617 research outputs found
A Domain-Specific Language and Editor for Parallel Particle Methods
Domain-specific languages (DSLs) are of increasing importance in scientific
high-performance computing to reduce development costs, raise the level of
abstraction and, thus, ease scientific programming. However, designing and
implementing DSLs is not an easy task, as it requires knowledge of the
application domain and experience in language engineering and compilers.
Consequently, many DSLs follow a weak approach using macros or text generators,
which lack many of the features that make a DSL a comfortable for programmers.
Some of these features---e.g., syntax highlighting, type inference, error
reporting, and code completion---are easily provided by language workbenches,
which combine language engineering techniques and tools in a common ecosystem.
In this paper, we present the Parallel Particle-Mesh Environment (PPME), a DSL
and development environment for numerical simulations based on particle methods
and hybrid particle-mesh methods. PPME uses the meta programming system (MPS),
a projectional language workbench. PPME is the successor of the Parallel
Particle-Mesh Language (PPML), a Fortran-based DSL that used conventional
implementation strategies. We analyze and compare both languages and
demonstrate how the programmer's experience can be improved using static
analyses and projectional editing. Furthermore, we present an explicit domain
model for particle abstractions and the first formal type system for particle
methods.Comment: Submitted to ACM Transactions on Mathematical Software on Dec. 25,
201
A new class of highly efficient exact stochastic simulation algorithms for chemical reaction networks
We introduce an alternative formulation of the exact stochastic simulation
algorithm (SSA) for sampling trajectories of the chemical master equation for a
well-stirred system of coupled chemical reactions. Our formulation is based on
factored-out, partial reaction propensities. This novel exact SSA, called the
partial propensity direct method (PDM), is highly efficient and has a
computational cost that scales at most linearly with the number of chemical
species, irrespective of the degree of coupling of the reaction network. In
addition, we propose a sorting variant, SPDM, which is especially efficient for
multiscale reaction networks.Comment: 23 pages, 3 figures, 4 tables; accepted by J. Chem. Phy
PPF - A Parallel Particle Filtering Library
We present the parallel particle filtering (PPF) software library, which
enables hybrid shared-memory/distributed-memory parallelization of particle
filtering (PF) algorithms combining the Message Passing Interface (MPI) with
multithreading for multi-level parallelism. The library is implemented in Java
and relies on OpenMPI's Java bindings for inter-process communication. It
includes dynamic load balancing, multi-thread balancing, and several
algorithmic improvements for PF, such as input-space domain decomposition. The
PPF library hides the difficulties of efficient parallel programming of PF
algorithms and provides application developers with the necessary tools for
parallel implementation of PF methods. We demonstrate the capabilities of the
PPF library using two distributed PF algorithms in two scenarios with different
numbers of particles. The PPF library runs a 38 million particle problem,
corresponding to more than 1.86 GB of particle data, on 192 cores with 67%
parallel efficiency. To the best of our knowledge, the PPF library is the first
open-source software that offers a parallel framework for PF applications.Comment: 8 pages, 8 figures; will appear in the proceedings of the IET Data
Fusion & Target Tracking Conference 201
A Lagrangian particle method for reaction-diffusion systems on deforming surfaces
Reaction-diffusion processes on complex deforming surfaces are fundamental to a number of biological processes ranging from embryonic development to cancer tumor growth and angiogenesis. The simulation of these processes using continuum reaction-diffusion models requires computational methods capable of accurately tracking the geometric deformations and discretizing on them the governing equations. We employ a Lagrangian level-set formulation to capture the deformation of the geometry and use an embedding formulation and an adaptive particle method to discretize both the level-set equations and the corresponding reaction-diffusion. We validate the proposed method and discuss its advantages and drawbacks through simulations of reaction-diffusion equations on complex and deforming geometrie
A robustness measure for singular point and index estimation in discretized orientation and vector fields
The identification of singular points or topological defects in discretized
vector fields occurs in diverse areas ranging from the polarization of the
cosmic microwave background to liquid crystals to fingerprint recognition and
bio-medical imaging. Due to their discrete nature, defects and their
topological charge cannot depend continuously on each single vector, but they
discontinuously change as soon as a vector changes by more than a threshold.
Considering this threshold of admissible change at the level of vectors, we
develop a robustness measure for discrete defect estimators. Here, we compare
different template paths for defect estimation in discretized vector or
orientation fields. Sampling prototypical vector field patterns around defects
shows that the robustness increases with the length of template path, but less
so in the presence of noise on the vectors. We therefore find an optimal
trade-off between resolution and robustness against noise for relatively small
templates, except for the "single pixel" defect analysis, which cannot exclude
zero robustness. The presented robustness measure paves the way for uncertainty
quantification of defects in discretized vector fields.Comment: 4 pages, 1 figur
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