4,871 research outputs found
Managing Communication Latency-Hiding at Runtime for Parallel Programming Languages and Libraries
This work introduces a runtime model for managing communication with support
for latency-hiding. The model enables non-computer science researchers to
exploit communication latency-hiding techniques seamlessly. For compiled
languages, it is often possible to create efficient schedules for
communication, but this is not the case for interpreted languages. By
maintaining data dependencies between scheduled operations, it is possible to
aggressively initiate communication and lazily evaluate tasks to allow maximal
time for the communication to finish before entering a wait state. We implement
a heuristic of this model in DistNumPy, an auto-parallelizing version of
numerical Python that allows sequential NumPy programs to run on distributed
memory architectures. Furthermore, we present performance comparisons for eight
benchmarks with and without automatic latency-hiding. The results shows that
our model reduces the time spent on waiting for communication as much as 27
times, from a maximum of 54% to only 2% of the total execution time, in a
stencil application.Comment: PREPRIN
Python bindings for the open source electromagnetic simulator Meep
Meep is a broadly used open source package for finite-difference time-domain electromagnetic simulations. Python bindings for Meep make it easier to use for researchers and open promising opportunities for integration with other packages in the Python ecosystem. As this project shows, implementing Python-Meep offers benefits for specific disciplines and for the wider research community
COFFEE -- An MPI-parallelized Python package for the numerical evolution of differential equations
COFFEE (ConFormal Field Equation Evolver) is a Python package primarily
developed to numerically evolve systems of partial differential equations over
time using the method of lines. It includes a variety of time integrators and
finite differencing stencils with the summation-by-parts property, as well as
pseudo-spectral functionality for angular derivatives of spin-weighted
functions. Some additional capabilities include being MPI-parallelisable on a
variety of different geometries, HDF data output and post processing scripts to
visualize data, and an actions class that allows users to create code for
analysis after each timestep.Comment: 12 pages, 1 figure, accepted to be published in Software
Parallel Computation in Econometrics: A Simplified Approach
Parallel computation has a long history in econometric computing, but is not at all wide spread. We believe that a major impediment is the labour cost of coding for parallel architectures. Moreover, programs for specific hardware often become obsolete quite quickly. Our approach is to take a popular matrix programming language (Ox), and implement a message-passing interface using MPI. Next, object-oriented programming allows us to hide the specific parallelization code, so that a program does not need to be rewritten when it is ported from the desktop to a distributed network of computers. Our focus is on so-called embarrassingly parallel computations, and we address the issue of parallel random number generation.Code optimization; Econometrics; High-performance computing; Matrix-programming language; Monte Carlo; MPI; Ox; Parallel computing; Random number generation.
Beyond XSPEC: Towards Highly Configurable Analysis
We present a quantitative comparison between software features of the defacto
standard X-ray spectral analysis tool, XSPEC, and ISIS, the Interactive
Spectral Interpretation System. Our emphasis is on customized analysis, with
ISIS offered as a strong example of configurable software. While noting that
XSPEC has been of immense value to astronomers, and that its scientific core is
moderately extensible--most commonly via the inclusion of user contributed
"local models"--we identify a series of limitations with its use beyond
conventional spectral modeling. We argue that from the viewpoint of the
astronomical user, the XSPEC internal structure presents a Black Box Problem,
with many of its important features hidden from the top-level interface, thus
discouraging user customization. Drawing from examples in custom modeling,
numerical analysis, parallel computation, visualization, data management, and
automated code generation, we show how a numerically scriptable, modular, and
extensible analysis platform such as ISIS facilitates many forms of advanced
astrophysical inquiry.Comment: Accepted by PASP, for July 2008 (15 pages
Computationally-intensive Econometrics using a Distributed Matrix-programming Language
This paper reviews the need for powerful facilities in econometrics, focusing on concrete problems which arise in financial economics and in macroeconomics. We argue that the profession is being held back by the lack of easy to use generic software which is able to exploit the availability of cheap clusters of distributed computers. Our response is to extend, in a number of directions, the well known matrix-programming interpreted language Ox developed by the first author. We note three possible levels of extensions: (i) Ox with parallelization explicit in the Ox code; (ii) Ox with a parallelized run-time library; (iii) Ox with a parallelized interpreter. This paper studies and implements the first case, emphasizing the need for deterministic computing in science. We give examples in the context of financial economics and time-series modelling.Distributed computing; Econometrics; High-performance computing; Matrix-programming language
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