60,046 research outputs found
Necessary and Sufficient Conditions on Partial Orders for Modeling Concurrent Computations
Partial orders are used extensively for modeling and analyzing concurrent
computations. In this paper, we define two properties of partially ordered
sets: width-extensibility and interleaving-consistency, and show that a partial
order can be a valid state based model: (1) of some synchronous concurrent
computation iff it is width-extensible, and (2) of some asynchronous concurrent
computation iff it is width-extensible and interleaving-consistent. We also
show a duality between the event based and state based models of concurrent
computations, and give algorithms to convert models between the two domains.
When applied to the problem of checkpointing, our theory leads to a better
understanding of some existing results and algorithms in the field. It also
leads to efficient detection algorithms for predicates whose evaluation
requires knowledge of states from all the processes in the system
Distributed measurement-based quantum computation
We develop a formal model for distributed measurement-based quantum
computations, adopting an agent-based view, such that computations are
described locally where possible. Because the network quantum state is in
general entangled, we need to model it as a global structure, reminiscent of
global memory in classical agent systems. Local quantum computations are
described as measurement patterns. Since measurement-based quantum computation
is inherently distributed, this allows us to extend naturally several concepts
of the measurement calculus, a formal model for such computations. Our goal is
to define an assembly language, i.e. we assume that computations are
well-defined and we do not concern ourselves with verification techniques. The
operational semantics for systems of agents is given by a probabilistic
transition system, and we define operational equivalence in a way that it
corresponds to the notion of bisimilarity. With this in place, we prove that
teleportation is bisimilar to a direct quantum channel, and this also within
the context of larger networks.Comment: 17 page
Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code
This paper introduces Tiramisu, a polyhedral framework designed to generate
high performance code for multiple platforms including multicores, GPUs, and
distributed machines. Tiramisu introduces a scheduling language with novel
extensions to explicitly manage the complexities that arise when targeting
these systems. The framework is designed for the areas of image processing,
stencils, linear algebra and deep learning. Tiramisu has two main features: it
relies on a flexible representation based on the polyhedral model and it has a
rich scheduling language allowing fine-grained control of optimizations.
Tiramisu uses a four-level intermediate representation that allows full
separation between the algorithms, loop transformations, data layouts, and
communication. This separation simplifies targeting multiple hardware
architectures with the same algorithm. We evaluate Tiramisu by writing a set of
image processing, deep learning, and linear algebra benchmarks and compare them
with state-of-the-art compilers and hand-tuned libraries. We show that Tiramisu
matches or outperforms existing compilers and libraries on different hardware
architectures, including multicore CPUs, GPUs, and distributed machines.Comment: arXiv admin note: substantial text overlap with arXiv:1803.0041
Distributing the Kalman Filter for Large-Scale Systems
This paper derives a \emph{distributed} Kalman filter to estimate a sparsely
connected, large-scale, dimensional, dynamical system monitored by a
network of sensors. Local Kalman filters are implemented on the
(dimensional, where ) sub-systems that are obtained after
spatially decomposing the large-scale system. The resulting sub-systems
overlap, which along with an assimilation procedure on the local Kalman
filters, preserve an th order Gauss-Markovian structure of the centralized
error processes. The information loss due to the th order Gauss-Markovian
approximation is controllable as it can be characterized by a divergence that
decreases as . The order of the approximation, , leads to a lower
bound on the dimension of the sub-systems, hence, providing a criterion for
sub-system selection. The assimilation procedure is carried out on the local
error covariances with a distributed iterate collapse inversion (DICI)
algorithm that we introduce. The DICI algorithm computes the (approximated)
centralized Riccati and Lyapunov equations iteratively with only local
communication and low-order computation. We fuse the observations that are
common among the local Kalman filters using bipartite fusion graphs and
consensus averaging algorithms. The proposed algorithm achieves full
distribution of the Kalman filter that is coherent with the centralized Kalman
filter with an th order Gaussian-Markovian structure on the centralized
error processes. Nowhere storage, communication, or computation of
dimensional vectors and matrices is needed; only dimensional
vectors and matrices are communicated or used in the computation at the
sensors
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