302 research outputs found
A Multi-Code Analysis Toolkit for Astrophysical Simulation Data
The analysis of complex multiphysics astrophysical simulations presents a
unique and rapidly growing set of challenges: reproducibility, parallelization,
and vast increases in data size and complexity chief among them. In order to
meet these challenges, and in order to open up new avenues for collaboration
between users of multiple simulation platforms, we present yt (available at
http://yt.enzotools.org/), an open source, community-developed astrophysical
analysis and visualization toolkit. Analysis and visualization with yt are
oriented around physically relevant quantities rather than quantities native to
astrophysical simulation codes. While originally designed for handling Enzo's
structure adaptive mesh refinement (AMR) data, yt has been extended to work
with several different simulation methods and simulation codes including Orion,
RAMSES, and FLASH. We report on its methods for reading, handling, and
visualizing data, including projections, multivariate volume rendering,
multi-dimensional histograms, halo finding, light cone generation and
topologically-connected isocontour identification. Furthermore, we discuss the
underlying algorithms yt uses for processing and visualizing data, and its
mechanisms for parallelization of analysis tasks.Comment: 18 pages, 6 figures, emulateapj format. Resubmitted to Astrophysical
Journal Supplement Series with revisions from referee. yt can be found at
http://yt.enzotools.org
Cosmological Parameters from Observations of Galaxy Clusters
Studies of galaxy clusters have proved crucial in helping to establish the
standard model of cosmology, with a universe dominated by dark matter and dark
energy. A theoretical basis that describes clusters as massive,
multi-component, quasi-equilibrium systems is growing in its capability to
interpret multi-wavelength observations of expanding scope and sensitivity. We
review current cosmological results, including contributions to fundamental
physics, obtained from observations of galaxy clusters. These results are
consistent with and complementary to those from other methods. We highlight
several areas of opportunity for the next few years, and emphasize the need for
accurate modeling of survey selection and sources of systematic error.
Capitalizing on these opportunities will require a multi-wavelength approach
and the application of rigorous statistical frameworks, utilizing the combined
strengths of observers, simulators and theorists.Comment: 53 pages, 21 figures. To appear in Annual Review of Astronomy &
Astrophysic
Dark Matter: A Primer
Dark matter is one of the greatest unsolved mysteries in cosmology at the
present time. About 80% of the universe's gravitating matter is non-luminous,
and its nature and distribution are for the most part unknown. In this paper,
we will outline the history, astrophysical evidence, candidates, and detection
methods of dark matter, with the goal to give the reader an accessible but
rigorous introduction to the puzzle of dark matter. This review targets
advanced students and researchers new to the field of dark matter, and includes
an extensive list of references for further study.Comment: 26 pages, 6 figure
ArborX: A Performance Portable Geometric Search Library
Searching for geometric objects that are close in space is a fundamental
component of many applications. The performance of search algorithms comes to
the forefront as the size of a problem increases both in terms of total object
count as well as in the total number of search queries performed. Scientific
applications requiring modern leadership-class supercomputers also pose an
additional requirement of performance portability, i.e. being able to
efficiently utilize a variety of hardware architectures. In this paper, we
introduce a new open-source C++ search library, ArborX, which we have designed
for modern supercomputing architectures. We examine scalable search algorithms
with a focus on performance, including a highly efficient parallel bounding
volume hierarchy implementation, and propose a flexible interface making it
easy to integrate with existing applications. We demonstrate the performance
portability of ArborX on multi-core CPUs and GPUs, and compare it to the
state-of-the-art libraries such as Boost.Geometry.Index and nanoflann
Doctor of Philosophy
dissertationRecent trends in high performance computing present larger and more diverse computers using multicore nodes possibly with accelerators and/or coprocessors and reduced memory. These changes pose formidable challenges for applications code to attain scalability. Software frameworks that execute machine-independent applications code using a runtime system that shields users from architectural complexities oer a portable solution for easy programming. The Uintah framework, for example, solves a broad class of large-scale problems on structured adaptive grids using fluid-flow solvers coupled with particle-based solids methods. However, the original Uintah code had limited scalability as tasks were run in a predefined order based solely on static analysis of the task graph and used only message passing interface (MPI) for parallelism. By using a new hybrid multithread and MPI runtime system, this research has made it possible for Uintah to scale to 700K central processing unit (CPU) cores when solving challenging fluid-structure interaction problems. Those problems often involve moving objects with adaptive mesh refinement and thus with highly variable and unpredictable work patterns. This research has also demonstrated an ability to run capability jobs on the heterogeneous systems with Nvidia graphics processing unit (GPU) accelerators or Intel Xeon Phi coprocessors. The new runtime system for Uintah executes directed acyclic graphs of computational tasks with a scalable asynchronous and dynamic runtime system for multicore CPUs and/or accelerators/coprocessors on a node. Uintah's clear separation between application and runtime code has led to scalability increases without significant changes to application code. This research concludes that the adaptive directed acyclic graph (DAG)-based approach provides a very powerful abstraction for solving challenging multiscale multiphysics engineering problems. Excellent scalability with regard to the different processors and communications performance are achieved on some of the largest and most powerful computers available today
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