112,176 research outputs found
Loo.py: From Fortran to performance via transformation and substitution rules
A large amount of numerically-oriented code is written and is being written
in legacy languages. Much of this code could, in principle, make good use of
data-parallel throughput-oriented computer architectures. Loo.py, a
transformation-based programming system targeted at GPUs and general
data-parallel architectures, provides a mechanism for user-controlled
transformation of array programs. This transformation capability is designed to
not just apply to programs written specifically for Loo.py, but also those
imported from other languages such as Fortran. It eases the trade-off between
achieving high performance, portability, and programmability by allowing the
user to apply a large and growing family of transformations to an input
program. These transformations are expressed in and used from Python and may be
applied from a variety of settings, including a pragma-like manner from other
languages.Comment: ARRAY 2015 - 2nd ACM SIGPLAN International Workshop on Libraries,
Languages and Compilers for Array Programming (ARRAY 2015
Approximating Generalized Network Design under (Dis)economies of Scale with Applications to Energy Efficiency
In a generalized network design (GND) problem, a set of resources are
assigned to multiple communication requests. Each request contributes its
weight to the resources it uses and the total load on a resource is then
translated to the cost it incurs via a resource specific cost function. For
example, a request may be to establish a virtual circuit, thus contributing to
the load on each edge in the circuit. Motivated by energy efficiency
applications, recently, there is a growing interest in GND using cost functions
that exhibit (dis)economies of scale ((D)oS), namely, cost functions that
appear subadditive for small loads and superadditive for larger loads.
The current paper advances the existing literature on approximation
algorithms for GND problems with (D)oS cost functions in various aspects: (1)
we present a generic approximation framework that yields approximation results
for a much wider family of requests in both directed and undirected graphs; (2)
our framework allows for unrelated weights, thus providing the first
non-trivial approximation for the problem of scheduling unrelated parallel
machines with (D)oS cost functions; (3) our framework is fully combinatorial
and runs in strongly polynomial time; (4) the family of (D)oS cost functions
considered in the current paper is more general than the one considered in the
existing literature, providing a more accurate abstraction for practical energy
conservation scenarios; and (5) we obtain the first approximation ratio for GND
with (D)oS cost functions that depends only on the parameters of the resources'
technology and does not grow with the number of resources, the number of
requests, or their weights. The design of our framework relies heavily on
Roughgarden's smoothness toolbox (JACM 2015), thus demonstrating the possible
usefulness of this toolbox in the area of approximation algorithms.Comment: 39 pages, 1 figure. An extended abstract of this paper is to appear
in the 50th Annual ACM Symposium on the Theory of Computing (STOC 2018
Fast Generation of Random Spanning Trees and the Effective Resistance Metric
We present a new algorithm for generating a uniformly random spanning tree in
an undirected graph. Our algorithm samples such a tree in expected
time. This improves over the best previously known bound
of -- that follows from the work of
Kelner and M\k{a}dry [FOCS'09] and of Colbourn et al. [J. Algorithms'96] --
whenever the input graph is sufficiently sparse.
At a high level, our result stems from carefully exploiting the interplay of
random spanning trees, random walks, and the notion of effective resistance, as
well as from devising a way to algorithmically relate these concepts to the
combinatorial structure of the graph. This involves, in particular,
establishing a new connection between the effective resistance metric and the
cut structure of the underlying graph
Smart Conversational Agents for Reminiscence
In this paper we describe the requirements and early system design for a
smart conversational agent that can assist older adults in the reminiscence
process. The practice of reminiscence has well documented benefits for the
mental, social and emotional well-being of older adults. However, the
technology support, valuable in many different ways, is still limited in terms
of need of co-located human presence, data collection capabilities, and ability
to support sustained engagement, thus missing key opportunities to improve care
practices, facilitate social interactions, and bring the reminiscence practice
closer to those with less opportunities to engage in co-located sessions with a
(trained) companion. We discuss conversational agents and cognitive services as
the platform for building the next generation of reminiscence applications, and
introduce the concept application of a smart reminiscence agent
Anatomy of the Third-Party Web Tracking Ecosystem
The presence of third-party tracking on websites has become customary.
However, our understanding of the third-party ecosystem is still very
rudimentary. We examine third-party trackers from a geographical perspective,
observing the third-party tracking ecosystem from 29 countries across the
globe. When examining the data by region (North America, South America, Europe,
East Asia, Middle East, and Oceania), we observe significant geographical
variation between regions and countries within regions. We find trackers that
focus on specific regions and countries, and some that are hosted in countries
outside their expected target tracking domain. Given the differences in
regulatory regimes between jurisdictions, we believe this analysis sheds light
on the geographical properties of this ecosystem and on the problems that these
may pose to our ability to track and manage the different data silos that now
store personal data about us all
Quantum central limit theorem for continuous-time quantum walks on odd graphs in quantum probability theory
The method of the quantum probability theory only requires simple structural
data of graph and allows us to avoid a heavy combinational argument often
necessary to obtain full description of spectrum of the adjacency matrix. In
the present paper, by using the idea of calculation of the probability
amplitudes for continuous-time quantum walk in terms of the quantum probability
theory, we investigate quantum central limit theorem for continuous-time
quantum walks on odd graphs.Comment: 19 page, 1 figure
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