35,909 research outputs found
Parallel Weighted Random Sampling
Data structures for efficient sampling from a set of weighted items are an important building block of many applications. However, few parallel solutions are known. We close many of these gaps both for shared-memory and distributed-memory machines. We give efficient, fast, and practicable algorithms for sampling single items, k items with/without replacement, permutations, subsets, and reservoirs. We also give improved sequential algorithms for alias table construction and for sampling with replacement. Experiments on shared-memory parallel machines with up to 158 threads show near linear speedups both for construction and queries
Source-specific routing
Source-specific routing (not to be confused with source routing) is a routing
technique where routing decisions depend on both the source and the destination
address of a packet. Source-specific routing solves some difficult problems
related to multihoming, notably in edge networks, and is therefore a useful
addition to the multihoming toolbox. In this paper, we describe the semantics
of source-specific packet forwarding, and describe the design and
implementation of a source-specific extension to the Babel routing protocol as
well as its implementation - to our knowledge, the first complete
implementation of a source-specific dynamic routing protocol, including a
disambiguation algorithm that makes our implementation work over widely
available networking APIs. We further discuss interoperability between ordinary
next-hop and source-specific dynamic routing protocols. Our implementation has
seen a moderate amount of deployment, notably as a testbed for the IETF Homenet
working group
Parallel Sort-Based Matching for Data Distribution Management on Shared-Memory Multiprocessors
In this paper we consider the problem of identifying intersections between
two sets of d-dimensional axis-parallel rectangles. This is a common problem
that arises in many agent-based simulation studies, and is of central
importance in the context of High Level Architecture (HLA), where it is at the
core of the Data Distribution Management (DDM) service. Several realizations of
the DDM service have been proposed; however, many of them are either
inefficient or inherently sequential. These are serious limitations since
multicore processors are now ubiquitous, and DDM algorithms -- being
CPU-intensive -- could benefit from additional computing power. We propose a
parallel version of the Sort-Based Matching algorithm for shared-memory
multiprocessors. Sort-Based Matching is one of the most efficient serial
algorithms for the DDM problem, but is quite difficult to parallelize due to
data dependencies. We describe the algorithm and compute its asymptotic running
time; we complete the analysis by assessing its performance and scalability
through extensive experiments on two commodity multicore systems based on a
dual socket Intel Xeon processor, and a single socket Intel Core i7 processor.Comment: Proceedings of the 21-th ACM/IEEE International Symposium on
Distributed Simulation and Real Time Applications (DS-RT 2017). Best Paper
Award @DS-RT 201
Revision of Specification Automata under Quantitative Preferences
We study the problem of revising specifications with preferences for automata
based control synthesis problems. In this class of revision problems, the user
provides a numerical ranking of the desirability of the subgoals in their
specifications. When the specification cannot be satisfied on the system, then
our algorithms automatically revise the specification so that the least
desirable user goals are removed from the specification. We propose two
different versions of the revision problem with preferences. In the first
version, the algorithm returns an exact solution while in the second version
the algorithm is an approximation algorithm with non-constant approximation
ratio. Finally, we demonstrate the scalability of our algorithms and we
experimentally study the approximation ratio of the approximation algorithm on
random problem instances.Comment: 9 pages, 3 figures, 3 tables, in Proceedings of the IEEE Conference
on Robotics and Automation, May 201
Free Lunch for Optimisation under the Universal Distribution
Function optimisation is a major challenge in computer science. The No Free
Lunch theorems state that if all functions with the same histogram are assumed
to be equally probable then no algorithm outperforms any other in expectation.
We argue against the uniform assumption and suggest a universal prior exists
for which there is a free lunch, but where no particular class of functions is
favoured over another. We also prove upper and lower bounds on the size of the
free lunch
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