Performance of Large Scale MHD Simulation of Global Planetary Magnetosphere with Massively Parallel Scalar Type Supercomputer Including Post Processing

2012 ◽  
Author(s):  
Keiichiro Fukazawa ◽  
Takeshi Nanri
Keyword(s):  
Large Scale ◽  
Massively Parallel ◽  
Post Processing ◽  
Mhd Simulation ◽  
Planetary Magnetosphere ◽  
Scalar Type

scholarly journals Acoustoelectronic nanotweezers enable dynamic and large-scale control of nanomaterials

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Peiran Zhang ◽  
Joseph Rufo ◽  
Chuyi Chen ◽  
Jianping Xia ◽  
Zhenhua Tian ◽  
...  
Keyword(s):  
Large Scale ◽  
Condensed Matter ◽  
Dynamic Control ◽  
Massively Parallel ◽  
Large Field ◽  
Graphene Flakes ◽  
Field Dynamic ◽  
Scale Control ◽  
Orientation Pattern ◽  
Resolution Single

AbstractThe ability to precisely manipulate nano-objects on a large scale can enable the fabrication of materials and devices with tunable optical, electromagnetic, and mechanical properties. However, the dynamic, parallel manipulation of nanoscale colloids and materials remains a significant challenge. Here, we demonstrate acoustoelectronic nanotweezers, which combine the precision and robustness afforded by electronic tweezers with versatility and large-field dynamic control granted by acoustic tweezing techniques, to enable the massively parallel manipulation of sub-100 nm objects with excellent versatility and controllability. Using this approach, we demonstrated the complex patterning of various nanoparticles (e.g., DNAs, exosomes, ~3 nm graphene flakes, ~6 nm quantum dots, ~3.5 nm proteins, and ~1.4 nm dextran), fabricated macroscopic materials with nano-textures, and performed high-resolution, single nanoparticle manipulation. Various nanomanipulation functions, including transportation, concentration, orientation, pattern-overlaying, and sorting, have also been achieved using a simple device configuration. Altogether, acoustoelectronic nanotweezers overcome existing limitations in nano-manipulation and hold great potential for a variety of applications in the fields of electronics, optics, condensed matter physics, metamaterials, and biomedicine.

Massively parallel architectures for large scale neural network simulations

1992 ◽  
Vol 3 (6) ◽  
pp. 876-888 ◽  
Author(s):  
Y. Fujimoto ◽  
N. Fukuda ◽  
T. Akabane
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Parallel Architectures ◽  
Massively Parallel ◽  
Massively Parallel Architectures ◽  
Network Simulations

Crack Growth and Propagation in Metallic Alloys

1995 ◽  
Vol 409 ◽  
Author(s):  
W. C. Morrey ◽  
L. T. Wille
Keyword(s):  
Large Scale ◽  
Critical Strain ◽  
Cleavage Plane ◽  
Crack Tip ◽  
Metallic Alloys ◽  
Propagation Direction ◽  
Dynamics Simulation ◽  
Parallel Computer ◽  
Massively Parallel ◽  
Tip Position

AbstractUsing large-scale molecular dynamics simulation on a massively parallel computer, we have studied the initiation of cracking in a Monel-like alloy of Cu-Ni. In a low temperature 2D sample, fracture from a notch starts at a little beyond 2.5% critical strain when the propagation direction is perpendicular to a cleavage plane. We discuss a method of characterizing crack tip position using a measure of area around the crack tip.

Skill assessment of post-processing methods for ECMWF SEAS5 seasonal forecasts over Europe

2021 ◽  
Author(s):  
Alice Crespi ◽  
Marcello Petitta ◽  
Lucas Grigis ◽  
Paola Marson ◽  
Jean-Michel Soubeyroux ◽  
...  
Keyword(s):  
Large Scale ◽  
Production Management ◽  
Spatial Scales ◽  
Research Field ◽  
Skill Assessment ◽  
Post Processing ◽  
Seasonal Forecasts ◽  
Climate Services ◽  
Local Climate ◽  
Climate Conditions

<p>Seasonal forecasts provide information on climate conditions several months ahead and therefore they could represent a valuable support for decision making, warning systems as well as for the optimization of industry and energy sectors. However, forecast systems can be affected by systematic biases and have horizontal resolutions which are typically coarser than the spatial scales of the practical applications. For this reason, the reliability of forecasts needs to be carefully assessed before applying and interpreting them for specific applications. In addition, the use of post-processing approaches is recommended in order to improve the representativeness of the large-scale predictions of regional and local climate conditions. The development and evaluation downscaling and bias-correction procedures aiming at improving the skills of the forecasts and the quality of derived climate services is currently an open research field. In this context, we evaluated the skills of ECMWF SEAS5 forecasts of monthly mean temperature, total precipitation and wind speed over Europe and we assessed the skill improvements of calibrated predictions.</p><p>For the calibration, we combined a bilinear interpolation and a quantile mapping approach to obtain corrected monthly forecasts on a 0.25°x0.25° grid from the original 1°x1° values. The forecasts were corrected against the reference ERA5 reanalysis over the hindcast period 1993–2016. The processed forecasts were compared over the same domain and period with another calibrated set of ECMWF SEAS5 forecasts obtained by the ADAMONT statistical method.</p><p>The skill assessment was performed by means of both deterministic and probabilistic verification metrics evaluated over seasonal forecasted aggregations for the first lead time. Greater skills of the forecast systems in Europe were generally observed in spring and summer, especially for temperature, with a spatial distribution varying with the seasons. The calibration was proved to effectively correct the model biases for all variables, however the metrics not accounting for bias did not show significant improvements in most cases, and in some areas and seasons even small degradations in skills were observed.</p><p>The presented study supported the activities of the H2020 European project SECLI-FIRM on the improvement of the seasonal forecast applicability for energy production, management and assessment.</p>

scholarly journals Beyond spatial scalability limitations with a massively parallel method for linear oscillatory problems

2017 ◽  
Vol 32 (6) ◽  
pp. 913-933 ◽  
Author(s):  
Martin Schreiber ◽  
Pedro S Peixoto ◽  
Terry Haut ◽  
Beth Wingate
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Weather Prediction ◽  
Finite Difference Methods ◽  
Scaling Limit ◽  
Performance Model ◽  
Massively Parallel ◽  
Large Set ◽  
Problem Size ◽  
Single Node

This paper presents, discusses and analyses a massively parallel-in-time solver for linear oscillatory partial differential equations, which is a key numerical component for evolving weather, ocean, climate and seismic models. The time parallelization in this solver allows us to significantly exceed the computing resources used by parallelization-in-space methods and results in a correspondingly significantly reduced wall-clock time. One of the major difficulties of achieving Exascale performance for weather prediction is that the strong scaling limit – the parallel performance for a fixed problem size with an increasing number of processors – saturates. A main avenue to circumvent this problem is to introduce new numerical techniques that take advantage of time parallelism. In this paper, we use a time-parallel approximation that retains the frequency information of oscillatory problems. This approximation is based on (a) reformulating the original problem into a large set of independent terms and (b) solving each of these terms independently of each other which can now be accomplished on a large number of high-performance computing resources. Our results are conducted on up to 3586 cores for problem sizes with the parallelization-in-space scalability limited already on a single node. We gain significant reductions in the time-to-solution of 118.3× for spectral methods and 1503.0× for finite-difference methods with the parallelization-in-time approach. A developed and calibrated performance model gives the scalability limitations a priori for this new approach and allows us to extrapolate the performance of the method towards large-scale systems. This work has the potential to contribute as a basic building block of parallelization-in-time approaches, with possible major implications in applied areas modelling oscillatory dominated problems.

scholarly journals A general Cluster data and global MHD simulation comparison

2008 ◽  
Vol 26 (11) ◽  
pp. 3411-3428 ◽  
Author(s):  
P. Daum ◽  
M. H. Denton ◽  
J. A. Wild ◽  
M. G. G. T. Taylor ◽  
J. Šafránková ◽  
...  
Keyword(s):  
Large Scale ◽  
Numerical Models ◽  
Mhd Simulation ◽  
Global Context ◽  
Cluster Data ◽  
Verification Methods ◽  
Modelling Studies ◽  
Global Mhd Simulation ◽  
The Many

Abstract. Among the many challenges facing the space weather modelling community today, is the need for validation and verification methods of the numerical models available describing the complex nonlinear Sun-Earth system. Magnetohydrodynamic (MHD) models represent the latest numerical models of this environment and have the unique ability to span the enormous distances present in the magnetosphere, from several hundred kilometres to several thousand kilometres above the Earth's surface. This makes it especially difficult to develop verification and validation methods which posses the same range spans as the models. In this paper we present a first general large-scale comparison between four years (2001–2004) worth of in situ Cluster plasma observations and the corresponding simulated predictions from the coupled Block-Adaptive-Tree-Solarwind-Roe-Upwind-Scheme (BATS-R-US) MHD code. The comparison between the in situ measurements and the model predictions reveals that by systematically constraining the MHD model inflow boundary conditions a good correlation between the in situ observations and the modeled data can be found. These results have an implication for modelling studies addressing also smaller scale features of the magnetosphere. The global MHD simulation can therefore be used to place localised satellite and/or ground-based observations into a global context and fill the gaps left by measurements.

scholarly journals Supercomputer simulations of structure formation in the Universe

2016 ◽  
Vol 12 (S325) ◽  
pp. 10-16
Author(s):  
Tomoaki Ishiyama
Keyword(s):  
Domain Decomposition ◽  
Structure Formation ◽  
Short Range ◽  
Large Scale ◽  
Massively Parallel ◽  
Average Performance ◽  
And Performance ◽  
Performance Results ◽  
The Universe ◽  
Peak Speed

AbstractWe describe the implementation and performance results of our massively parallel MPI†/OpenMP‡ hybrid TreePM code for large-scale cosmological N-body simulations. For domain decomposition, a recursive multi-section algorithm is used and the size of domains are automatically set so that the total calculation time is the same for all processes. We developed a highly-tuned gravity kernel for short-range forces, and a novel communication algorithm for long-range forces. For two trillion particles benchmark simulation, the average performance on the fullsystem of K computer (82,944 nodes, the total number of core is 663,552) is 5.8 Pflops, which corresponds to 55% of the peak speed.

scholarly journals Automatic instance segmentation of mitochondria in electron microscopy data

2021 ◽  
Author(s):  
Luke Nightingale ◽  
Joost de Folter ◽  
Helen Spiers ◽  
Amy Strange ◽  
Lucy M Collinson ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Large Scale ◽  
Machine Learning Algorithms ◽  
Public Repository ◽  
Post Processing ◽  
Human Cortex ◽  
Electron Microscopy Data ◽  
Processing Procedure ◽  
Microscopy Data ◽  
Instance Segmentation

We present a new method for rapid, automated, large-scale 3D mitochondria instance segmentation, developed in response to the ISBI 2021 MitoEM Challenge. In brief, we trained separate machine learning algorithms to predict (1) mitochondria areas and (2) mitochondria boundaries in image volumes acquired from both rat and human cortex with multi-beam scanning electron microscopy. The predictions from these algorithms were combined in a multi-step post-processing procedure, that resulted in high semantic and instance segmentation performance. All code is provided via a public repository.

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