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Green, C.

Publications and source records attributed to Green, C..

6 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Data-parallel Python for High Energy Physics Analyses

In this paper, we explore features available in Python which are useful for data reduction tasks in High Energy Physics (HEP). Highlevel abstractions in Python are convenient for implementing data reduction tasks. However, in order for such abstractions to be practical, the efficiency of their performance must also be high. Because the data sets we process are typically large, we care about both I/O performance and in-memory processing speed. In particular, we evaluate the use of data-parallel programming, using MPI and numpy, to process a large experimental data set (42 TiB) stored in an HDF5 file. We measure the speed of processing of the data, distinguishing between the time spent reading data and the time spent processing the data in memory, and demonstrate the scalability of both, using up to 1200 KNL nodes (76800 cores) on Cori at NERSC

Paterno, Marc↗
DOE OSTI2017

Python and HPC for High Energy Physics Data Analyses

High level abstractions in Python that can utilize computing hardware well seem to be an attractive option for writing data reduction and analysis tasks. In this paper, we explore the features available in Python which are useful and efficient for end user analysis in High Energy Physics (HEP). A typical vertical slice of an HEP data analysis is somewhat fragmented: the state of the reduction/analysis process must be saved at certain stages to allow for selective reprocessing of only parts of a generally time-consuming workflow. Also, algorithms tend to to be modular because of the heterogeneous nature of most detectors and the need to analyze different parts of the detector separately before combining the information. This fragmentation causes difficulties for interactive data analysis, and as data sets increase in size and complexity (O10 TiB for a “small” neutrino experiment to the O10 PiB currently held by the CMS experiment at the LHC), data analysis methods traditional to the field must evolve to make optimum use of emerging HPC technologies and platforms. Mainstream big data tools, while suggesting a direction in terms of what can be done if an entire data set can be available across a system and analysed with high-level programming abstractions, are not designed with either scientific computing generally, or modern HPC platform features in particular, such as data caching levels, in mind. Our example HPC use case is a search for a new elementary particle which might explain the phenomenon known as “Dark Matter”. Here, using data from the CMS detector, we will use HDF5 as our input data format, and MPI with Python to implement our use case.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2003

Rapid Prototyping of Continuous Fiber Reinforced Ceramic Matrix Composites

For ceramics to be used as structural components in high temperature applications, their fracture toughness is improved by embedding continuous ceramic fibers. Ceramic matrix composite (CMC) materials allow increasing the overall operating temperature, raising the temperature safety margins, avoiding the need for cooling, and improving the damping capacity, while reducing the weight at the same time. They also need to be reliable and available in large quantities as well. In this paper, an innovative rapid prototyping technique to fabricate continuous fiber reinforced ceramic matrix composites is described. The process is simple, robust and will be widely applicable to a number of high temperature material systems. This technique was originally developed at the University of Delaware Center for Composite Materials (UD-CCM) for rapid fabrication of polymer matrix composites by a technique called automated tow placement or ATP. The results of mechanical properties and microstructural characterization are presented, together with examples of complex shapes and parts. It is believed that the process will be able to create complex shaped parts at an order of magnitude lower cost than current chemical vapor infiltration (CVI) and polymer impregnation and pyrolysis (PIP) processes.

Vaidyanathan, R.↗
NASA NTRS2002

Rapid Prototyping of Continuous Fiber Reinforced Ceramic Matrix Composites

For ceramics to be used as structural components in high temperature applications, their fracture toughness is improved by embedding continuous ceramic fibers. Ceramic matrix composite (CMC) materials allow increasing the overall operating temperature, raising the temperature safety margins, avoiding the need for cooling, and improving the damping capacity, while reducing the weight at the same time. They also need to be reliable and available in large quantities as well. In this paper, an innovative rapid prototyping technique to fabricate continuous fiber reinforced ceramic matrix composites is described. The process is simple, robust and will be widely applicable to a number of high temperature material systems. This technique was originally developed at the University of Delaware Center for Composite Materials (UD-CCM) for rapid fabrication of polymer matrix composites by a technique called automated tow placement or ATP. The results of mechanical properties and microstructural characterization are presented, together with examples of complex shapes and parts. It is believed that the process will be able to create complex shaped parts at an order of magnitude lower cost than current CVI and PIP processes.

Vaidyanathan, R.↗
NASA NTRS1971

Vibration manual

Guidelines of the methods and applications used in vibration technology at the MSFC are presented. The purpose of the guidelines is to provide a practical tool for coordination and understanding between industry and government groups concerned with vibration of systems and equipments. Topics covered include measuring, reducing, analyzing, and methods for obtaining simulated environments and formulating vibration specifications. Methods for vibration and shock testing, theoretical aspects of data processing, vibration response analysis, and techniques of designing for vibration are also presented.

Green, C.↗