329 research outputs found
FooPar: A Functional Object Oriented Parallel Framework in Scala
We present FooPar, an extension for highly efficient Parallel Computing in
the multi-paradigm programming language Scala. Scala offers concise and clean
syntax and integrates functional programming features. Our framework FooPar
combines these features with parallel computing techniques. FooPar is designed
modular and supports easy access to different communication backends for
distributed memory architectures as well as high performance math libraries. In
this article we use it to parallelize matrix matrix multiplication and show its
scalability by a isoefficiency analysis. In addition, results based on a
empirical analysis on two supercomputers are given. We achieve close-to-optimal
performance wrt. theoretical peak performance. Based on this result we conclude
that FooPar allows to fully access Scala's design features without suffering
from performance drops when compared to implementations purely based on C and
MPI
Uniform WKB approximation of Coulomb wave functions for arbitrary partial wave
Coulomb wave functions are difficult to compute numerically for extremely low
energies, even with direct numerical integration. Hence, it is more convenient
to use asymptotic formulas in this region. It is the object of this paper to
derive analytical asymptotic formulas valid for arbitrary energies and partial
waves. Moreover, it is possible to extend these formulas for complex values of
parameters.Comment: 5 pages, 2 figure
Scalable Parallel Numerical Constraint Solver Using Global Load Balancing
We present a scalable parallel solver for numerical constraint satisfaction
problems (NCSPs). Our parallelization scheme consists of homogeneous worker
solvers, each of which runs on an available core and communicates with others
via the global load balancing (GLB) method. The parallel solver is implemented
with X10 that provides an implementation of GLB as a library. In experiments,
several NCSPs from the literature were solved and attained up to 516-fold
speedup using 600 cores of the TSUBAME2.5 supercomputer.Comment: To be presented at X10'15 Worksho
Universal Programmable Quantum Circuit Schemes to Emulate an Operator
Unlike fixed designs, programmable circuit designs support an infinite number
of operators. The functionality of a programmable circuit can be altered by
simply changing the angle values of the rotation gates in the circuit. Here, we
present a new quantum circuit design technique resulting in two general
programmable circuit schemes. The circuit schemes can be used to simulate any
given operator by setting the angle values in the circuit. This provides a
fixed circuit design whose angles are determined from the elements of the given
matrix-which can be non-unitary-in an efficient way. We also give both the
classical and quantum complexity analysis for these circuits and show that the
circuits require a few classical computations. They have almost the same
quantum complexities as non-general circuits. Since the presented circuit
designs are independent from the matrix decomposition techniques and the global
optimization processes used to find quantum circuits for a given operator, high
accuracy simulations can be done for the unitary propagators of molecular
Hamiltonians on quantum computers. As an example, we show how to build the
circuit design for the hydrogen molecule.Comment: combined with former arXiv:1207.174
Fast But Not Furious. When Sped Up Bit Rate of Information Drives Rule Induction
The language abilities of young and adult learners range from memorizing specific items to finding statistical regularities between them (item-bound generalization) and generalizing rules to novel instances (category-based generalization). Both external factors, such as input variability, and internal factors, such as cognitive limitations, have been shown to drive these abilities. However, the exact dynamics between these factors and circumstances under which rule induction emerges remain largely underspecified. Here, we extend our information-theoretic model (Radulescu et al., 2019), based on Shannon’s noisy-channel coding theory, which adds into the “formula” for rule induction the crucial dimension of time: the rate of encoding information by a time-sensitive mechanism. The goal of this study is to test the channel capacity-based hypothesis of our model: if the input entropy per second is higher than the maximum rate of information transmission (bits/second), which is determined by the channel capacity, the encoding method moves gradually from item-bound generalization to a more efficient category-based generalization, so as to avoid exceeding the channel capacity. We ran two artificial grammar experiments with adults, in which we sped up the bit rate of information transmission, crucially not by an arbitrary amount but by a factor calculated using the channel capacity formula on previous data. We found that increased bit rate of information transmission in a repetition-based XXY grammar drove the tendency of learners toward category-based generalization, as predicted by our model. Conversely, we found that increased bit rate of information transmission in complex non-adjacent dependency aXb grammar impeded the item-bound generalization of the specific a_b frames, and led to poorer learning, at least judging by our accuracy assessment method. This finding could show that, since increasing the bit rate of information precipitates a change from item-bound to category-based generalization, it impedes the item-bound generalization of the specific a_b frames, and that it facilitates category-based generalization both for the intervening Xs and possibly for a/b categories. Thus, sped up bit rate does not mean that an unrestrainedly increasing bit rate drives rule induction in any context, or grammar. Rather, it is the specific dynamics between the input entropy and the maximum rate of information transmission
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