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Register a new userPhysics data management tools: computational evolutions and benchmarks
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Mincheol Han
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The development of a package for the management of physics data is described: its design, implementation and computational benchmarks. This package improves the data management tools originally developed for Geant4 physics models based on the EADL, EEDL and EPDL97 data libraries. The implementation exploits recent evolutions of the C++ libraries appearing in the C++0x draft, which are intended for inclusion in the next C++ ISO Standard. The new tools improve the computational performance of physics data management.
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The development of a package for the management of physics data is described: its design, implementation and computational benchmarks. This package improves the data management tools originally developed for Geant4 physics models based on the EADL, EEDL and EPDL97 data libraries. The implementation exploits recent evolutions of the C++ libraries appearing in the C++0x draft, which are intended for inclusion in the next C++ ISO Standard. The new tools improve the computational performance of physics data management.
This paper summarizes the current status of the electromagnetic data libraries, reviews recent experimental validation results, highlights open issues and introduces new perspectives for the future of these data libraries taking shape in the context of INFN research. Special emphasis is given to the characteristics of reliability, transparency and openness, along with opportunities for the improvement and the extension of the physics content.
Computing plays an essential role in all aspects of high energy physics. As computational technology evolves rapidly in new directions, and data throughput and volume continue to follow a steep trend-line, it is important for the HEP community to develop an effective response to a series of expected challenges. In order to help shape the desired response, the HEP Forum for Computational Excellence (HEP-FCE) initiated a roadmap planning activity with two key overlapping drivers -- 1) software effectiveness, and 2) infrastructure and expertise advancement. The HEP-FCE formed three working groups, 1) Applications Software, 2) Software Libraries and Tools, and 3) Systems (including systems software), to provide an overview of the current status of HEP computing and to present findings and opportunities for the desired HEP computational roadmap. The fin
The SunPy Project developed a 13-question survey to understand the software and hardware usage of the solar physics community. 364 members of the solar physics community, across 35 countries, responded to our survey. We found that 99$pm$0.5% of respondents use software in their research and 66% use the Python scientific software stack. Students are twice as likely as faculty, staff scientists, and researchers to use Python rather than Interactive Data Language (IDL). In this respect, the astrophysics and solar physics communities differ widely: 78% of solar physics faculty, staff scientists, and researchers in our sample uses IDL, compared with 44% of astrophysics faculty and scientists sampled by Momcheva and Tollerud (2015). 63$pm$4% of respondents have not taken any computer-science courses at an undergraduate or graduate level. We also found that most respondents utilize consumer hardware to run software for solar-physics research. Although 82% of respondents work with data from space-based or ground-based missions, some of which (e.g. the Solar Dynamics Observatory and Daniel K. Inouye Solar Telescope) produce terabytes of data a day, 14% use a regional or national cluster, 5% use a commercial cloud provider, and 29% use exclusively a laptop or desktop. Finally, we found that 73$pm$4% of respondents cite scientific software in their research, although only 42$pm$3% do so routinely.
We introduce a computational method in physics that goes beyond linear use of equation superposition (BLUES). A BLUES function is defined as a solution of a nonlinear differential equation (DE) with a delta source that is at the same time a Greens function for a related linear DE. For an arbitrary source, the BLUES function can be used to construct an exact solution to the nonlinear DE with a different, but related source. Alternatively, the BLUES function can be used to construct an approximate piecewise analytical solution to the nonlinear DE with an arbitrary source. For this alternative use the related linear DE need not be known. The method is illustrated in a few examples using analytical calculations and numerical computations. Areas for further applications are suggested.
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