2,118 research outputs found
Internal Diffusion-Limited Aggregation: Parallel Algorithms and Complexity
The computational complexity of internal diffusion-limited aggregation (DLA)
is examined from both a theoretical and a practical point of view. We show that
for two or more dimensions, the problem of predicting the cluster from a given
set of paths is complete for the complexity class CC, the subset of P
characterized by circuits composed of comparator gates. CC-completeness is
believed to imply that, in the worst case, growing a cluster of size n requires
polynomial time in n even on a parallel computer.
A parallel relaxation algorithm is presented that uses the fact that clusters
are nearly spherical to guess the cluster from a given set of paths, and then
corrects defects in the guessed cluster through a non-local annihilation
process. The parallel running time of the relaxation algorithm for
two-dimensional internal DLA is studied by simulating it on a serial computer.
The numerical results are compatible with a running time that is either
polylogarithmic in n or a small power of n. Thus the computational resources
needed to grow large clusters are significantly less on average than the
worst-case analysis would suggest.
For a parallel machine with k processors, we show that random clusters in d
dimensions can be generated in O((n/k + log k) n^{2/d}) steps. This is a
significant speedup over explicit sequential simulation, which takes
O(n^{1+2/d}) time on average.
Finally, we show that in one dimension internal DLA can be predicted in O(log
n) parallel time, and so is in the complexity class NC
The identification of cellular automata
Although cellular automata have been widely studied as a class of the spatio temporal systems, very few investigators have studied how to identify the CA rules given observations of the patterns. A solution using a polynomial realization to describe the CA rule is reviewed in the present study based on the application of an orthogonal least squares algorithm. Three new neighbourhood detection methods are then reviewed as important preliminary analysis procedures to reduce the complexity of the estimation. The identification of excitable media is discussed using simulation examples and real data sets and a new method for the identification of
hybrid CA is introduced
Sequentializing Parameterized Programs
We exhibit assertion-preserving (reachability preserving) transformations
from parameterized concurrent shared-memory programs, under a k-round
scheduling of processes, to sequential programs. The salient feature of the
sequential program is that it tracks the local variables of only one thread at
any point, and uses only O(k) copies of shared variables (it does not use extra
counters, not even one counter to keep track of the number of threads).
Sequentialization is achieved using the concept of a linear interface that
captures the effect an unbounded block of processes have on the shared state in
a k-round schedule. Our transformation utilizes linear interfaces to
sequentialize the program, and to ensure the sequential program explores only
reachable states and preserves local invariants.Comment: In Proceedings FIT 2012, arXiv:1207.348
Custom Integrated Circuits
Contains reports on ten research projects.Analog Devices, Inc.IBM CorporationNational Science Foundation/Defense Advanced Research Projects Agency Grant MIP 88-14612Analog Devices Career Development Assistant ProfessorshipU.S. Navy - Office of Naval Research Contract N0014-87-K-0825AT&TDigital Equipment CorporationNational Science Foundation Grant MIP 88-5876
Modeling and Simulation of Spark Streaming
As more and more devices connect to Internet of Things, unbounded streams of
data will be generated, which have to be processed "on the fly" in order to
trigger automated actions and deliver real-time services. Spark Streaming is a
popular realtime stream processing framework. To make efficient use of Spark
Streaming and achieve stable stream processing, it requires a careful interplay
between different parameter configurations. Mistakes may lead to significant
resource overprovisioning and bad performance. To alleviate such issues, this
paper develops an executable and configurable model named SSP (stands for Spark
Streaming Processing) to model and simulate Spark Streaming. SSP is written in
ABS, which is a formal, executable, and object-oriented language for modeling
distributed systems by means of concurrent object groups. SSP allows users to
rapidly evaluate and compare different parameter configurations without
deploying their applications on a cluster/cloud. The simulation results show
that SSP is able to mimic Spark Streaming in different scenarios.Comment: 7 pages and 13 figures. This paper is published in IEEE 32nd
International Conference on Advanced Information Networking and Applications
(AINA 2018
Reversible Computation: Extending Horizons of Computing
This open access State-of-the-Art Survey presents the main recent scientific outcomes in the area of reversible computation, focusing on those that have emerged during COST Action IC1405 "Reversible Computation - Extending Horizons of Computing", a European research network that operated from May 2015 to April 2019. Reversible computation is a new paradigm that extends the traditional forwards-only mode of computation with the ability to execute in reverse, so that computation can run backwards as easily and naturally as forwards. It aims to deliver novel computing devices and software, and to enhance existing systems by equipping them with reversibility. There are many potential applications of reversible computation, including languages and software tools for reliable and recovery-oriented distributed systems and revolutionary reversible logic gates and circuits, but they can only be realized and have lasting effect if conceptual and firm theoretical foundations are established first
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