP's theoretical core
data, using MPContribs tools for data formatting, management and exploration.
The capabilities being developed for these collaborations are serving as the
model for how new materials data can be incorporated into the Materials Project
website with minimal staff overhead while giving powerful tools for data search
and display to the user community.Comment: 12 pages, 5 figures, Proceedings of 10th Gateway Computing
Environments Workshop (2015), to be published in "Concurrency in Computation:
Practice and Experience
MEADWORKS – HYDROLOGY, ECOLOGY, MEAD AND ARCHITECTURE
This thesis seeks to redefine the relationship between communities and water infrastructure through a scalable and adaptable hybrid architectural solution. By focusing on the ambiguous intersection of nature and the built environment, this thesis will make an attempt at place-making in a setting typically disregarded by cities and communities. Challenging the boundaries of public infrastructure, architecture, and landscape architecture, this thesis will provide a dynamic solution to the water pollution epidemic of the Chesapeake Bay that involves subliminal community awareness and engagement. Through the program of a meadery, beekeeping, agriculture, and brewing will integrate with water treatment infrastructure to mutually benefit all processes
Asynchronous Execution of Python Code on Task Based Runtime Systems
Despite advancements in the areas of parallel and distributed computing, the
complexity of programming on High Performance Computing (HPC) resources has
deterred many domain experts, especially in the areas of machine learning and
artificial intelligence (AI), from utilizing performance benefits of such
systems. Researchers and scientists favor high-productivity languages to avoid
the inconvenience of programming in low-level languages and costs of acquiring
the necessary skills required for programming at this level. In recent years,
Python, with the support of linear algebra libraries like NumPy, has gained
popularity despite facing limitations which prevent this code from distributed
runs. Here we present a solution which maintains both high level programming
abstractions as well as parallel and distributed efficiency. Phylanx, is an
asynchronous array processing toolkit which transforms Python and NumPy
operations into code which can be executed in parallel on HPC resources by
mapping Python and NumPy functions and variables into a dependency tree
executed by HPX, a general purpose, parallel, task-based runtime system written
in C++. Phylanx additionally provides introspection and visualization
capabilities for debugging and performance analysis. We have tested the
foundations of our approach by comparing our implementation of widely used
machine learning algorithms to accepted NumPy standards
A universal equivariant graph neural network for the elasticity tensors of any crystal system
The elasticity tensor that describes the elastic response of a material to
external forces is among the most fundamental properties of materials. The
availability of full elasticity tensors for inorganic crystalline compounds,
however, is limited due to experimental and computational challenges. Here, we
report the materials tensor (MatTen) model for rapid and accurate estimation of
the full fourth-rank elasticity tensors of crystals. Based on equivariant graph
neural networks, MatTen satisfies the two essential requirements for elasticity
tensors: independence of the frame of reference and preservation of material
symmetry. Consequently, it provides a universal treatment of elasticity tensors
for all crystal systems across diverse chemical spaces. MatTen was trained on a
dataset of first-principles elasticity tensors garnered by the Materials
Project over the past several years (we are releasing the data herein) and has
broad applications in predicting the isotropic elastic properties of
polycrystalline materials, examining the anisotropic behavior of single
crystals, and discovering new materials with exceptional mechanical properties.
Using MatTen, we have discovered a hundred new crystals with extremely large
maximum directional Young's modulus and eleven polymorphs of elemental cubic
metals with unconventional spatial orientation of Young's modulus
Simulating Stellar Merger using HPX/Kokkos on A64FX on Supercomputer Fugaku
The increasing availability of machines relying on non-GPU architectures,
such as ARM A64FX in high-performance computing, provides a set of interesting
challenges to application developers. In addition to requiring code portability
across different parallelization schemes, programs targeting these
architectures have to be highly adaptable in terms of compute kernel sizes to
accommodate different execution characteristics for various heterogeneous
workloads. In this paper, we demonstrate an approach to code and performance
portability that is based entirely on established standards in the industry. In
addition to applying Kokkos as an abstraction over the execution of compute
kernels on different heterogeneous execution environments, we show that the use
of standard C++ constructs as exposed by the HPX runtime system enables superb
portability in terms of code and performance based on the real-world Octo-Tiger
astrophysics application. We report our experience with porting Octo-Tiger to
the ARM A64FX architecture provided by Stony Brook's Ookami and Riken's
Supercomputer Fugaku and compare the resulting performance with that achieved
on well established GPU-oriented HPC machines such as ORNL's Summit, NERSC's
Perlmutter and CSCS's Piz Daint systems. Octo-Tiger scaled well on
Supercomputer Fugaku without any major code changes due to the abstraction
levels provided by HPX and Kokkos. Adding vectorization support for ARM's SVE
to Octo-Tiger was trivial thanks to using standard C+
A representation-independent electronic charge density database for crystalline materials
In addition to being the core quantity in density functional theory, the
charge density can be used in many tertiary analyses in materials sciences from
bonding to assigning charge to specific atoms. The charge density is data-rich
since it contains information about all the electrons in the system. With
increasing utilization of machine-learning tools in materials sciences, a
data-rich object like the charge density can be utilized in a wide range of
applications. The database presented here provides a modern and user-friendly
interface for a large and continuously updated collection of charge densities
as part of the Materials Project. In addition to the charge density data, we
provide the theory and code for changing the representation of the charge
density which should enable more advanced machine-learning studies for the
broader community
Deutsche Bahn Schedules Train Rotations Using Hypergraph Optimization
Deutsche Bahn (DB) operates a large fleet of rolling stock (locomotives, wagons, and train sets) that must be combined into trains to perform rolling stock rotations. This train composition is a special characteristic of railway operations that distinguishes rolling stock rotation planning from the vehicle scheduling problems prevalent in other industries. DB models train compositions using hyperarcs. The resulting hypergraph models are addressed using a novel coarse-to-fine method that implements a hierarchical column generation over three levels of detail. This algorithm is the mathematical core of DB's fleet employment optimization (FEO) system for rolling stock rotation planning. FEO's impact within DB's planning departments has been revolutionary. DB has used it to support the company's procurements of its newest high-speed passenger train fleet and its intermodal cargo locomotive fleet for crossborder operations. FEO is the key to successful tendering in regional transport and to construction site management in daily operations. DB's planning departments appreciate FEO's high-quality results, ability to reoptimize (quickly), and ease of use. Both employees and customers benefit from the increased regularity of operations. DB attributes annual savings of 74 million euro, an annual reduction of 34,000 tons of CO2 emissions, and the elimination of 600 coupling operations in crossborder operations to the implementation of FEO
From Piz Daint to the Stars: Simulation of Stellar Mergers using High-Level Abstractions
We study the simulation of stellar mergers, which requires complex
simulations with high computational demands. We have developed Octo-Tiger, a
finite volume grid-based hydrodynamics simulation code with Adaptive Mesh
Refinement which is unique in conserving both linear and angular momentum to
machine precision. To face the challenge of increasingly complex, diverse, and
heterogeneous HPC systems, Octo-Tiger relies on high-level programming
abstractions.
We use HPX with its futurization capabilities to ensure scalability both
between nodes and within, and present first results replacing MPI with
libfabric achieving up to a 2.8x speedup. We extend Octo-Tiger to heterogeneous
GPU-accelerated supercomputers, demonstrating node-level performance and
portability. We show scalability up to full system runs on Piz Daint. For the
scenario's maximum resolution, the compute-critical parts (hydrodynamics and
gravity) achieve 68.1% parallel efficiency at 2048 nodes.Comment: Accepted at SC1
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