157 research outputs found
Invasive Computing in HPC with X10
High performance computing with thousands of cores relies on distributed
memory due to memory consistency reasons. The resource
management on such systems usually relies on static assignment of
resources at the start of each application. Such a static scheduling
is incapable of starting applications with required resources being
used by others since a reduction of resources assigned to applications
without stopping them is not possible. This lack of dynamic
adaptive scheduling leads to idling resources until the remaining
amount of requested resources gets available. Additionally, applications
with changing resource requirements lead to idling or less
efficiently used resources. The invasive computing paradigm suggests
dynamic resource scheduling and applications able to dynamically
adapt to changing resource requirements.
As a case study, we developed an invasive resource manager as
well as a multigrid with dynamically changing resource demands.
Such a multigrid has changing scalability behavior during its execution
and requires data migration upon reallocation due to distributed
memory systems.
To counteract the additional complexity introduced by the additional
interfaces, e. g. for data migration, we use the X10 programming
language for improved programmability. Our results show
improved application throughput and the dynamic adaptivity. In addition,
we show our extension for the distributed arrays of X10 to
support data migrationThis work was supported by the German Research Foundation
(DFG) as part of the Transregional Collaborative Research Centre
“Invasive Computing” (SFB/TR 89)
Efficient Resolution of Anisotropic Structures
We highlight some recent new delevelopments concerning the sparse
representation of possibly high-dimensional functions exhibiting strong
anisotropic features and low regularity in isotropic Sobolev or Besov scales.
Specifically, we focus on the solution of transport equations which exhibit
propagation of singularities where, additionally, high-dimensionality enters
when the convection field, and hence the solutions, depend on parameters
varying over some compact set. Important constituents of our approach are
directionally adaptive discretization concepts motivated by compactly supported
shearlet systems, and well-conditioned stable variational formulations that
support trial spaces with anisotropic refinements with arbitrary
directionalities. We prove that they provide tight error-residual relations
which are used to contrive rigorously founded adaptive refinement schemes which
converge in . Moreover, in the context of parameter dependent problems we
discuss two approaches serving different purposes and working under different
regularity assumptions. For frequent query problems, making essential use of
the novel well-conditioned variational formulations, a new Reduced Basis Method
is outlined which exhibits a certain rate-optimal performance for indefinite,
unsymmetric or singularly perturbed problems. For the radiative transfer
problem with scattering a sparse tensor method is presented which mitigates or
even overcomes the curse of dimensionality under suitable (so far still
isotropic) regularity assumptions. Numerical examples for both methods
illustrate the theoretical findings
A posteriori error analysis and adaptive non-intrusive numerical schemes for systems of random conservation laws
In this article we consider one-dimensional random systems of hyperbolic
conservation laws. We first establish existence and uniqueness of random
entropy admissible solutions for initial value problems of conservation laws
which involve random initial data and random flux functions. Based on these
results we present an a posteriori error analysis for a numerical approximation
of the random entropy admissible solution. For the stochastic discretization,
we consider a non-intrusive approach, the Stochastic Collocation method. The
spatio-temporal discretization relies on the Runge--Kutta Discontinuous
Galerkin method. We derive the a posteriori estimator using continuous
reconstructions of the discrete solution. Combined with the relative entropy
stability framework this yields computable error bounds for the entire
space-stochastic discretization error. The estimator admits a splitting into a
stochastic and a deterministic (space-time) part, allowing for a novel
residual-based space-stochastic adaptive mesh refinement algorithm. We conclude
with various numerical examples investigating the scaling properties of the
residuals and illustrating the efficiency of the proposed adaptive algorithm
Sparse Pseudospectral Approximation Method
Multivariate global polynomial approximations - such as polynomial chaos or
stochastic collocation methods - are now in widespread use for sensitivity
analysis and uncertainty quantification. The pseudospectral variety of these
methods uses a numerical integration rule to approximate the Fourier-type
coefficients of a truncated expansion in orthogonal polynomials. For problems
in more than two or three dimensions, a sparse grid numerical integration rule
offers accuracy with a smaller node set compared to tensor product
approximation. However, when using a sparse rule to approximately integrate
these coefficients, one often finds unacceptable errors in the coefficients
associated with higher degree polynomials.
By reexamining Smolyak's algorithm and exploiting the connections between
interpolation and projection in tensor product spaces, we construct a sparse
pseudospectral approximation method that accurately reproduces the coefficients
of basis functions that naturally correspond to the sparse grid integration
rule. The compelling numerical results show that this is the proper way to use
sparse grid integration rules for pseudospectral approximation
Modelling social identification and helping in evacuation simulation
Social scientists have criticised computer models of pedestrian streams for their treatment of psychological crowds as mere aggregations of individuals. Indeed most models for evacuation dynamics use analogies from physics where pedestrians are considered as particles. Although this ensures that the results of the simulation match important physical phenomena, such as the deceleration of the crowd with increasing density, social phenomena such as group processes are ignored. In particular, people in a crowd have social identities and share those social identities with the others in the crowd. The process of self categorisation determines norms within the crowd and influences how people will behave in evacuation situations. We formulate the application of social identity in pedestrian simulation algorithmically. The goal is to examine whether it is possible to carry over the psychological model to computer models of pedestrian motion so that simulation results correspond to observations from crowd psychology. That is, we quantify and formalise empirical research on and verbal descriptions of the effect of group identity on behaviour. We use uncertainty quantification to analyse the model’s behaviour when we vary crucial model parameters. In this first approach we restrict ourselves to a specific scenario that was thoroughly investigated by crowd psychologists and where some quantitative data is available: the bombing and subsequent evacuation of a London underground tube carriage on July 7th 2005
The long-term consequences of hybridization between the two Daphnia species, D. galeata and D. dentifera, in mature habitats
<p>Abstract</p> <p>Background</p> <p>Ecological specializations such as antipredator defense can reinforce morphological and distributional divergence within hybridizing species. Two hybridizing species of <it>Daphnia </it>(<it>D. galeata </it>and <it>D. dentifera</it>) are distributed in both Japan and North America; however, these populations have a longer history in Japan than in North America due to the differing impact of the last glaciation on these two regions. We tested the hypothesis that this longer coexistence in Japan would lead to extensive genetic admixture in nuclear and mitochondrial DNA whilst the distinct morphological traits and distributional patterns would be maintained.</p> <p>Results</p> <p>The high level of correspondence among morphological traits, distribution, and mitochondrial and nuclear DNA types for the specimens with <it>D. dentifera </it>mtDNA indicated that the species distinction has been maintained. However, a discordance between mtDNA and nuclear ITS-1 types was observed for most specimens that had <it>D. galeata </it>mtDNA, consistent with the pattern seen between the two species in North America. This observation suggests nuclear introgression from <it>D. dentifera </it>into <it>D. galeata </it>without mitochondrial introgression.</p> <p>Conclusions</p> <p>The separation of morphological traits and distribution ranges of the two hybridizing species in Japan, as well as in North America, has been maintained, despite large differences in climatic and geographical histories of these two regions. Variations in environmental factors, such as predation pressure, might affect maintenance of the distribution, although the further studies are needed to confirm this.</p
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