1,607 research outputs found
Analyzing and Visualizing Cosmological Simulations with ParaView
The advent of large cosmological sky surveys - ushering in the era of
precision cosmology - has been accompanied by ever larger cosmological
simulations. The analysis of these simulations, which currently encompass tens
of billions of particles and up to trillion particles in the near future, is
often as daunting as carrying out the simulations in the first place.
Therefore, the development of very efficient analysis tools combining
qualitative and quantitative capabilities is a matter of some urgency. In this
paper we introduce new analysis features implemented within ParaView, a
parallel, open-source visualization toolkit, to analyze large N-body
simulations. The new features include particle readers and a very efficient
halo finder which identifies friends-of-friends halos and determines common
halo properties. In combination with many other functionalities already
existing within ParaView, such as histogram routines or interfaces to Python,
this enhanced version enables fast, interactive, and convenient analyses of
large cosmological simulations. In addition, development paths are available
for future extensions.Comment: 9 pages, 8 figure
XScan: An Integrated Tool for Understanding Open Source Community-Based Scientific Code
Many scientific communities have adopted community-based models that integrate multiple components to simulate whole system dynamics. The community software projects’ complexity, stems from the integration of multiple individual software components that were developed under different application requirements and various machine architectures, has become a challenge for effective software system understanding and continuous software development. The paper presents an integrated software toolkit called X-ray Software Scanner (in abbreviation, XScan) for a better understanding of large-scale community-based scientific codes. Our software tool provides support to quickly summarize the overall information of scientific codes, including the number of lines of code, programming languages, external library dependencies, as well as architecture-dependent parallel software features. The XScan toolkit also realizes a static software analysis component to collect detailed structural information and provides an interactive visualization and analysis of the functions. We use a large-scale community-based Earth System Model to demonstrate the workflow, functions and visualization of the toolkit. We also discuss the application of advanced graph analytics techniques to assist software modular design and component refactoring
An Intelligent Text Extraction and Navigation System
We present sppc, a high-performance system for intelligent text extraction and navigation from German free text documents. The main purpose of sppc is to extract as much linguistic structure as possible for performing domain-specific processing. sppc consists of a set of domain-independent shallow core components which are realized by means of cascaded weighted finite state machines and generic dynamic tries. All extracted information is represented uniformly in one data structure (called the text chart) in a highly compact and linked form in order to support indexing and navigation through the set of solutions. Germa
Exploration of different parameter optimization algorithms within the context of ACTS software framework
Particle track reconstruction, in which the trajectories of charged particles
are determined, is a critical and time consuming component of the full event
reconstruction chain. The underlying software is complex and consists of a
number of mathematically intense algorithms, each dealing with a particular
tracking sub-process. These algorithms have many input parameters that need to
be supplied in advance. However, it is difficult to determine the configuration
of these parameters that produces the best performance. Currently, the input
parameter values are decided on the basis of prior experience or by the use of
brute force techniques. A parameter optimization approach that is able to
automatically tune these parameters for high performance is greatly desirable.
In the current work, we explore various machine learning based optimization
methods to devise a suitable technique to optimize parameters in the complex
track reconstruction environment. These methods are evaluated on the basis of a
metric that targets high efficiency while keeping the duplicate and fake rates
small. We focus on derivative free optimization approaches that can be applied
to problems involving non-differentiable loss functions. For our studies, we
consider the tracking algorithms defined within A Common Tracking Software
(ACTS) framework. We test our methods using simulated data from ACTS software
corresponding to the ACTS Generic detector and the ATLAS ITk detector
geometries
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