40,932 research outputs found
nbodykit: an open-source, massively parallel toolkit for large-scale structure
We present nbodykit, an open-source, massively parallel Python toolkit for
analyzing large-scale structure (LSS) data. Using Python bindings of the
Message Passing Interface (MPI), we provide parallel implementations of many
commonly used algorithms in LSS. nbodykit is both an interactive and scalable
piece of scientific software, performing well in a supercomputing environment
while still taking advantage of the interactive tools provided by the Python
ecosystem. Existing functionality includes estimators of the power spectrum, 2
and 3-point correlation functions, a Friends-of-Friends grouping algorithm,
mock catalog creation via the halo occupation distribution technique, and
approximate N-body simulations via the FastPM scheme. The package also provides
a set of distributed data containers, insulated from the algorithms themselves,
that enable nbodykit to provide a unified treatment of both simulation and
observational data sets. nbodykit can be easily deployed in a high performance
computing environment, overcoming some of the traditional difficulties of using
Python on supercomputers. We provide performance benchmarks illustrating the
scalability of the software. The modular, component-based approach of nbodykit
allows researchers to easily build complex applications using its tools. The
package is extensively documented at http://nbodykit.readthedocs.io, which also
includes an interactive set of example recipes for new users to explore. As
open-source software, we hope nbodykit provides a common framework for the
community to use and develop in confronting the analysis challenges of future
LSS surveys.Comment: 18 pages, 7 figures. Feedback very welcome. Code available at
https://github.com/bccp/nbodykit and for documentation, see
http://nbodykit.readthedocs.i
TensorFlow Enabled Genetic Programming
Genetic Programming, a kind of evolutionary computation and machine learning
algorithm, is shown to benefit significantly from the application of vectorized
data and the TensorFlow numerical computation library on both CPU and GPU
architectures. The open source, Python Karoo GP is employed for a series of 190
tests across 6 platforms, with real-world datasets ranging from 18 to 5.5M data
points. This body of tests demonstrates that datasets measured in tens and
hundreds of data points see 2-15x improvement when moving from the scalar/SymPy
configuration to the vector/TensorFlow configuration, with a single core
performing on par or better than multiple CPU cores and GPUs. A dataset
composed of 90,000 data points demonstrates a single vector/TensorFlow CPU core
performing 875x better than 40 scalar/Sympy CPU cores. And a dataset containing
5.5M data points sees GPU configurations out-performing CPU configurations on
average by 1.3x.Comment: 8 pages, 5 figures; presented at GECCO 2017, Berlin, German
Massively Parallel Computing and the Search for Jets and Black Holes at the LHC
Massively parallel computing at the LHC could be the next leap necessary to
reach an era of new discoveries at the LHC after the Higgs discovery.
Scientific computing is a critical component of the LHC experiment, including
operation, trigger, LHC computing GRID, simulation, and analysis. One way to
improve the physics reach of the LHC is to take advantage of the flexibility of
the trigger system by integrating coprocessors based on Graphics Processing
Units (GPUs) or the Many Integrated Core (MIC) architecture into its server
farm. This cutting edge technology provides not only the means to accelerate
existing algorithms, but also the opportunity to develop new algorithms that
select events in the trigger that previously would have evaded detection. In
this article we describe new algorithms that would allow to select in the
trigger new topological signatures that include non-prompt jet and black
hole--like objects in the silicon tracker.Comment: 15 pages, 11 figures, submitted to NIM
Large-Scale Forcing with Less Communication in Finite-Difference Simulations of Stationary Isotropic Turbulence
「気候変動に適応可能な環境探索のためのマルチスケールシミュレーション」プロジェク
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