2505 Large-scale structural analysis for high-performance blade design

Recently, the use of refractory metal silicides as low resistivity, high temperature and high oxidation resistance gate materials in large scale integrated circuits (LSI) has become an important approach in advanced MOS process development (1). This research is a systematic study on the structure and properties of molybdenum silicide thin film and its applicability to high performance LSI fabrication.

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Neural network assisted large-scale structural analysis/reanalysis in a massively parallel environment

10.2514/6.1994-1512 ◽

1994 ◽

Cited By ~ 1

Author(s):

R. Shieh

Keyword(s):

Neural Network ◽

Structural Analysis ◽

Large Scale ◽

Massively Parallel ◽

Parallel Environment

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RECOMMENDATIONS FOR THE CHOICE OF MILKING INSTALLATIONS IN LOOSE HOUSING SYSTEMS OF COWS

Molochnoe i miasnoe skotovodstvo ◽

10.33943/mms.2020.12.24.001 ◽

2020 ◽

Author(s):

В.В. ГОРДЕЕВ ◽

В.Е. ХАЗАНОВ

Keyword(s):

Dairy Cows ◽

High Performance ◽

Large Scale ◽

Dairy Farms ◽

Economic Indicators ◽

Technical Level ◽

Housing Systems ◽

Working Shift ◽

Technical And Economic Indicators

При выборе типа доильной установки и ее размера необходимо учитывать максимальное планируемое поголовье дойных коров и размер технологической группы, кратность и время одного доения, продолжительность рабочей смены дояров. Анализ технико-экономических показателей наиболее распространенных на сегодняшний день типов доильных установок одинакового технического уровня свидетельствует, что наилучшие удельные показатели имеет установка типа «Карусель» (1), а установка типа «Елочка» (2) требует более высоких затрат труда и средств. Установка «Параллель» (3) занимает промежуточное положение. Из анализа пропускной способности и количества необходимых операторов: установка 2 рекомендована для ферм с поголовьем дойного стада до 600 голов, 3 — не более 1200 дойных коров, 1 — более 1200 дойных коров. «Карусель» — наиболее рациональный, высокопроизводительный, легко автоматизируемый и, следовательно, перспективный способ доения в залах, особенно для крупных молочных ферм. The choice of the proper type and size of milking installations needs to take into account the maximum planned number of dairy cows, the size of a technological group, the number of milkings per day, and the duration of one milking and the operator's working shift. The analysis of technical and economic indicators of currently most common types of milking machines of the same technical level revealed that the Carousel installation had the best specific indicators while the Herringbone installation featured higher labour inputs and cash costs. The Parallel installation was found somewhere in between. In terms of the throughput and the required number of operators Herringbone is recommended for farms with up to 600 dairy cows, Parallel — below 1200 dairy cows, Carousel — above 1200 dairy cows. Carousel was found the most practical, high-performance, easily automated and, therefore, promising milking system for milking parlours, especially on the large-scale dairy farms.

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Faculty Opinions recommendation of Histone recognition and large-scale structural analysis of the human bromodomain family.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.14265963.15778067 ◽

2012 ◽

Author(s):

Cheng-Ming Chiang

Keyword(s):

Structural Analysis ◽

Large Scale

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Statistical and machine learning models for optimizing energy in parallel applications

The International Journal of High Performance Computing Applications ◽

10.1177/1094342019842915 ◽

2019 ◽

Vol 33 (6) ◽

pp. 1079-1097 ◽

Cited By ~ 2

Author(s):

Mark Endrei ◽

Chao Jin ◽

Minh Ngoc Dinh ◽

David Abramson ◽

Heidi Poxon ◽

...

Keyword(s):

Machine Learning ◽

Energy Efficiency ◽

High Performance ◽

Large Scale ◽

Energy Use ◽

Parallel Applications ◽

Learning Models ◽

Trade Off ◽

Time Required ◽

Machine Learning Models

Rising power costs and constraints are driving a growing focus on the energy efficiency of high performance computing systems. The unique characteristics of a particular system and workload and their effect on performance and energy efficiency are typically difficult for application users to assess and to control. Settings for optimum performance and energy efficiency can also diverge, so we need to identify trade-off options that guide a suitable balance between energy use and performance. We present statistical and machine learning models that only require a small number of runs to make accurate Pareto-optimal trade-off predictions using parameters that users can control. We study model training and validation using several parallel kernels and more complex workloads, including Algebraic Multigrid (AMG), Large-scale Atomic Molecular Massively Parallel Simulator, and Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics. We demonstrate that we can train the models using as few as 12 runs, with prediction error of less than 10%. Our AMG results identify trade-off options that provide up to 45% improvement in energy efficiency for around 10% performance loss. We reduce the sample measurement time required for AMG by 90%, from 13 h to 74 min.

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